From 0b9f17f85d2e9cf50357adb0367fe0997e1a9862 Mon Sep 17 00:00:00 2001 From: Hugo Tavares Date: Wed, 10 Jan 2024 16:15:40 +0000 Subject: [PATCH] update to quarto template; fix #41 --- .github/.gitignore | 1 - .github/workflows/build_site.yaml | 43 - .github/workflows/publish_site.yml | 38 + .gitignore | 31 +- 02-unix-sed.md | 386 - 02a-unix_intro.md | 226 - 02b-unix_files_directories.md | 556 -- 02c-unix_text_manipulation.md | 575 -- 02d-unix_pipes.md | 276 - 04-artic_nextflow.md | 663 -- 05-lineage_assignment.md | 429 - 100-homework.md | 680 -- LICENSE.md | 29 + README.md | 28 +- .../courseformat/_extension.yml | 16 + .../courseformat/callout_exercise.lua | 45 + .../cambiotraining/courseformat/footer.html | 36 + .../img/university-of-cambridge-favicon.ico | Bin 0 -> 811 bytes .../img/university_crest_reversed.png | Bin 0 -> 214457 bytes .../courseformat/star_levels.lua | 17 + .../cambiotraining/courseformat/theme.scss | 139 + .../cambiotraining/fontawesome/_extension.yml | 6 + 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materials/original/index.md rename cov19-artic-pipeline.Rproj => sars-quarto.Rproj (72%) create mode 100644 sars-quarto.code-workspace rename 101-data.md => setup.md (84%) delete mode 100644 setup_environment.sh diff --git a/.github/.gitignore b/.github/.gitignore deleted file mode 100644 index 2d19fc7..0000000 --- a/.github/.gitignore +++ /dev/null @@ -1 +0,0 @@ -*.html diff --git a/.github/workflows/build_site.yaml b/.github/workflows/build_site.yaml deleted file mode 100644 index f599330..0000000 --- a/.github/workflows/build_site.yaml +++ /dev/null @@ -1,43 +0,0 @@ -on: - push: - branches: - - main - - master - -name: build-site - -jobs: - build-site: - runs-on: ubuntu-22.04 - env: - RSPM: https://packagemanager.rstudio.com/cran/__linux__/jammy/latest - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - - steps: - - name: Check out repository - uses: actions/checkout@v2 - - - name: Setup R - uses: r-lib/actions/setup-r@v2 - id: install-r - - - name: Setup pandoc - uses: r-lib/actions/setup-pandoc@v2 - - - name: Install rmarkdown - run: Rscript -e 'install.packages("rmarkdown")' - - - name: Install tidyverse - run: Rscript -e 'install.packages(c("dplyr", "readr", "ggplot2"))' - - - name: Build website - run: | - Rscript -e 'rmarkdown::render_site()' - touch _site/.nojekyll - - - name: Deploy - uses: JamesIves/github-pages-deploy-action@4.1.0 - with: - branch: gh-pages - folder: _site - single-commit: true diff --git a/.github/workflows/publish_site.yml b/.github/workflows/publish_site.yml new file mode 100644 index 0000000..78e211b --- /dev/null +++ b/.github/workflows/publish_site.yml @@ -0,0 +1,38 @@ +on: + push: + branches: + - main + - master + +name: Render & Publish Site + +jobs: + build-deploy: + runs-on: ubuntu-latest + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - name: Check out repository + uses: actions/checkout@v2 + + - name: Set up Quarto + uses: quarto-dev/quarto-actions/setup@v2 + with: + version: 1.3.450 + + - name: Render Site + run: | + quarto render + + - name: No Jekyll + run: | + touch _site/.nojekyll + + - name: Publish + uses: JamesIves/github-pages-deploy-action@4.1.0 + with: + branch: gh-pages + folder: _site + single-commit: true \ No newline at end of file diff --git a/.gitignore b/.gitignore index 7014e2f..361b7ac 100644 --- a/.gitignore +++ b/.gitignore @@ -1,16 +1,23 @@ -# directories we often use -slides -notes -course_materials -prepare_data - -# local site rendering is not pushed +# Quarto files +/.quarto/ _site -# in case RStudio is used for editing/building site +# python notebooks +*.ipynb + +# R files .Rhistory -.Rproj.user -.Rbuildignore +.Rapp.history +.RData +.RDataTmp +.Ruserdata +.Rproj.user/ +*_cache/ +/cache/ +*.utf8.md +*.knit.md +.Renviron -# VS Code workspace file -*.code-workspace +# Jupyter cache +.jupyter_cache +/.luarc.json diff --git a/02-unix-sed.md b/02-unix-sed.md deleted file mode 100644 index 3349611..0000000 --- a/02-unix-sed.md +++ /dev/null @@ -1,386 +0,0 @@ ---- -pagetitle: "The Unix Shell: sed - stream editor" ---- - -# The Unix Shell: `sed` - stream editor - -:::highlight - -**Questions** - -- What is `sed`? How to use `sed`? -- What is the `sed` script? -- What is the `s` command? How to use the `s` command? -- What is an escape character? -- How to use wildcards in the `sed`? - -**Learning Objectives** - -- Explain the basic usage of the `sed`. -- A closer look to `sed` script. -- Demonstrate the usage of the `sed` in processing text. - -::: - -## Introduction -The `sed` command is short for stream editor. A stream editor is used to perform basic text transformations on an input stream (a file or input from a pipeline). - -`sed` is one of the most prominent text-processing utilities on GNU/Linux. `sed` performs operations on text coming from standard input or a file. - -:::note -In this tutorial, we'll use the GNU version of `sed` (available on Ubuntu and other Linux operating systems). The macOS has the BSD version of `sed` which has different options and arguments. You can install the GNU version of `sed` with [Homebrew](https://brew.sh/) using `brew install gnu-sed`. -::: - -## Basic Usage -There are many instances when we want to substitute a text in a line or filter out specific lines. In such cases, we can take advantage of `sed`. -`sed` operates on a stream of text which it gets either from a text file or from standard input (STDIN). It means you can use the output of another command as the input of `sed` -- in short, you can combine `sed` with other commands. - -By default, `sed` outputs everything to standard output (STDOUT). It means, unless redirected, `sed` will print its output onto the terminal/screen instead of saving it in a file. - -:::note -`sed` edits line-by-line and in a non-interactive way. -::: - -The basic usage of `sed`: - -```bash -$ sed [OPTIONS] SCRIPT INPUT-FILES -``` - -To begin this tutorial, create a file 'input.txt' and write the following line and save. -```bash -hello world -``` - -Now we'll use the `sed` to replace 'hello' with 'world': - -```bash -$ sed 's/hello/world/' input.txt -``` - -```bash -# Output -world world -``` -Just follow this tutorial we will explain all the magic which just happened. - -## `sed` script commands - -Let's take a closer look at the `sed` script command. In the above script (`s/hello/world`), 's' is called the `sed` command. There are many `sed` commands, but we'll mainly focus on the 's' command. To see a list of all `sed`'s commands, visit [https://www.gnu.org/software/sed/manual/html_node/sed-commands-list.html](https://www.gnu.org/software/sed/manual/html_node/sed-commands-list.html). - -The basic syntax for a `sed` command is: -```bash -[ADDRESS]X[OPTION] -``` - -Here, 'X' is the single letter `sed` command. [ADDRESS] is optional. If [ADDRESS] is specified, the command X will be executed only on the matched lines. Now, [ADDRESS] can be a single line number, regular expression, or a range of lines. For some `sed` commands we use additional [OPTION]. - -## The 's' command - -The 's' command, also known as substitute command, is probably the most important in `sed` and has a lot of different options. The basic syntax for 's' command is: - -```bash -s/regexp/replacement/[flags] -``` - -As you might have guessed, 's' is for substitution. It will tell `sed` that we are going make some substitution. 's' is followed by '/' you can think of it as a delimiter. - -After that, we have *regexp*, which is short for the regular expression. We use the regular expression to match the pattern against the content of a line. - -:::note -**What is a regular expression?** - -A regular expression is a pattern formed in a standardised way that helps search in a string. Regular expressions are useful for matching common patterns of string such as email addresses, phone numbers, URLs, etc. To learn more visit: [https://www.keycdn.com/support/regex-cheatsheet](https://www.keycdn.com/support/regex-cheatsheet) -::: - -The third one is the replacement. So, if `sed` finds the match, it will replace the matched text/pattern with the replacement text. - -So, the basic concept of the 's' command is: it attempts to match the pattern in a line, if the match is successful, then that portion of the pattern is replaced with *replacement*. - -At the last of the 's' command, you can see [flags]. [flags] are optional. An 's' command can have zero or more flags. Some of the common flags are: - -- **g**: By default `sed` command will only substitute the first match. If the line has more than one match, all will not get replaced. It replaces only the first match. But, if we use the **g** flag, then the `sed` command will replace all matching text. - -- **i**: `sed` matches the regular expression in a case-sensitive manner. It means 'A' (Uppercase A) and 'a' (Lowercase A) are different. But, if we use the **i** flag, then the `sed` will match the regular expression in a case-insensitive manner. This means now the `sed` will treat 'A' and 'a' as same. - -To learn more about the 's' command visit [https://www.gnu.org/software/sed/manual/html_node/The-_0022s_0022-Command.html](https://www.gnu.org/software/sed/manual/html_node/The-_0022s_0022-Command.html). - - -## Using the 's' command - -Let's go back to our first example. -```bash -$ sed 's/hello/world' input.txt -``` -Now, I guess it is somewhat clear to you what is happening here. The `sed` is substituting 'hello' with 'world' at every line. And we are getting the input from a file called 'input.txt'. - -Create a new file 'input2.txt' and write the following text in it: - -```bash -# Output - -Hello, this is a test line. This is a very short line. -This is test line two. In this line, we have two occurrences of the test. -This line has many occurrences of the Test with different cases. test tEst TesT. -``` - -Now try to replace 'test' with 'hello'. We can do something like this: - -```bash -$ sed 's/test/hello/' input2.txt -``` - -```bash -# Output - -Hello, this is a hello line. This is a very short line. -This is hello line two. In this line, we have two occurrences of the test. -This line has many occurrences of the Test with different case. hello tEst TesT. -``` - -You may have noticed that lines two and three still have 'test'. This is because we ask the `sed` to only replace the first text which matches. To replace all the matches, we have to use **g** flag. Let's try with **g** flag. - - -```bash -$ sed 's/test/hello/g' input2.txt -``` - -```bash -# Output - -Hello, this is a hello line. This is a very short line. -This is hello line two. In this line, we have two occurrences of the hello. -This line has many occurrences of the Test with different case. hello tEst TesT. -``` - -Ah, something is still wrong with the third line. It is not replacing 'Test', 'tEst' and 'TesT'. We have to do something to tell the `sed` that we want to replace all of them. We can do this by using **i** flag. Let's add one more flag: - -```bash -$ sed 's/test/hello/gi' input2.txt -``` - -```bash -# Output - -Hello, this is a hello line. This is a very short line. -This is hello line two. In this line, we have two occurrences of the hello. -This line has many occurrences of the hello with different case. hello hello hello. -``` - -Wonderful! - -Let's say now we only want to replace all the occurrences of the 'test' at only line 3 or from line 2 to 3. Try to remember the basic syntax for the `sed` command. Remember! You can add the address of the line or a range of lines that you want to edit. Here is an example: - -```bash -$ sed '3s/test/hello/gi' input2.txt -``` - -```bash -# Output - -Hello, this is a test line. This is a very short line. -This is test line two. In this line, we have two occurrences of the test. -This line has many occurrences of the hello with different case. hello hello hello. -``` - -See only the third line is executed by the `sed`. The first two lines are as it is. At the beginning of the 's' command, we are adding the line number which we want to edit. We can also add a range of lines. Here is one more example: - - -```bash -$ sed '2,3s/test/hello/gi' input2.txt -``` - -As you may have got the idea, it will edit lines 2 and 3. The output will be: - -```bash -# Output - -Hello, this is a test line. This is a very short line. -This is hello line two. In this line, we have two occurrences of the hello. -This line has many occurrences of the hello with different case. hello hello hello. -``` - - -## Combining `sed` commands - -You can combine multiple `sed` commands. Let's say in the 'input2.txt' we first want to remove all the occurrences of 'test', then we want to replace 'line' with 'world'. Here it is clear that we have to do two substitutions. One for removing 'test' and one for replacing 'line'. We can do something like this: - -```bash -$ sed 's/test//gi' input2.txt | sed 's/line/world/gi' -``` - -Here first we are removing 'test' with empty text, and then we are passing the output of first `sed` as the input of second `sed`. The second `sed` is replacing 'line' with 'world'. You will get an output like this: - -```bash -# Output - -Hello, this is a world. This is a very short world. -This is world two. In this world, we have two occurrences of the . -This world has many occurrences of the with different case. . -``` - -## Escape character - -An escape character is a character that invokes an alternative interpretation of the following character. Sometimes it is also used to insert unallowed characters in a string. An escape character is a backslash `\` followed by a character (or characters). Some of the keywords/characters which you want to escape are as follows: - -- `*`: Asterisk. -- `.`: Dot. -- `[`: Left square bracket. -- `]`: Right square bracket. -- `?`: Question mark. -- `$`: Dollar sign. -- `^`: Caret -- `/`: Forward slash -- `\`: Backward slash - -Let's try to understand this with examples. For this tutorial, create a file 'input3.txt' and put the following text in it: -``` -This software/application is a part of this workshop. -``` - -Now, what if we want to replace 'software/application' with 'material'. If we try to do something like: `sed 's/software/application/material/' input3.txt`, the `sed` will throw an error. Because here we are using '/' as delimiter, sed will think it has to replace 'software' with 'application' but, why there is a third option. To solve this error, we have to escape one '/', we can do this by adding a `\` in front of '/'. It will tell the `sed` not to interpret '/' as a special character. - -```bash -$ sed 's/software\/application/material/' input3.txt -``` - -```bash -# Output - -This material is a part of this workshop. -``` - -Escape characters are also used to provide visual representations of non-printing characters and characters that usually have special meanings. The list of commonly used escape characters in the `sed` is as follows: - -- `\n`: a newline. -- `\r`: a carriage return. -- `\t`: a horizontal tab. - -For more details visit [https://www.gnu.org/software/sed/manual/sed.html#Escapes](https://www.gnu.org/software/sed/manual/sed.html#Escapes). - - -## Wildcards in `sed` - -We have already covered wildcards on the "[Basic Commands](./02-unix-basic-commands.html#Using_wildcards_for_accessing_multiple_files_at_once)" page; here, we will discuss the use of `.`, `*` and `?` in the `sed`. These wildcards are part of the regular expression. - -- You can use `.` as a placeholder for any character except newline (`\n`) or empty text. For example, if you use `.` in your regular expression like `x.z` then it will match to strings like `xaz`, `xbz`, `x1z`, `xzz`, etc., but, it will not match to `xz`. - -- You can use `*` to match 0 or more occurrences of the previous character. For example, `xy*z` will match to strings like `xz` (0 occurrences of y), `xyz` (1 occurrence of y), `xyyz` and so on. - -- `?` is a bit similar to `*`. The difference is it will only match for 0 or 1 occurrence of the previous character. For example, `xy?z` will match to strings like `xz` (0 occurrences of y), `xyz` (1 occurrence of y) but not to `xyyz`. - -Now, let's do some coding. Create a file 'input4.txt' and copy the following sequence: -```bash -ATGCCTGATTGGGCTACGTCGTAAGCGATGGCTAGGTATCGTAAAGGGGTTTGGGAACCCCAATCACTAGCT -``` - -Let's say we want to replace anything between `A` and `G` with `U`. We can do this using the `sed`: - -```bash -$ sed 's/A.G/AUG/g' input4.txt -``` -```bash -#Output - -AUGCCTGATTGGGCTAUGTCGTAUGCGAUGGCTAUGTATCGTAAUGGGGTTTGGGAACCCCAATCACTAGCT -``` - - -:::exercise - -**Replace Me** - -Click on the following link to download the fasta file containing the protein sequence for one of the SARS-CoV-2, `envelope_gene.fa`. You have four tasks to do on this file. They are: - -- Replace the word `patient` with `sample`. Make sure to replace all words matching to `patient` in a case insensitive manner. -- Replace all the `.` with `X`. -- Remove `/incomplete` from one of the sample names. -- Save the out to `envelope_gene_processed.fa`. - -
-Answer - -We will walk through the solution step by step. At first, to replace the word `patient` with `sample`, we can do something like this: - -```bash -$ sed 's/patient/sample/i' envelope_gene.fa -``` -```bash -# Output - ->sample01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->sample02/incomplete -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFY................... ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -We have to use the flag `i` to tell the `sed` that we want to match the word `patient` in case insensitive manner. - -Second we have to replace all the `.` with `X`. Remember the `.` is a keyword (or has special meaning in the `sed`). So, to have the literal meaning of `.`, we have to escape the `.` with `\`. We can replace all the `.` as follows: - -```bash -$ sed 's/\./X/g' envelope_gene.fa -``` -```bash -# Output - ->patient01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Patient02/incomplete -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFYXXXXXXXXXXXXXXXXXXX ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -In line 4, you can see that all the `.` is replaced by `X`. Note that to replace all the `.` we also have to use `g` flag. - -Third, we have remove `\incomplete`. Again `/` is a keyword in the `sed`. We have to do something similar to the previous step. - -```bash -$ sed 's/\/incomplete//' envelope_gene.fa -``` -```bash -# Output - ->patient01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Patient02 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFY................... ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -Now, we have to combine all three steps (this we can do by using pipe`|`) and then redirect the output to a file rather than the default output. - -```bash -$ sed 's/patient/sample/i' < envelope_gene.fa | sed 's/\./X/g' | sed 's/\/incomplete//' > envelope_gene_processed.fa -``` - -```bash -# You will see no output in the terminal. But you will see a new file -# `envelope_gene_processed.fa`, which will look similar to the following - ->sample01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->sample02 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFYXXXXXXXXXXXXXXXXXXX ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -
-::: - -## Resources - -To learn more about sed visit [https://www.gnu.org/software/sed/manual/sed.html](https://www.gnu.org/software/sed/manual/sed.html) diff --git a/02a-unix_intro.md b/02a-unix_intro.md deleted file mode 100644 index c119544..0000000 --- a/02a-unix_intro.md +++ /dev/null @@ -1,226 +0,0 @@ ---- -pagetitle: "The Unix Shell" ---- - -:::box -These materials are based on the [Carpentries Shell Lesson](https://swcarpentry.github.io/shell-novice/) with credit to their authors and contributors. - -CC BY 4.0 - -Gabriel A. Devenyi (Ed.), Gerard Capes (Ed.), Colin Morris (Ed.), Will Pitchers (Ed.), -Greg Wilson, Gerard Capes, Gabriel A. Devenyi, Christina Koch, Raniere Silva, Ashwin Srinath, … Vikram Chhatre. -(2019, July). swcarpentry/shell-novice: Software Carpentry: the UNIX shell, June 2019 (Version v2019.06.1). -Zenodo. http://doi.org/10.5281/zenodo.3266823 -::: - - -# The Unix Shell: Introduction - -:::highlight - -**Questions** - -- Why is the Unix command line useful for bioinformatics? -- What is the general structure and usage of a command? - -**Learning Objectives** - -- List some advantages of using the Unix command line, compared to graphical user interfaces. -- Run and modify the behaviour of a simple command. -::: - -:::note -This section has an accompanying slide deck. -::: - -## Overview - -In these lessons we will give a brief introduction to the Unix Command Line. -But what is the Command Line? - -Humans and computers commonly interact in many different ways, such as through a keyboard and mouse, -touch screen interfaces, or using speech recognition systems. -The most widely used way to interact with personal computers is called a -**graphical user interface** (GUI). -With a GUI, we give instructions by clicking a mouse and using menu-driven interactions. - -While the visual aid of a GUI makes it intuitive to learn, this way of delivering instructions to a computer scales very poorly. -Imagine the following task: for a literature search, you have to copy the third line of one thousand text files in one thousand different directories and paste it into a single file. - -Using a GUI, you would not only be clicking at your desk for several hours, but you could potentially also commit an error in the process of completing this repetitive task. -This is where we take advantage of the Unix shell. -The Unix shell is both a **command-line interface** (CLI) and a scripting language, allowing such repetitive tasks to be done automatically and fast. -With the proper commands, the shell can repeat tasks with or without some modification as many times as we want. -Using the shell, the task in the literature example can be accomplished in seconds. - -![An example of listing some files from a command line interface (the Unix terminal, on the left) and the same files shown on a graphical user interface (in this example a file browser, on the right).](images/gui_vs_cli.png) - -## The Shell - -The shell is a program where users can type commands. -With the shell, it's possible to invoke complicated programs like bioinformatics software or simple commands that create an empty directory with only one line of code. -The most popular Unix shell is Bash. -Bash is the default shell on most modern implementations of Unix and in most packages that provide -Unix-like tools for Windows. - -Using the shell will take some effort and some time to learn. -While a GUI presents you with choices to select, CLI choices are not automatically presented to you, so you must learn a few commands like new vocabulary in a language you're studying. -However, unlike a spoken language, a small number of "words" (i.e. commands) gets you a long way, and we'll cover those essential few today. - -The grammar of a shell allows you to combine existing tools into powerful pipelines and handle large volumes of data automatically. -Sequences of commands can be written into a *script*, improving the reproducibility of workflows. - -In addition, the command line is often the easiest way to interact with remote machines and supercomputers. -Familiarity with the shell is near essential to run a variety of specialized tools and resources including high-performance computing systems. -As clusters and cloud computing systems become more popular for scientific data crunching, being able to interact with the shell is becoming a necessary skill. -We can build on the command-line skills covered here to tackle a wide range of scientific questions and computational challenges. - - -## Running Commands - -When the shell is first opened, you are presented with a **prompt**, -indicating that the shell is waiting for input. - -```console -$ -``` - -The shell typically uses `$ ` as the prompt, but may use a different symbol. -In the examples for this lesson, we'll show the prompt as `$ `. -Most importantly: when typing commands, either from these lessons or from other sources, *do not type the prompt*, only the commands that follow it. -Also note that after you type a command, you have to press the Enter key to execute it. - -The prompt is followed by a **text cursor**, a character that indicates the position where your typing will appear. -The cursor is usually a flashing or solid block, but it can also be an underscore or a pipe. -You may have seen it in a text editor program, for example. - -So let's try our first command, `ls` which is short for "listing". -This command will list the contents of the current directory: - -```console -$ ls -``` -``` -01-unix 02-consensus 03-lineages -``` - -:::note -**Command not found** - -If the shell can't find a program whose name is the command you typed, it will print an error message such as: - -```console -$ ks -``` -``` -ks: command not found -``` - -This might happen if the command was mis-typed or if the program corresponding to that command is not installed. -::: - - -## Command Options - -Commands can often change their behaviour with additional options. -Consider the command below as a general example of a command, which we will dissect into its component parts: - -```console -$ ls -F /home/ubuntu/ -``` - -- `ls` is the **command**. -- `-F` is an **option**, also called **switch** or **flag**. Options either start with a single dash (`-`) or two dashes (`--`). -- `/home/ubuntu/` is an **argument**. Arguments tell the command what to operate on (e.g. files and directories). - -A command can be called with more than one option and more than one argument: but a command doesn't always require an argument or an option. - -Each part is separated by spaces: if you omit the space between `ls` and `-F` the shell will look for a command called `ls-F`, which doesn't exist. -Also, capitalisation can be important: `ls -r` is different to `ls -R`. - -So, our command above gives us a listing of files and directories in the directory `/home/ubuntu`. -An example of the output of the command is: - -```console -$ ls -F /home/ubuntu/ -``` - -``` -Applications/ Documents/ Library/ Music/ Public/ -Desktop/ Downloads/ Movies/ Pictures/ -``` - -### Getting help - -`ls` has lots of other **options**. There are two common ways to find out how to use a command and what options it accepts: - -1. We can pass a `--help` option to the command, such as: - ```console - $ ls --help - ``` - -2. We can read its manual with `man`, such as: - ```console - $ man ls - ``` - - -:::note -**Unsupported command-line options** - -If you try to use an option (flag) that is not supported, `ls` and other commands will usually print an error message similar to: - -```console -$ ls -j -``` - -``` -ls: invalid option -- 'j' -Try 'ls --help' for more information. - ``` -::: - -:::note -**Manual Page** - -The `man` page is not available for every software. -For example, specialist software (such as bioinformatics packages) only have the documentation available through the `--help` option. -We will see several examples of this during the course. -::: - -:::exercise -**Exploring More `ls` Flags** - -- What does the command `ls` do when used with the `-l` option? -- What about if you use both the `-l` and the `-h` option? (You can use more than one option at once). - -Some of its output is about properties that we do not cover in this lesson (such as file permissions and ownership), but the rest should be useful nevertheless. - -
-Answer - -```console -$ ls -l -h -``` - -The `-l` option makes `ls` use a **l**ong listing format, showing not only the file/directory names but also additional information such as the file size and the time of its last modification. -If you use both the `-h` option and the `-l` option, this makes the file size "**h**uman readable", i.e. displaying something like `5.3K` instead of `5369`. - -
-::: - - - - -## Summary - -:::highlight - -**Key Points** - -- The Unix Command Line can allow running complex operations with a few commands. -- Many specialist software in bioinformatics is only written for Linux and runs from the command line. -- The basic syntax of a command is: `command -options argument`. For example, `ls -l /home/ubuntu/` would **l**ist the contents of the `/home/ubuntu/` directory in a **l**ong format. - -::: - diff --git a/02b-unix_files_directories.md b/02b-unix_files_directories.md deleted file mode 100644 index 7ba90e6..0000000 --- a/02b-unix_files_directories.md +++ /dev/null @@ -1,556 +0,0 @@ ---- -pagetitle: "The Unix Shell" ---- - -# The Unix Shell: Navigating the File System - -:::highlight - -**Questions** - -- How is a filesystem typically organised? -- How can I navigate a filesystem and work with files from the command line? - -**Learning Objectives** - -- Recognise the hierarchical structure of a filesystem. -- Navigate the filesystem and create or remove files and directories (`pwd`, `ls`, `cd`, `mkdir`, `rmdir`, `rm`, `cp`, `mv`). - -::: - -:::note -This section has an accompanying slide deck. -::: - -## Working Directory - -The part of the operating system responsible for managing files and directories is called the **file system**. -It organizes our data into files, which hold information, and directories (also called "folders"), which hold files or other directories. - -Several commands are frequently used to create, inspect, rename, and delete files and directories. -To start exploring them, we'll go to our open shell window. - -First let's find out where we are by running a command called `pwd` (which stands for "print working directory"). -Directories are like *places* - at any time while we are using the shell we are in exactly one place, called our **current working directory**. -Commands mostly read and write files in the current working directory, so knowing where you are before running a command is important. -`pwd` shows you where you are: - -```console -$ pwd -``` - -``` -/home/ubuntu/Course_Materials -``` - -Here, the computer's response is `/home/ubuntu/Course_Materials`, which is the default starting directory on our training machines. -Normally, the default directory would be `/home/ubuntu`, which is called the **home directory**. -The name "ubuntu" is our _username_. -If you were using your own computer, it would look different, for example `/home/larry`. - -:::note -**Home Directory Variation** - -The home directory path will look different on different operating systems. -For a user named "larry", on a Mac it would look like `/Users/larry`, and on Windows `C:\Users\larry`. -::: - -:::note -**Slashes** - -Notice that there are two meanings for the `/` character. -When it appears at the beginning of a file or directory name, it refers to the root directory. -When it appears *inside* a name, it's a separator. -::: - -Underneath `/home`, we find one directory for each user with an account on the machine, in the example shown in the figure below we have *imhotep*, *larry*, and "ubuntu" (you). - -![Example of users' home directories](images/home-directories.svg) - -The user *imhotep*'s files are stored in `/home/imhotep`, user *larry*'s in `/home/larry`, and yours in `/home/ubuntu`. -Because you are the current user in our examples here, this is why we get `/home/ubuntu` as our home directory. -Typically, when you open a new command prompt you will be in your home directory to start. - - -## Listing Files - -Now let's learn the command that will let us see the contents of our own filesystem. -We can see what's in our current directory by running `ls`, which stands for "listing": - -```console -$ ls -``` - -``` -01-unix 02-consensus 03-lineages -``` - -The `/home/ubuntu/Course_Materials/01-unix` directory contains the data that we will use in this tutorial. -We can look at its contents passing a directory name as an argument to `ls`: - -```console -$ ls /home/ubuntu/Course_Materials/01-unix -``` - -``` -artic_primers_pool1.bed artic_primers_pool2.bed -data metadata output.tsv -``` - - -## Changing Directory - -So far, we have been working from the `/home/ubuntu/Course_Materials/`. -However, we can change our location to the `01-unix` directory to do our work. - -The command to change locations is `cd` ("change directory") followed by a directory name to change our working directory. - -```console -$ cd /home/ubuntu/Course_Materials/01-unix/ -``` - -We can check with `pwd` that we are in the correct directory. -We can also run `ls` again to see the files within our current directory. - -What if we now wanted to go to the `data` directory? -We could do: - -```console -$ cd /home/ubuntu/Course_Materials/01-unix/data/ -``` - -However, that's a lot of typing! -Instead, we can move to that directory by specifying its location _relative_ to our current directory. -So, if our current directory was `/home/ubuntu/Course_Materials/01-unix/` we could just do: - -```console -$ cd data -``` - -In conclusion, there are two ways to specify directory names: - -- An **absolute path** includes the entire path (or location) from the root directory, which is indicated by a leading slash. The leading `/` tells the computer to follow the path from the root of the file system, so it always refers to exactly one directory, no matter where we are when we run the command. -- A **relative path** tries to find that location from where we are (our current directory), rather than from the root of the file system. - -We now know how to go _down_ the directory tree, but how do we go _up_? -We might try the following: - -```console -$ cd 01-unix -``` - -``` --bash: cd: 01-unix: No such file or directory -``` - -But we get an error! Why is this? -With our methods so far, `cd` can only see sub-directories _inside_ your current directory. -To move up one directory we need to use the special shortcut `..` like this: - -```console -$ cd ../ -``` - -`..` is a special directory name meaning "the directory containing this one", or more succinctly, the **parent** of the current directory. -Sure enough, if we run `pwd` after running `cd ..`, we're back in `/home/ubuntu/Course_Materials/01-unix`. - - -:::note -**Another Shortcut** - -The shell interprets the character `~` (tilde) at the start of a path to mean "the current user's home directory". -For example, for your home directory,`/home/ubuntu`, then `~/Course_Materials` is equivalent to `/home/ubuntu/Course_Materials`. -This only works if it is the first character in the path: `here/there/~/elsewhere` is *not* `here/there/home/ubuntu/elsewhere`. -::: - - -:::note -**Tab completion** - -Sometimes file and directory names get too long and it's tedious to have to -type the full name for example when moving with `cd`. -We can let the shell do most of the work > through what is called **tab completion**. -Let's say we are in the `/home/ubuntu/Course_Materials/01-unix/` and we type: - -```console -$ ls met -``` - -and then press the Tab ↹ key on the keyboard, the shell automatically completes the directory name: - -```console -$ ls metadata/ -``` - -If we press Tab ↹ again it does nothing, since there are 3 possibilities. -In this case, pressing Tab twice brings up a list of all the files. -::: - -:::exercise - -Using the filesystem diagram below, if `pwd` displays `/Users/thing`, what will `ls ../backup` display? - -1. `../backup: No such file or directory` -2. `2012-12-01 2013-01-08 2013-01-27` -3. `original pnas_final pnas_sub` - -![](images/filesystem-challenge.svg) - -
-Answer - 1. No: from the diagram, we can see that there *is* a directory `backup` in `/Users`. - 2. No: this is the content of `Users/thing/backup`, but with `..` we asked for one level further up. - 3. **Yes:** `../backup/` refers to `/Users/backup/`. -
-::: - - -## Creating directories - -We now know how to explore files and directories, but how do we create them in the first place? - -**First, we should see where we are and what we already have.** -Let's go back to our `01-unix` directory and use `ls` to see what it contains: - -```console -$ cd /home/ubuntu/Course_Materials/01-unix -$ ls -``` - -``` -artic_primers_pool1.bed artic_primers_pool2.bed data/ metadata/ output.tsv -``` - -Now, let's **create a new directory** called `results` using the command `mkdir results`: - -```console -$ mkdir results -``` - -As you might guess from its name, `mkdir` means "make directory". -Since `results` is a relative path (i.e., does not have a leading slash, like `/what/ever/results`), the new directory is created in the current working directory: - -```console -$ ls -``` - -```console -artic_primers_pool1.bed data/ output.tsv -artic_primers_pool2.bed metadata/ results/ -``` - -:::note -**Two ways of doing the same thing** - -Using the shell to create a directory is no different than using a file explorer. -If you open the current directory using your operating system's graphical file explorer , the `results` directory will appear there too. -While the shell and the file explorer are two different ways of interacting with the files, the files and directories themselves are the same. -::: - -:::note -**Good names for files and directories** - -Complicated names of files and directories can make your life painful when working on the command line. - -
Click Here to see some useful tips for naming your files. - -1. Don't use spaces. - - Spaces can make a name more meaningful, but since spaces are used to separate arguments on the command line it is better to avoid them in names of files and directories. - You can use `-` or `_` instead (e.g. `consensus_sequences/` rather than `consensus sequences/`). - -2. Don't begin the name with `-` (dash). - - Commands treat names starting with `-` as options. - -3. Stick with letters, numbers, `.` (period or 'full stop'), `-` (dash) and `_` (underscore). - - Many other characters have special meanings on the command line. - We will learn about some of these during this lesson. - There are special characters that can cause your command to not work as expected and can even result in data loss. - -If you need to refer to names of files or directories that have spaces or other special characters, you should surround the name in quotes (`""`). -
-::: - -Since we've just created the `results` directory, there's nothing in it yet: - -```console -$ ls results -``` - -:::note -**What's In A Name?** - -You may have noticed that all of the files in our data directory are named "something dot something". -For example `output.tsv`, which indicates this is a "tab-delimited values" file. - -The second part of such a name is called the **filename extension**, and indicates what type of data the file holds. -Here are some common examples: - -- `.txt` signals a plain text file. -- `.csv` is a text file with tabular data where each column is separated by a comma. -- `.tsv` is like a CSV but values are separated by a tab. -- `.log` is a text file containing messages produced by a software while it runs. -- `.pdf` indicates a PDF document. -- `.png` is a PNG image. - -This is just a convention: we can call a file `mydocument` or almost anything else we want. -However, most people use two-part names most of the time to help them (and their programs) tell different kinds of files apart. - -This is just a convention, albeit an important one. -Files contain bytes: it's up to us and our programs to interpret those bytes according to the rules for plain text files, PDF documents, configuration -files, images, and so on. - -Naming a PNG image of a whale as `whale.mp3` doesn't somehow magically turn it into a recording of whalesong, though it *might* cause the operating system to try to open it with a music player when someone double-clicks it. -::: - - -## Moving & Renaming Files - -In our `01-unix` directory we have a file `output.tsv`, which contains the results of an analysis of SARS-CoV-2 variants for different samples. -Let's move this file to the `results` directory we created earlier, using the command `mv`, which is short for "move": - -```console -$ mv output.tsv results/ -``` - -The first argument tells `mv` what we're "moving", while the second is where it's to go. -In this case, we're moving `output.tsv` to `results/`. -We can check the file has moved there: - -```console -$ ls results -``` - -``` -output.tsv -``` - -This isn't a particularly informative name for our file, so let's change it! -Interestingly, we also use the `mv` command to change a file's name. -Here's how we would do it: - -```console -$ mv results/output.tsv results/variants.tsv -``` - -In this case, we are "moving" the file to the same place but with a different name. -One has to be careful when specifying the target file name, since `mv` will silently overwrite any existing file with the same name, which could lead to data loss. - -The command `mv` also works with directories, and you can use it to move/rename an entire directory just as you use it to move an individual file. - - -## Copying Files and Directories - -The `cp` command works very much like `mv`, except it copies a file instead of moving it. -For example: - -```console -$ cp data/envelope_protein.fa protein_copy.fa -$ ls -``` - -``` -artic_primers_pool1.bed data protein_copy.fa -artic_primers_pool2.bed metadata results -``` - -:::exercise - -Make a copy of the `results` directory named `backup`. -When copying an entire directory, you will need to use the option `-r` with the `cp` command (`-r` means "recursive"). - -
Answer - -If we run the command without the `-r` option, this is what happens: - -```console -$ cp results backup -``` - -``` -cp: -r not specified; omitting directory 'results' -``` - -This message is already indicating what the problem is. -By default, directories (and their contents) are not copied unless we specify the option `-r`. - -This would work: - -```console -$ cp -r results backup -``` - -Running `ls` we can see a new folder called `backup`: - -```console -$ ls -``` - -``` -artic_primers_pool1.bed artic_primers_pool2.bed -backup data metadata results -``` - -
-::: - - -## Removing Files and Directories - -Let's tidy up our `01-unix` directory by removing the `protein_copy.fa` file we created earlier. -The Unix command we'll use for this is `rm` (short for 'remove'): - -```console -$ rm protein_copy.fa -``` - -We can confirm the file has gone using `ls`. - -:::note -**Deleting Is Forever** - -The Unix shell doesn't have a trash bin that we can recover deleted files from (though most graphical interfaces to Unix do). -Instead, when we delete files, they are unlinked from the file system so that their storage space on disk can be recycled. -Tools for finding and recovering deleted files do exist, but there's no guarantee they'll work in any particular situation, since the computer may recycle the file's disk space right away. -::: - -If we try to remove the `backup` directory we created in the exercise: - -```console -$ rm backup -``` - -``` -rm: cannot remove `backup': Is a directory -``` - -This happens because `rm` by default only works on files, not directories. - -`rm` can remove a directory *and all its contents* if we use the -recursive option `-r`, and it will do so *without any confirmation prompts*: - -```console -$ rm -r backup -``` - -Given that there is no way to retrieve files deleted using the shell, `rm -r` *should be used with great caution* (you might consider adding the interactive option `rm -r -i`). - -To remove _empty_ directories, we can also use the `rmdir` command. -This is a safer option than `rm -r`, because it will never delete the directory if it contains files, giving us a chance to check whether we really want to delete all its contents. - - -## Operations with multiple files and directories - -Oftentimes one needs to copy several files at once. -This can be done by providing a list of individual filenames, or specifying a naming pattern using wildcards. - -:::exercise -**Copy with Multiple Filenames** - -Working from the `01-unix` directory, run the following code. -What does `cp` do when given several filenames and a directory name? - -```console -$ mkdir backup -$ cp metadata/run1_samples.csv metadata/run2_samples.csv backup/ -``` - -In the example below, what does `cp` do when given three or more file names? - -```console -$ cp metadata/run1_samples.csv metadata/run2_samples.csv metadata/run3_samples.csv -``` - - -
-Answer - -If given more than one file name followed by a directory name (i.e. the destination directory must -be the last argument), `cp` copies the files to the named directory. - -If given three file names, `cp` throws an error such as the one below, because it is expecting a directory name as the last argument. - -```console -cp: target 'run3_samples.csv' is not a directory -``` - -
-::: - - -### Wildcards - -Wildcards are special characters that can be used to access multiple files at once. -The most commonly-used wildcard is `*`, which is used to match zero or more characters. - -Let's consider the `data` directory: - -```console -$ ls data/ -``` - -``` -envelope_protein.fa sequencing_run1 spike_protein.fa -reference_genome.fa sequencing_run2 - -``` - -In this case, `*.fa` would match every file that ends with that word. - -```console -$ ls data/*.fa -``` - -``` -data/envelope_protein.fa data/reference_genome.fa data/spike_protein.fa -``` - - -:::exercise - -Use the `*` wildcard together with the copy command to copy all the protein files in the `data` folder to the `backup` folder that we created earlier. - -
HintRemember the "**c**o**p**y" command is `cp`.
- -
Answer - -First, make sure you are in the correct directory: `cd ~/Course_Materials/01-unix`. - -Now, we can use the `cp` command together with the `*` wildcard: - -```console -$ cp data/*protein.fa backup/ -``` - -We can check the files are all there with `ls backup/*protein.fa`. - -
- -::: - - -## Summary - -:::highlight - -**Key Points** - -- The file system is organised in a hierarchical way. -- Every user has a home directory, which on Linux is typically `/home/username/`. -- Locations in the filesystem are represented by a **path**: - - The `/` used at the _start_ of a path means the "root" directory (the start of the filesystem). - - `/` used in the _middle_ of the path separates different directories. -- Some of the commands used to navigate the filesystem are: - - `pwd` to print the working directory (or the current directory) - - `ls` to list files and directories - - `cd` to change directory -- Directories can be created with the `mkdir` command. -- Files can be moved and/or renamed using the `mv` command. -- Files can be copied with the `cp` command. To copy an entire directory (and its contents) we need to use `cp -r` (the `-r` option will copy files **r**ecursively). -- Files can be removed with the `rm` command. To remove an entire directory (and its contents) we need to use `rm -r` (the `-r` option will remove files **r**ecursively). - - Deleting files from the command line is _permanent_. -- We can operate on multiple files using the `*` wildcard, which matches "zero or more characters". For example `ls *.txt` would list all files that have a `.txt` file extension. -::: - diff --git a/02c-unix_text_manipulation.md b/02c-unix_text_manipulation.md deleted file mode 100644 index 58fce69..0000000 --- a/02c-unix_text_manipulation.md +++ /dev/null @@ -1,575 +0,0 @@ ---- -pagetitle: "SARS-CoV-2 Genomics" ---- - -# The Unix Shell: Text Manipulation - -:::highlight - -**Questions** - -- How can I inspect the content of and manipulate text files? -- How can I find and replace text patterns within files? - -**Learning Objectives** - -- Inspect the content of text files (`head`, `tail`, `cat`, `zcat`, `less`). -- Use the `*` wildcard to work with multiple files at once. -- Redirect the output of a command to a file (`>`, `>>`). -- Find a pattern in a text file (`grep`) and do basic pattern replacement (`sed`). - -::: - -:::note -This section has an accompanying slide deck. -::: - -## Looking Inside Files - -Often we want to investigate the content of a file, without having to open it in a text editor. -This is especially useful if the file is very large (as is often the case in bioinformatic applications). - -For example, let's take a look at the file `artic_primers_pool1.bed`. -We will start by printing the whole content of the file with the `cat` command, which stands for "concatenate" (we will see why it's called this way in a little while): - -```console -$ cat artic_primers_pool1.bed -``` - -This outputs a lot of text, because the file is quite long! - -Instead, it is often more useful to look only at the top few lines of the file. -We can do this with the `head` command: - -```console -$ head artic_primers_pool1.bed -``` - -``` -MN908947.3 30 54 nCoV-2019_1_LEFT 1 + -MN908947.3 385 410 nCoV-2019_1_RIGHT 1 - -MN908947.3 642 664 nCoV-2019_3_LEFT 1 + -MN908947.3 1004 1028 nCoV-2019_3_RIGHT 1 - -MN908947.3 1242 1264 nCoV-2019_5_LEFT 1 + -MN908947.3 1623 1651 nCoV-2019_5_RIGHT 1 - -MN908947.3 1875 1897 nCoV-2019_7_LEFT 1 + -MN908947.3 1868 1890 nCoV-2019_7_LEFT_alt0 1 + -MN908947.3 2247 2269 nCoV-2019_7_RIGHT 1 - -MN908947.3 2242 2264 nCoV-2019_7_RIGHT_alt5 1 - -``` - -By default, `head` prints the first 10 lines of the file. -We can change this using the `-n` option, followed by a number, for example: - -```console -$ head -n 2 artic_primers_pool1.bed -``` - -``` -MN908947.3 30 54 nCoV-2019_1_LEFT 1 + -MN908947.3 385 410 nCoV-2019_1_RIGHT 1 - -``` - -Similarly, we can look at the _bottom_ few lines of a file with the `tail` command: - -```console -$ tail -n 2 artic_primers_pool1.bed -``` - -``` -MN908947.3 29288 29316 nCoV-2019_97_LEFT 1 + -MN908947.3 29665 29693 nCoV-2019_97_RIGHT 1 - -``` - -Finally, if we want to open the file and browse through it, we can use the `less` command: - -```console -$ less artic_primers_pool1.bed -``` - -`less` will open the file and you can use ↑ and ↓ to move line-by-line or the Page Up and Page Down keys to move page-by-page. -You can exit `less` by pressing Q (for "quit"). -This will bring you back to the console. - -Finally, it can sometimes be useful to _count_ how many lines, words and characters a file has. -We can use the `wc` command for this: - -```console -wc artic_primers_pool*.bed -``` - -``` -110 660 4961 artic_primers_pool1.bed -108 648 4878 artic_primers_pool2.bed -218 1308 9839 total -``` - -In this case, we used the `*` wildcard to count lines, words and characters (in that order, left-to-right) of both our primer files. -Often, we only want to count one of these things, and `wc` has options for all of them: - -- `-l` counts lines only. -- `-w` counts words only. -- `-c` counts characters only. - -For example, if we just want to know how many primers we have (the number of lines in the files): - -```console -wc -l artic_primers_pool*.bed -``` - -``` -110 artic_primers_pool1.bed -108 artic_primers_pool2.bed -218 total -``` - -:::exercise - -- Use the `less` command to look inside the file `data/reference_genome.fa`. -- How many lines does this file contain? -- Use the `less` command again but with the option `-S`. Can you understand what this option does? - -
Answer - -We can investigate the content of the reference file using `less data/reference_genome.fa`. -From this view, it looks like this file contains several lines of content: the genome is almost 30kb long, so it's not surprising we see so much text! -We can use Q to quit and go back to the console. - -To check the number of lines in the file, we can use the `wc -l data/reference_genome.fa` command. -The answer is only 2. - -If we use `less -S data/reference_genome.fa` the display is different this time. -We see only two lines in the output. -If we use the → and ← arrows we can see that the text now goes "out of the screen". -So, what happens is that by default `less` will "wrap" long lines, so if a line of text is too long, it will continue it on the next line of the screen. -When we use the option `-S` it instead displays each line individually, and we can use the arrow keys to see the content that does not fit on the screen. - -
-::: - - -:::note -The primer files we just looked into are in a format called [BED](https://en.wikipedia.org/wiki/BED_(file_format)). -This is a standard bioinformatic file format used to store coordinates of genomic regions. -In this case, it corresponds to the coordinates of each primer start and end position in the SARS-CoV-2 reference genome (Wuhan-Hu-1). - -In the exercise we looked at another standard file format called [FASTA](https://en.wikipedia.org/wiki/FASTA). -This one is used to store nucleotide or amino acid sequences. -In this case, the complete nucleotide sequence of the SARS-CoV-2 reference genome. - -We will learn more about these files in [Intro to NGS](03-intro_ngs.html). -::: - - -## Combining several files - -We said that the `cat` command we used above stands for "concatenate". -This is because this command can be used to _concatenate_ (combine) several files together. -For example, if we wanted to combine both sets of primer pools into a single file: - -```console -cat artic_primers_pool1.bed artic_primers_pool2.bed -``` - -## Redirecting Output - -The `cat` command we just used printed the output to the screen. -But what if we wanted to save it into a file? -We can achieve this by sending (or _redirecting_) the output of the command to a file using the `>` operator. - -```console -cat artic_primers_pool1.bed artic_primers_pool2.bed > artic_primers.bed -``` - -Now, the output is not printed to the console, but instead sent to a new file. -We can check that the file was created with `ls`. - -If we use `>` and the output file already exists, its content will be replaced. -If what we want to do is _append_ the result of the command to the existing file, we should use `>>` instead. -Let's see this in practice in the next exercise. - -:::exercise - -1. List the files in the `data/sequencing_run1/` directory. Save the output in a file called "sequencing_files.txt". -2. What happens if you run the command `ls data/sequencing_run2/ > sequencing_files.txt`? -3. The operator `>>` can be used to _append_ the output of a command to an existing file. Try re-running both of the previous commands, but instead using the `>>` operator. What happens now? - -
Answer - -**Task 1** - -To list the files in the directory we use `ls`, followed by `>` to save the output in a file: - -```console -$ ls data/sequencing_run1/ > sequencing_files.txt -``` - -We can check the content of the file: - -```console -$ cat sequencing_files.txt -``` - -``` -sample1_run1.fastq -sample2_run1.fastq -sample3_run1.fastq -sample4_run1.fastq -``` - ----- - -**Task 2** - -If we run `ls data/sequencing_run2/ > sequencing_files.txt`, we will replace the content of the file: - -```console -$ cat sequencing_files.txt -``` - -``` -sample1_run2.fastq -sample2_run2.fastq -sample3_run2.fastq -sample4_run2.fastq -sample5_run2.fastq -sample6_run2.fastq -``` - ----- - -**Task 3** - -If we start again from the beggining, but instead use the `>>` operator the second time we run the command, we will append the output to the file instead of replacing it: - -```console -$ ls data/sequencing_run1/ > sequencing_files.txt -$ ls data/sequencing_run2/ >> sequencing_files.txt -$ cat sequencing_files.txt -``` - -``` -sample1_run1.fastq -sample2_run1.fastq -sample3_run1.fastq -sample4_run1.fastq -sample1_run2.fastq -sample2_run2.fastq -sample3_run2.fastq -sample4_run2.fastq -sample5_run2.fastq -sample6_run2.fastq -``` - -
-::: - -## Finding Patterns - -Something it can be very useful to find lines of a file that match a particular text pattern. -We can use the tool `grep` ("global regular expression print") to achieve this. -For example, let's find the word "nCoV-2019_46" in our primers file: - -```console -$ grep "nCoV-2019_46" artic_primers.bed -``` - -``` -MN908947.3 13599 13621 nCoV-2019_46_LEFT 2 + -MN908947.3 13602 13625 nCoV-2019_46_LEFT_alt1 2 + -MN908947.3 13962 13984 nCoV-2019_46_RIGHT 2 - -MN908947.3 13961 13984 nCoV-2019_46_RIGHT_alt2 2 - -``` - -We can see the result is all the lines that matched this word pattern. - -:::exercise - -Consider the file we previously saved in `results/variants.tsv`. -(Note, if you don't have this file, run the following commands: `mkdir -p results; mv output.tsv results/variants.tsv`) - -1. Create a new file called `results/alpha.tsv` that contains only the Alpha variant samples.
HintYou can use `grep` to find a pattern in a file. You can use `>` to _redirect_ the output of a command to a new file.
-2. How many samples are you left with? - -
Answer - -**Task 1** - -We can use `grep` to find a pattern in our text file and use `>` to save the output in a new file: - -```console -$ grep "Alpha" results/variants.tsv > results/alpha.tsv -``` - -We could investigate the output of our command using `less results/alpha.tsv`. - ----- - -**Task 2** - -We can use `wc` to count the lines of the newly created file: - -```console -$ wc -l results/alpha.tsv -``` - -Giving us 31 as the result. - -
-::: - - -## Text Replacement - -One of the most prominent text-processing utilities is the `sed` command, which is short for "stream editor". -A stream editor is used to perform basic text transformations on an input stream (a file or input from a pipeline). - -`sed` contains several sub-commands, but the main one we will use is the _substitute_ or `s` command. -The syntax is: - -```bash -sed 's/pattern/replacement/options' -``` - -Where `pattern` is the word we want to substitute and `replacement` is the new word we want to use instead. -There are also other "options" added at the end of the command, which change the default behaviour of the text substitution. -Some of the common options are: - -- `g`: by default `sed` will only substitute the first match of the pattern. If we use the `g` option ("**g**lobal"), then `sed` will substitute all matching text. -- `i`: by default `sed` matches the pattern in a case-sensitive manner. For example 'A' (Uppercase A) and 'a' (Lowercase A) are treated as different. If we use the `i` option ("case-**i**nsensitive") then `sed` will treat 'A' and 'a' as the same. - -For example, let's create a file with some text inside it: - -```console -$ echo "Hello world. How are you world?" > hello.txt -``` - -(Note: the `echo` command is used to print some text on the console. In this case we are sending that text to a file to use in our example.) - -If we do: - -```console -$ sed 's/world/participant/' hello.txt -``` - -This is the result - -``` -Hello participant. How are you world? -``` - -We can see that the first "world" word was replaced with "participant". -This is the default behaviour of `sed`: only the first pattern it finds in a line of text is replaced with the new word. -We can modify this by using the `g` option after the last `/`: - -```console -$ sed 's/world/participant/g' hello.txt -``` - -``` -Hello participant. How are you participant? -``` - -:::note -**Regular Expressions** - -Finding patterns in text can be a very powerful skill to master. -In our examples we have been finding a literal word and replacing it with another word. -However, we can do more complex text substitutions by using special keywords that define a more general pattern. -These are known as **regular expressions**. - -For example, in regular expression syntax, the character `.` stands for "any character". -So, for example, the pattern `H.` would match a "H" followed by any character, and the expression: - -```console -$ sed 's/H./X/g' hello.txt -``` - -Results in: - -``` -Xllo world. Xw are you world? -``` - -Notice how both "He" (at the start of the word "Hello") and "Ho" (at the start of the word "How") are replaced with the letter "X". -Because both of them match the pattern "H followed by any character" (`H.`). - -To learn more see this [Regular Expression Cheatsheet](https://www.keycdn.com/support/regex-cheatsheet). -::: - -### The `\` Escape Character - -You may have asked yourself, if the `/` character is used to separate parts of the `sed` substitute command, then how would we replace the "/" character itself in a piece of text? -For example, let's add a new line of text to our file: - -```console -$ echo "Welcome to this workshop/course." >> hello.txt -``` - -Let's say we wanted to replace "workshop/course" with "tutorial" in this text. -If we did: - -```console -$ sed 's/workshop/course/tutorial/' hello.txt -``` - -We would get an error: - -``` -sed: -e expression #1, char 5: unknown option to `s' -``` - -This is because we ended up with too many `/` in the command, and `sed` uses that to separate its different parts of the command. -In this situation we need to tell `sed` to ignore that `/` as being a special character but instead treat it as the literal "/" character. -To to this, we need to use `\` before `/`, which is called the "escape" character. -That will tell `sed` to treat the `/` as a normal character rather than a separator of its commands. -So: - -```console -$ sed 's/workshop\/course/tutorial/' hello.txt - ↑ - This / is "escaped" with \ beforehand -``` - -This looks a little strange, but the main thing to remember is that `\/` will be interpreted as the character "/" rather than the separator of `sed`'s substitute command. - - -:::exercise - -The file in `data/envelope_protein.fa` contains the amino acid sequence of the SARS-CoV-2 envelope protein for 4 patient samples. - -```console -$ cat data/envelope_protein.fa -``` - -``` ->patient01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Patient02/incomplete -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFY................... ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -We will cover FASTA files in the following section, for now all we need to know is that each sequence has a name in a line that starts with the character `>`. - -Use `sed` to achive the following: - -1. Substitute the word `patient` with `sample`.
HintSimilarly to how you can use the `g` option to go "global" substitution, you can also use the `i` option to do case-**i**nsensitive text substitution.
-1. Substitute the word `/incomplete` with `-missing`. -1. The character `.` is also a keyword used in _regular expressions_ to mean "any character". See what happens if you run the command `sed 's/./X/g' data/envelope_protein.fa`. How would you fix this command to literally only substitute the character `.` with `X`? - - -
Answer - -**Task 1** - -To replace the word `patient` with `sample`, we can do: - -```console -$ sed 's/patient/sample/i' data/envelope_protein.fa -``` - -``` ->sample01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->sample02/incomplete -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFY................... ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -We have to use the option `i` to tell the `sed` that we want to match the word `patient` in case-insensitive manner. - ----- - -**Task 2** - -For the second task, if we do: - -```console -$ sed 's//incomplete/-missing/' data/envelope_protein.fa -``` - -We will get an error. -We need to use `\` to _escape_ the `/` keyword in our pattern, so: - -```console -$ sed 's/\/incomplete/-missing/' data/envelope_protein.fa -``` - -``` ->patient01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Patient02-missing -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFY................... ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - ----- - -**Task 3** - -Finally, to replace the character `.`, if we do: - -```console -$ sed 's/./X/g' data/envelope_protein.fa -``` - -``` -XXXXXXXXXX -XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX -XXXXXXXXXXXXXXXXXXXXX -XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX -XXXXXXXXX -XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX -XXXXXXXXX -XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX -``` - -Everything becomes "X"! -That's because `.` is a keyword used in _regular expressions_ to mean "any character". -Because we are using the `g` option (for "global substitution"), we replaced every single character with "X". -To literally replace the character ".", we need to again use the `\` escape character, so: - -```console -$ sed 's/\./X/g' data/envelope_protein.fa -``` - -``` ->patient01 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Patient02/incomplete -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNLSLVKPSFYXXXXXXXXXXXXXXXXXXX ->sample04 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* ->Sample05 -MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV* -``` - -
- - -::: - - -## Summary - -:::highlight -**Key Points** - -- The `head` and `tail` commands can be used to look at the top or bottom of a file, respectively. -- The `less` command can be used to interactively investigate the content of a file. Use ↑ and ↓ to browse the file and Q to quit and return to the console. -- The `cat` command can be used to combine multiple files together. The `zcat` command can be used instead if the files are compressed. -- The `>` operator redirects the output of a command into a file. If the file already exists, it's content will be overwritten. -- The `>>` operator also redictects the output of a command into a file, but _appends_ it to any content that already exists. -- The `grep` command can be used to find the lines in a text file that match a text pattern. -- The `sed` tool can be used for advanced text manipulation. The "substitute" command can be used to text replacement: `sed 's/pattern/replacement/options'`. -::: diff --git a/02d-unix_pipes.md b/02d-unix_pipes.md deleted file mode 100644 index f46fd75..0000000 --- a/02d-unix_pipes.md +++ /dev/null @@ -1,276 +0,0 @@ ---- -pagetitle: "SARS-CoV-2 Genomics" ---- - -# The Unix Shell: Combining Commands - -:::highlight - -**Questions** - -- How can I combine existing commands to do new things? -- How can I save and re-use commands? - -**Learning Objectives** - -- Construct command pipelines with two or more stages. -- Create files from the command line using a text editor. -- Write a shell script that runs a command or series of commands for a fixed set of files. -- Run a shell script from the command line. -- Customise shell scripts to work with inputs defined by the user. - -::: - -:::note -This section has an accompanying slide deck. -::: - -## The `|` Pipe - -Now that we know a few basic commands, we can finally look at the shell's most powerful feature: the ease with which it lets us combine existing programs in new ways. - -If we wanted to count how many files we have in the `data/sequencing_run1/` directory, we could do it in two steps: - -```console -$ ls data/sequencing_run1/ > sequencing_files.txt -$ wc -l sequencing_files.txt -``` - -- Used the `ls` command to list the files and save the output in a text file (with the `>` redirect operator). -- And then count the lines in the resulting file. - -But what if we wanted to do this without creating the file? -It turns out we can send the output of one command and pass it on to another using a special operator `|` called the **pipe**. -Like this: - -```console -$ ls data/sequencing_run1/ | wc -l -``` - -``` -4 -``` - -What happened there is that the output of `ls` was sent "through the pipe" to the `wc` command. - -:::exercise - -Let's go back to our primer files: - -```console -$ ls artic_primers_pool*.bed -``` - -Using the `|` pipe, write a chain of commands that does the following: - -- Combine (or "concatenate") both primer files.
HintUse the `cat` command.
-- Search for lines with the pattern "LEFT".
HintUse the `grep` command.
-- Count the number of lines of the output. - -
Answer - -The three commands we want to use to achieve this are: - -- `cat` to _concatenate_ the files. -- `grep` to only print the lines that match "LEFT". -- `wc -l` to count the number of lines. - -We can chain all the commands together like this: - -```console -$ cat artic_primers_pool*.bed | grep "LEFT" | wc -l -``` - -``` -109 -``` - -
- -::: - - -:::exercise - -If you had the following two text files: - -```console -cat animals_1.txt -``` - -``` -deer -rabbit -raccoon -rabbit -``` - -```console -cat animals_2.txt -``` - -``` -deer -fox -rabbit -bear -``` - -What would be the result of the following command? - -```console -cat animals*.txt | head -n 6 | tail -n 1 -``` - -
Answer - -The result would be "fox". -Let's go through this step-by-step. - -`cat animals*.txt` would combine the content of both files: - -``` -deer -rabbit -raccoon -rabbit -deer -fox -rabbit -bear -``` - -`head -n 6` would then print the first six lines of the combined file, so: - -``` -deer -rabbit -raccoon -rabbit -deer -fox -``` - -And finally `tail -n 1` would return the last line of this output: - -``` -fox -``` - -
-::: - - -## Shell Scripts - -So far we have been running commands directly on the console, in an interactive way. -However, to re-run a series of commands (or an analysis), we can save the commands in a file so that we can re-run all those operations again later by typing a single command. -The file containing the commands is usually called a **shell script** (you can think of them as small programs). - -For example, let's create a shell script that counts the number of sequences in one of our FASTA files in the `data` directory. -We could achieve this with the following command: - -```console -$ cat data/envelope_protein.fa | grep ">" | wc -l -``` - -To write a shell script we save this command within a text file. -But how do we create a text file from within the command line? - -### Creating Files - -There are many text editors available for programming, but we will use a simple one that can be used from the command line, called `nano`. - -First let's create a directory to save our script: - -```console -$ mkdir scripts/ -``` - -Now we can create a file with _Nano_ in the following way: - -```console -$ nano scripts/count_fasta_sequences.sh -``` - -This opens a text editor, where you can type the commands you want to save in the file. -Once we're happy with our text, we can press Ctrl+O (press the Ctrl or Control key and, while holding it down, press the O key) to write our data to disk. -We'll be asked what file we want to save this to: press Return to accept the suggested default of `scripts/count_fasta_sequences.sh`. -Once our file is saved, we can use Ctrl+X to quit the editor and return to the shell. - -We can check with `ls scripts/` that our new file is there. - -![Screenshot of the command line text editor _Nano_. In this example, we also included `!#/bin/bash` in the first line of the script. This is called a [_shebang_](https://en.wikipedia.org/wiki/Shebang_(Unix)) and is a way to inform that this script uses the program `bash` to run the script.](images/nano.png) - -Note that because we saved our file with `.sh` extension (the conventional extension used for shell scripts), _Nano_ does some colouring of our commands (this is called _syntax highlighting_) to make it easier to read the code. - -:::note -**Text Editors** - -When we say, "`nano` is a text editor," we really do mean "text": it can only work with plain character data, not tables, images, or any other human-friendly media. -We use it in examples because it is one of the least complex text editors. -However, because of this trait, it may not be powerful enough or flexible enough for the work you need to do after this workshop. - -On Unix systems (such as Linux and Mac OS X), many programmers use [Emacs](http://www.gnu.org/software/emacs/) or [Vim](http://www.vim.org/). -Both of these run from the terminal and have very advanced features, but require more time to learn. - -Alternatively, programmers also use graphical editors, such as [Visual Studio Code](105-vs_code.md). -This software offers many advanced capabilities and extensions and works on Windows, Mac OS and Linux. -::: - - -### Running Scripts - -Now that we have our script, we can run it using the program `bash`: - -```console -$ bash scripts/count_fasta_sequences.fa -``` - -Which will print the result on our screen. - - -### Customising Scripts - -The script we wrote so far works on a specific FASTA file (in our example `data/envelope_protein.fa`). -But what if we wanted to give it as input a file of our choice? -We can make our script more versatile by using a special _shell variable_ that means "the first argument on the command line". -Here is our modified script: - -```bash -#!/bin/bash - -echo "Processing file: $1" - -cat "$1" | grep ">" | wc -l -``` - -We have done two things: - -- We use the variable `$1` to indicate the file that we want to process will be given from the command line. -- We use the `echo` command to print an informative message to the user. - -If we run our new script, this is the result: - -```console -$ bash scripts/count_fasta_sequences.sh data/envelope_protein.fa -``` - -``` -Processing file: data/envelope_protein.fa -4 -``` - -## Summary - -:::highlight - -**Key Points** - -- The `|` pipe allows to chain several commands together. The output of the command on the left of the pipe is sent as input to the command on the right. -- The `nano` text editor can be used to create or edit files from the command line. -- We can save commands in a text file, which we call a _shell script_. Shell scripts have extension `.sh`. -- We can run a shell script using the program `bash`. -- We can use custom inputs to our script using the special variable `$1`. - -::: diff --git a/04-artic_nextflow.md b/04-artic_nextflow.md deleted file mode 100644 index 6d3f31d..0000000 --- a/04-artic_nextflow.md +++ /dev/null @@ -1,663 +0,0 @@ ---- -pagetitle: "SARS-CoV-2 Genomics" ---- - -:::warning -**Update May 2022** - -Please note that this is an old version of this lesson for workshops that ran prior to May 2022. -We kept this in the materials as a reference, but would recommend that you look at the [new version of the lesson](04-artic_nextflow.html) instead. -::: - -# Consensus Assembly - -::: highlight - -**Questions** - -- What are the steps involved in assembling SARS-CoV-2 genome from amplicon sequencing (Illumina and Nanopore)? -- How can I do reference-based assembly of SARS-CoV-2 genomes? - -**Learning Objectives** - -- Recognise what the main steps are in processing raw sequencing data to generate consensus genome sequences, including sequence alignment, primer trimming and consensus generation. -- Recognise the differences between Illumina and Nanopore pipelines. -- Apply the `connor-lab/ncov2019-artic-nf` _Nextflow_ pipeline to generate a consensus sequence from Illumina and Nanopore data. -- Troubleshoot issues when running a _Nextflow_ pipeline and resume the pipeline in case of a failure. -- Check basic data quality metrics on the assembled sequences and identify sequences for downstream analyses. - -::: - -:::note -This section has an accompanying slide deck. -::: - - -## SARS-CoV-2 Consensus Assembly - -As we discussed [earlier in the course](01-intro.html), the starting material for sequencing SARS-CoV-2 samples from infected patients is PCR-amplified DNA generated with a panel of primers that covers the whole SARS-CoV-2 genome (for example, the primers developed and updated by the ARTIC network). -This material can then be sequenced using either _Illumina_ or _Nanopore_ platforms. - -Although different sotware tools are used depending on which kind of sequencing platform was used, the main goal is the same: to align the sequencing reads to the reference genome, and identify any DNA changes (SNPs or Indels) relative to the reference genome (_Wuhan-Hu-1_). -This is called **consensus assembly**, since we are _assembling_ the genome of our sample from the PCR-amplified fragments and generating a _consensus_ sequence based on changes present in several reads covering a particular position of the genome. - -The general data processing steps are: - -- Filter high-quality sequencing reads. -- Map the reads to the _Wuhan-Hu-1_ reference genome. -- Trim the primers from the aligned reads based on the primer location file (BED file). -- Perform variant calling (SNPs and indels) to identify changes relative to the reference sequence. -- Generate a consensus sequence for the sample based on those variants. -- "Mask" low-quality positions to avoid mis-identifying variants. - -![Overview of the consensus assembly procedure from amplicon sequencing reads. In this schematic, each read spans the whole length of a PCR amplicon, which is what is expected from Nanopore reads. With Illumina data, there would be two pairs of reads starting at each end of the PCR amplicon.](images/workflow_overview.svg) - - -:::note -**Primer trimming** is a key step of the data processing, otherwise SNPs might be missed at the primer sites, on the final consensus sequence. -This is because the primer sequence is retained during PCR instead of the original sequence of the sample. -Because the PCR amplicons overlap with each other, we can trim the primers from each read and do variant calling after trimming. -An example of this is shown in the Figure above. - - -::: - - -## Bioinformatic Workflows/Pipelines - -As can already be seen from the brief description above, bioinformatic analyses always involve multiple steps where data is gathered, cleaned and integrated to give a final set of processed files of interest to the user. -These sequences of steps are called a **workflow** or **pipeline**. -As analyses become more complex, workflows may include the use of many different software tools, each requiring a specific set of inputs and options to be defined. -Furthermore, as we want to chain multiple tools together, the inputs of one tool may be the output of another, which can become challenging to manage. - -Although it is possible to code such workflows using _shell_ scripts, these often don't scale well across different users and compute setups. -To overcome these limitations, dedicated [_workflow management software_](https://en.wikipedia.org/wiki/Workflow_management_system) packages have been developed to help standardise workflows and make it easier for the user to process their data. - -Nextflow - -Two of the most popular _workflow software_ packages are [_Snakemake_](https://snakemake.readthedocs.io/en/stable/) and [_Nextflow_](https://www.nextflow.io/). -We will not cover how to develop workflows with these packages, but rather how to use an existing workflow to generate consensus sequences from SARS-CoV-2 data. - -### Why Use a Standardised Workflow? {.unlisted .unnumbered} - -These are some of the key advantages of using a standardised workflow for our analysis: - -- Fewer errors - because the workflow automates the process of managing input/output files, there are less chances for errors or bugs in the code to occur. -- Consistency and reproducibility - analysis ran by different people should result in the same output, regardless of their computational setup. -- Software installation - all software dependencies are automatically installed for the user using solutions such as _Conda_, _Docker_ and _Singularity_ (more about these in a later section of the course). -- Scalability - workflows can run on a local desktop or scale up to run on _high performance compute clusters_. -- Checkpoint and resume - if a workflow fails in one of the tasks, it can be resumed at a later time. - - -## SARS-CoV-2 Workflow {.tabset} - -For this section, we will be working from the following directory: - -```console -$ cd ~/Course_Materials/02-consensus -``` - -This directory contains two sets of sequencing data, from Illumina and Nanopore platforms. -These data were generated using the ARTIC protocol with V3 primer scheme. - -To generate consensus SARS-CoV-2 genomes from these data, we will use a _Nextflow_ workflow that was developed by the [Connor Lab](https://github.com/connor-lab/ncov2019-artic-nf). -Its objective was to harmonise the assembly of SARS-CoV-2 genomes from both Illumina and Nanopore amplicon sequencing data. -This workflow therefore includes different sub-workflows, which are launched depending on the type of sequence data we have. - -To see all the options available with this workflow, we run: - -```console -$ nextflow run ncov2019-artic-nf --help -``` - -``` -Usage: - nextflow run connor-lab/ncov2019-artic-nf -profile (singularity,docker,conda) ( --illumina | --nanpolish | --medaka ) --prefix [prefix] [workflow-options] - -Description: - Turn Nanopore or Illumina SARS-CoV2 sequencing reads generated by ARTIC tiling amplification protocols into consensus sequences. - - Nanopore: ARTIC (https://github.com/artic-network/fieldbioinformatics) - - Illumina: iVar (https://github.com/andersen-lab/ivar) - - All options set via CLI can be set in conf directory - -Nextflow arguments (single DASH): - -profile Allowed values: conda, singularity, docker, [COG-UK institutional profile] - -Mandatory workflow arguments (mutually exclusive): - --illumina Run the Illumina workflow - --nanopolish Run the Nanopore/Nanopolish workflow (https://github.com/jts/nanopolish) - --medaka Run the Nanopore/Medaka workflow (https://github.com/nanoporetech/medaka) - -[...further output omitted...] -``` - -From the help output printed above, we can see that there are three mandatory workflow arguments named `--illumina`, `--nanopolish` and `--medaka`. -These are the options used to launch each of the sub-workflows available to us, which we detail below. - -:::note -The _Wuhan-Hu-1_ reference genome sequence and the amplicon primer locations (in BED file format) can all be found on the ARTIC [Primer Schemes repository](https://github.com/artic-network/primer-schemes/tree/master/nCoV-2019). -The workflow we will use takes care of downloading these files for us automatically, however it can be useful to know where to find them, in case you want to use other tools that require these files. -::: - - -### Illumina (FASTQ) - -The Illumina sub-workflow is based on several standard bioinformatic tools and, importantly, on the [iVar](https://andersen-lab.github.io/ivar/html/) software, which was developed for analysing amplicon-based sequencing data. - -![Schematic of the key steps in the `--illumina` sub-workflow.](images/workflow_illumina.svg) - -
Click to see more details about this sub-workflow - -In summary, the steps performed by the `--illumina` sub-workflow are: - -- Adapter trimming with [`trim_galore`](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). -- Trimmed sequences are mapped to the _Wuhan-Hu-1_ reference genome using `bwa mem`. - - The reference genome is "indexed" to be ready for mapping using `bwa index`. - - The mapped files are sorted by coordinate (using `samtools sort`). - - Unmapped reads are removed using `samtools view -F4`. -- Primers are removed from the aligned reads using `ivar trim` (using the primer BED file). - - Reads are retained if they are at least 30bp after clipping the primers. - - Reads are also clipped if they fall below a Phred-quality score of 20. - - These defaults (30bp minimum length and Phred-score threshold) can be adjusted with the `--illuminaKeepLen` and `--illuminaQualThreshold` options on the nextflow pipeline. Keeping the default options is advised. - - There is an option to retain reads even if no primer is detected on them (`--allowNoprimer`). This should be set depending on the protocol used: ligation = false, tagmentation = true (default: true). -- Variant calling is done using `ivar variants`. - - Only positions with at least 10x depth are called. Generally it is advised not to go below this threshold. -- Consensus sequences are called using `ivar consensus`. - - Only positions with a minimum depth of 10x are called. Otherwise, they are marked as an ambiguous base 'N'. - -
- -To run the pipeline on Illumina data, we use the following general command: - -```console -nextflow run ncov2019-artic-nf \ - -with-report -with-dag \ - -profile conda \ - --outdir OUTPUT_DIRECTORY \ - --prefix NAME_OF_YOUR_ANALYSIS_RUN \ - --schemeVersion V3 \ - --directory DIRECTORY_WITH_FASTQ_FILES \ - --illumina -``` - -The key option here is `--illumina`, which makes sure that the correct sub-workflow will be used. - -### Nanopore (basecalled FASTQ) - -The nanopore sub-workflow is based on the [ARTIC bioinformatics protocol](https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html) and uses several of the tools from the accompanying [`artic` software package](https://artic.readthedocs.io/en/latest/). - -This sub-workflow is similar to the other nanopore sub-workflow, the main difference is the software used for generating a consensus sequence (`medata` instead of `nanopolish`). - -![Schematic of the key steps in the `--medaka` sub-workflow.](images/workflow_medaka.svg) - -
Click to see more details about this sub-workflow - -In summary, the steps performed by the `--medaka` sub-workflow are: - -- Aggregate and filter reads to ensure they pass minimum read length thresholds using `artic guppyplex`: - - minimum length 400bp (can be changed with `--min_length` option) - - maximum length 700bp (can be changed with `--max_length` option) -- Run the `artic minion` tool, which internally does: - - Read mapping to reference genome using `minimap2` (can be changed to use `bwa mem` with the `--bwa` option). - - Trim primers from the aligned reads based on the known primer positions in the BED file (using a custom python script called `align_trim.py`). - - Call consensus sequences and variants using `medaka consensus` and `medaka variant`: - - Positions with less than 20x depth are "masked" by assigning the ambiguous base 'N'. It is not advised to go below this threshold as the models used to call variants do not perform as well. - -
- -To run our pipeline on basecalled data (FASTQ files), we use the following command: - -```console -nextflow run ncov2019-artic-nf \ - -with-report -with-dag \ - -profile conda \ - --outdir OUTPUT_DIRECTORY \ - --prefix NAME_OF_YOUR_ANALYSIS_RUN \ - --schemeVersion V3 \ - --basecalled_fastq DIRECTORY_WITH_FASTQ_FILES \ - --medaka -``` - -The key option here is `--medaka`, which makes sure that the correct sub-workflow will be used. -We also specify the directory containing our basecalled FASTQ files with `--basecalled_fastq`. -This should contain sub-directories for each barcoded sample following the naming convention `barcodeXXXX` (where X is a number between 0 and 9). -By default, `guppy_basecaller` generates such a folder structure. - -:::warning -For this workflow to work, you should not use compressed FASTQ files (`fastq.gz`) but rather uncompressed files. -Otherwise the workflow will fail with an error "Couldn't detect whether your Nanopore run was barcoded or not." -::: - -### Nanopore (signal-level FAST5) - -The nanopore sub-workflow is based on the [ARTIC bioinformatics protocol](https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html) and uses several of the tools from the accompanying [`artic` software package](https://artic.readthedocs.io/en/latest/). - -This sub-workflow is similar to the other nanopore sub-workflow, the main difference is the software used for generating a consensus sequence (`nanopolish` instead of `medaka`). - -![Schematic of the key steps in the `--nanopolish` sub-workflow.](images/workflow_nanopolish.svg) - -
Click to see more details about this sub-workflow - -In summary, the steps performed by the `--nanopolish` sub-workflow are: - -- Filter reads to ensure they pass minimum read length thresholds: - - minimum length 400bp (can be changed with `--min_length` option) - - maximum length 700bp (can be changed with `--max_length` option) -- Run the `artic minion` tool, which internally does: - - Read alignment to reference genome using `minimap2` (can be changed to use `bwa mem` with the `--bwa` option). - - Trim primers from the aligned reads (based on the known primer positions in the BED file). - - Call consensus sequences and variants using `nanopolish variants` if using signal-level FAST5 files. - - Positions with less than 20x depth are assigned the ambiguous base 'N'. It is not advised to go below this threshold as the models used to call variants do not perform as well. -- Unmapped reads are removed using `samtools view -F4`. - -
- -To run our pipeline on signal-level data (FAST5 files), we use the following command: - -```console -nextflow run ncov2019-artic-nf \ - -with-report -with-dag \ - -profile conda \ - --outdir OUTPUT_DIRECTORY \ - --prefix NAME_OF_YOUR_ANALYSIS_RUN \ - --schemeVersion V3 \ - --fast5_pass DIRECTORY_WITH_FAST5_FILES \ - --sequencing_summary SEQUENCING_SUMMARY_FILE \ - --nanopolish -``` - -The key option here is `--nanopolish`, which makes sure the correct sub-workflow is used. -We also need to specify a directory containing our FAST5 files with `--fast5_pass` and a path to the `sequencing_summary.txt` file that is standard output from the `guppy` software. - -## {.unlisted .unnumbered} - -The first few arguments used in the above commands are generic options for Nextflow (they can be used with any workflow, not just specifically with our SARS-CoV-2 workflow): - -- `-with-report` generates a report about the resources used at different steps in the pipeline (e.g. how long each step took to run, how many CPUs and memory was used, etc.). These will be found in a sub-directory called `pipeline_info`. -- `-with-dag` produces a "directed acyclic graph", which shows how the different steps of the pipeline link to each other. This will be saved in the same `pipeline_info` directory. -- `-profile conda` uses the _Conda_ package manager to automatically install all the necessary software used by this pipeline (we will talk more about _Conda_ later in the course). - -The following arguments are specific to our workflow: - -- `--illumina`, `--medaka` or `--nanopolish` indicates whether want to use the pipeline steps developed for Illumina or Nanopore data. -- `--outdir results/consensus` indicates that we want the results of the pipeline to be saved in the directory `results/consensus` (the directory will be created if it does not exist). -- `--prefix run_name` is a prefix that Nextflow will use to name some of the output files. This is useful if we had multiple runs in the same project and wanted to run the pipeline on each run. -- `--schemeVersion V3` indicates the Artic primer version used when preparing the sequencing libraries (V1, V2, V3 or V4). -- `--directory`, `--basecalled_fastq` or `--fast5_pass` is the directory where all the sequencing data is stored. For Illumina, the workflow will automatically recognise files with suffix "_1.fastq.gz" or "_2.fastq.gz" as being paired-end data. - - -:::note -The term **masking** is often used to refer to the process of converting sequence bases to the ambiguous character 'N'. -You may come across this term in the documentation of certain tools, for example: -"Positions with less than 20x depth of sequencing are masked." - -Masks are not limited to depth of sequencing. For example, [reference genomes from ENSEMBL](https://ensemblgenomes.org/) are available with masked repeat or low-complexity sequences (e.g. around centromeres, transposon-rich regions, etc.). - -The term **soft masking** is also used to refer to cases where, instead of using the ambiguous character 'N', sequences are masked with a lowercase. -For example: - -``` ->seq_with_soft_masking -ACAGACTGACGCTGTcatgtatgtcgacGATAGGCTGATGGCGAGTGACTCGAG ->seq_with_hard_masking -ACAGACTGACGCTGTNNNNNNNNNNNNNGATAGGCTGATGGCGAGTGACTCGAG -``` -::: - -### Running the Workflow - -Let's see an example in action by using some example data. -If you go to the directory `02-consensus/uk_illumina/` in the course materials, you will find several FASTQ files in the `data` directory. -There is also a _shell script_ (in `scripts/run_illumina_workflow.sh`) that contains the commands we will use to run the workflow on these data. - - - -Opening the script, we can see the following commands: - -```bash -# create output directory -mkdir results - -# run the workflow -nextflow run ncov2019-artic-nf -with-report -with-dag -profile conda --outdir results/consensus/ --prefix uk --schemeVersion V3 --directory data/reads/ --illumina -``` - -It first creates a results directory (to store our output files) and then runs the `nextflow` command using the `--illumina` sub-workflow. -We could run these commands one at a time by copy/pasting them to the terminal. -Or alternatively, we can run the entire script using `bash scripts/run_illumina_workflow.sh` - -When you start running the workflow, you will get a list of the workflow steps and their progress. -Once the workflow is complete, you should see something similar to the following: - -```console -[94/4d963f] process > ncovIllumina:prepareReferenceFiles:articDownloadScheme (https://github.com/artic-network/p... [100%] 1 of 1 ✔ -[c1/5147e1] process > ncovIllumina:prepareReferenceFiles:indexReference (nCoV-2019.reference.fasta) [100%] 1 of 1 ✔ -[62/7f03b3] process > ncovIllumina:sequenceAnalysis:readTrimming (ERR5921612) [100%] 7 of 7 ✔ -[e8/e7df33] process > ncovIllumina:sequenceAnalysis:readMapping (ERR5932418) [100%] 7 of 7 ✔ -[fe/71e5df] process > ncovIllumina:sequenceAnalysis:trimPrimerSequences (ERR5932418) [100%] 7 of 7 ✔ -[08/302b91] process > ncovIllumina:sequenceAnalysis:callVariants (ERR5932418) [100%] 7 of 7 ✔ -[6b/ff2d9d] process > ncovIllumina:sequenceAnalysis:makeConsensus (ERR5932418) [100%] 7 of 7 ✔ -[83/2dcd07] process > ncovIllumina:sequenceAnalysis:makeQCCSV (ERR5932418) [100%] 7 of 7 ✔ -[90/2aa165] process > ncovIllumina:sequenceAnalysis:writeQCSummaryCSV (uk) [100%] 1 of 1 ✔ -[b3/d57d1f] process > ncovIllumina:sequenceAnalysis:collateSamples (ERR5932418) [100%] 7 of 7 ✔ -Completed at: 22-Feb-2022 17:00:04 -Duration : 12m 5s -CPU hours : 0.9 -Succeeded : 52 -``` - -You should also get several output files in the results folder specified with our `nextflow` command. -We will detail what these files are in the following section. - -:::exercise - -Go to the course materials directory `02-consensus/india_nanopore` (on our training machines `cd ~/Course_Materials/02-consensus/india_nanopore`). -This contains Nanopore sequencing data for several samples collected in India. -Nanopore data is organised in directories named according to the convention `barcodeXX` where `XX` is a number. -The `--medaka` workflow expects to be given as an input a directory containing several sub-directories named in that way (this is a standard output from the program used to generate FASTQ files from Nanopore data). - -- Using `nano`, open the script found in `scripts/run_medaka_workflow.sh`. -- Fix the code in the script where you see the word "_FIXME_". Output the results to a directory called `results/consensus/`. -- Run the nextflow command on the terminal. This may take ~5 minutes to complete. -- Once complete, use the file explorer and go to the results folder to open the file in `pipeline_info/execution_report.html`. - - How long did the workflow take to run? - - Which step of the pipeline took the longest to run? - -
Answer - -The fixed code is: - -```bash -# create output directory -mkdir -p results - -# run the workflow -nextflow run ncov2019-artic-nf -with-report -with-dag -profile conda --outdir results/consensus/ --prefix india --schemeVersion V3 --basecalled_fastq data/reads/ --medaka -``` - -What we did to fix it was: - -- Set the output directory to `results/consensus/` -- Provide the path to the directory containing the FASTQ files for each sample, within their respective `barcodeXX` sub-directories. - -After the workflow was complete, we get the following message: - -``` -Completed at: 22-Feb-2022 17:23:54 -Duration : 5m 30s -CPU hours : 0.3 -Succeeded : 37 -``` - -From this we can already answer that it took around 5 minutes to run. -To know which step of the workflow took the longest time to run, we open the workflow report file. -In the section "Job Duration" we can see a graph that looks like this: - -![](images/workflow_job_duration.png) - -This indicates that the step running the `artic minion` tool takes the longest. -This is not surprising as this is the step where most of the work is happening (mapping, primer trimming and making a consensus sequence). -You can revise the steps of the workflow in the [respective section above](04-artic_nextflow.html#SARS-CoV-2_Workflow) - - -
- -::: - -## Output Files {.tabset} - -After running our pipeline, we will get several output directories. -Again, the directories we get will depend on which version of the workflow was used. - -### Illumina - -The output directory will contain the following folders: - -| Directory | Description | -| :- | :- | -| `ncovIllumina_sequenceAnalysis_readTrimming` | FASTQ files with Illumina adapters trimmed. | -| `ncovIllumina_sequenceAnalysis_readMapping` | BAM files with reads mapped to the reference genome. | -| `ncovIllumina_sequenceAnalysis_trimPrimerSequences` | same BAM files but with primers trimmed from the reads. | -| `ncovIllumina_sequenceAnalysis_makeConsensus` | FASTA files with consensus sequences for each sample. | -| `ncovIllumina_sequenceAnalysis_callVariants` | TSV files with variants identified in each sample. | -| `qc_plots` | PNG files with coverage plots (detailed below). | -| `qc_pass_climb_upload` | FASTA and BAM files for samples passing default QC thresholds. | - -We also get a file in the output directory that compiles several quality metrics called `PREFIX.qc.csv` (where "PREFIX" is what was defined with the `--prefix` option when running the workflow). - - -### Nanopore (medaka) - -| Directory | Description | -| :- | :- | -| `articNcovNanopore_sequenceAnalysisMedaka_articGuppyPlex` | FASTQ files with filtered reads. | -| `articNcovNanopore_sequenceAnalysisMedaka_articMinIONMedaka` | Output files from the "medaka" sub-workflow (see next table). | -| `qc_plots` | PNG files with coverage plots (detailed below). | -| `qc_pass_climb_upload` | FASTA and BAM files for samples passing default QC thresholds. | - -The "medaka" sub-workflow outputs several files for each sample. -Some of the more relevant ones being: - -| File Name | Description | -| :- | :- | -| `*.consensus.fasta` | Consensus FASTA file. | -| `*.pass.vcf.gz` | VCF file with identified variants (relative to the Wuhan reference) that pass quality thresholds. | -| `*.primertrimmed.rg.sorted.bam` | BAM file with mapped reads (with primers trimmed). | - -Finally, we also get a file in the output directory that compiles several quality metrics called `PREFIX.qc.csv` (where "PREFIX" is what was defined with the `--prefix` option when running the workflow). - -## {.unlisted .unnumbered} - - -## Quality Control - -The first file we want to look at in terms of quality control is the CSV in our workflow output directory. -This file can be open in any spreadsheet software (e.g. Excel or LibreOffice). - -For each sequence, it contains information about: - -- `sample_name` The name of the sample. -- `pct_N_bases` The percentage of bases in the consensus sequence that were marked as ambiguous ('N'). These are bases that didn't pass the minimum sequencing depth threshold (10x for Illumina or 20x for Nanopore). -- `pct_covered_bases` The percentage of bases that were called (i.e. above the sequencing depth threshold). -- `longest_no_N_run` The maximum length of consecutive non-missing ('N') bases in the consensus sequence. -- `num_aligned_reads` Total number of reads aligned to the reference genome. -- `fasta` Name of the FASTA file. -- `bam` Name of the BAM file. -- `qc_pass` Contains TRUE or FALSE depending on whether the sample passed default quality thresholds. - -The **default quality thresholds are very permissive** (and unfortunately cannot be changed). -A sample "passes" as high quality if at least 50% of the genome is covered. -This is not a very high threshold, and typical recommendations are to only use samples in downstream analysis if at they have at least 80-90% coverage. - -Therefore, it is always good to open this file and sort the samples by each of these quality metrics to identify any problematic samples (and consider excluding them from downstream analysis). - -Another helpful quality control output are the plots found in the `qc_plots` folder. - -![Quality control plots showing: the average read depth across 200bp windows (green line); the fraction of 'N' ambiguous bases across 10bp windows (red).](images/consensus_qc.svg) - - -For the nanopore sub-workflow you also get coverage barplots for individual primer pairs, which is useful to identify any particular fragments that did not get amplified. - -![Quality control barplot for nanopore data showing the read depth for each individual PCR amplicon. Blue and orange show the two pools of primers used for generating the libraries.](images/barcode_qc.svg) - -These may be particularly useful to identify if there is a systematic "dropout" of particular amplicons. -This may occur, for example, if a common variant has mutations that affect the primer hybridization during PCR. - -:::exercise - -Use the file browser to navigate to the results folder generated in the previous exercise (`india_nanopore/results/consensus`). - -Open the `india.qc.csv` file containing quality control metrics. - -- How many samples passed the default quality control filters? -- Which of these samples would you consider uploading to GISAID? -- Go to the `qc_plots` folder and compare the results in these plots with the summary in the QC file. -- Can you think of possible reasons why the coverage of some samples is lower than for others? - -
Answer - -The `india.qc.csv` can be opened in a regular spreadsheet software (in our training environment we are using the open-source LibreOffice, but Excel would also work). -When we open the file, we can see the last column indicating that all 7 samples passed the QC thresholds. - -However, examining the column named "pct_covered_bases" we can see that some samples have very low coverage, as low as 51.58% (the sample with "barcode01"). - -For the purposes of uploading data to public databases such as GISAID, we should probably only consider those samples with coverage > 90%. -In our case, this would be samples barcode18, barcode16, barcode34 and barcode31. - -Looking at the QC plots for some of these samples, we can see that there is an overall low coverage across the genome, with several regions below the coverage threshold of 20 reads. -The four samples we would consider for GISAID have substantially better coverage, with only a few regions falling below the threshold. - -Two possible reasons that some samples had poor coverage are: - -- The sample had very low viral load. This could be confirmed by looking at the Ct value from a qPCR (unfortunately this information was not given to us in the metadata sheet). If the Ct value is above 30 then there is no point re-sequencing the sample. -- The sample had Ct < 30 but was not sequenced enough. If this was the case, we could consider sequencing it again. - -
- -::: - - -## Cleaning FASTA Files - -To proceed with our analysis, we need a FASTA file containing _all_ of our consensus sequences. -However, our `ncov2019-artic-nf` Nextflow workflow outputs _separate_ FASTA files for each sample and in individual directories. -We can see this by running (from within the `02-consensus/uk_illumina/` directory): - -```console -$ ls results/consensus/qc_pass_climb_upload/uk/ -``` - -Also, the workflow modifies our original sample names in the FASTA file, by adding information about the steps used in the analysis. -For example: - -```console -$ head -n 1 results/consensus/qc_pass_climb_upload/uk/ERR5761182/ERR5761182.primertrimmed.consensus.fa -``` - -``` ->Consensus_ERR5761182.primertrimmed.consensus_threshold_0.75_quality_20 -``` - -What we want to do is clean these sample names, so that we end up with: - -``` ->ERR5761182 -``` - -We also want to make sure to combine all the samples into a single FASTA file. - -We can the command-line skills we acquired so far, in particular the use of the `cat` command to combine (or _concatenate_) the individual files and the `sed` command to replace text and clean our sample names. -Let's do this step by step. - -First, we can use the `*` _wildcard_ to combine all the FASTA files with the `cat` command: - -```console -$ cat results/consensus/qc_pass_climb_upload/uk/*/*.fa -``` - -Running this command will print all of the sequences on the screen! -To see what happened a little better, we could _pipe_ this command to `less` to browse up-and-down through the file: - -```console -$ cat results/consensus/qc_pass_climb_upload/uk/*/*.fa | less -``` - -We could also check that we now have all our samples combined, we could pass the results to `grep` and search for the word `>`, which in the FASTA format indicates the sequence name: - -```console -$ cat results/consensus/qc_pass_climb_upload/uk/*/*.fa | grep ">" | wc -l -``` - -This should give us 7 as the result (which makes sense, since we have 7 samples). - -We can now proceed with cleaning the names of the sequences, by using `sed`: - -```bash -cat results/consensus/qc_pass_climb_upload/uk/*/*.fa | sed 's/Consensus_//' | sed 's/.primertrimmed.consensus_threshold_0.75_quality_20//' > results/consensus/clean_sequences.fa -``` - -Notice how we use two rounds of text replacement, first we replace the word `Consensus_` by an empty string, and then again `.primertrimmed.consensus_threshold_0.75_quality_20` with nothing. - -We also make sure to redirect the result to a new file. - - -:::exercise - -In this exercise we will create a clean FASTA file for the samples collected in India. -These are found in the `02-consensus/india_nanopore` directory, so make sure to change to that directory first (on our training machines you can do: `cd ~/Course_Materials/02-consensus/india_nanopore`) - -If we look at one of the files - -```console -$ head -n 1 results/consensus/qc_pass_climb_upload/india/india_barcode01/india_barcode01.consensus.fasta -``` - -``` ->india_barcode01/ARTIC/medaka MN908947.3 -``` - -We want to clean the name of the sequences so that the result is: - -``` ->barcode01 -``` - -- Use the tools `cat` and `sed` to construct a command that generates a new file called `results/consensus/clean_sequences.fa` containing all the sequences with "clean" sequence names. - -
HintRemember the syntax for pattern replacement with `sed` is: `sed 's/replace this/with that/'`. Also remember that if you want to replace the character "/", you need to use the special _escape character_, for example: `sed 's/replace \/ slash//'`
- -
Answer - -The complete code to achieve the desired outcome is: - -```bash -cat results/consensus/qc_pass_climb_upload/india/*/*.fasta | sed 's/india_//' | sed 's/\/ARTIC\/medaka MN908947.3//' > results/consensus/clean_sequences.fa -``` - -Note that in order to replace the pattern `/ARTIC/medaka MN908947.3`, we needed to "_escape_" the `/` symbol by using `\/`. -This is because `/` alone is used by `sed` to separate different parts of the command. - -Look at the [section about pattern replacement](02-unix-sed.html) for a reminder of how the `sed` command works. - -
- -::: - - - -## Summary - -:::highlight - -**Key Points** - -- The main steps to generate SARS-CoV-2 consensus sequences are: filter high-quality reads, map reads to reference genome, trim PCR primers, variant calling, produce a consensus sequence and mask low-quality positions. -- _Nextflow_ is a software used for building workflows/pipelines involving multiple tools and data processing steps. Using established workflows helps with automation, reproducibility, consistency of results and reduces the chances of data processing errors. -- The `connor-lab/ncov2019-artic-nf` workflow implements the steps to generate SARS-CoV-2 consensus sequences from _Illumina_ or _Nanopore_ data. -- The command `nextflow run ncov2019-artic-nf` is used to run the workflow, using specific options depending on the data we have: - - `--illumina` for Illumina FASTQ files. - - `--medaka` for Nanopore basecalled FASTQ files. - - `--nanopolish` for Nanopore signal-level FAST5 files. -- The output of the workflow includes several quality metrics for each sample, including genome coverage and number of ambiguous 'N' bases. - - Sequences with > 90% coverage are recommended for downstream analysis and uploading to public databases such as GISAID. - -::: diff --git a/05-lineage_assignment.md b/05-lineage_assignment.md deleted file mode 100644 index d40e398..0000000 --- a/05-lineage_assignment.md +++ /dev/null @@ -1,429 +0,0 @@ ---- -pagetitle: "SARS-CoV-2 Genomics" ---- - -:::warning -**Update May 2022** - -Please note that this is an old version of this lesson for workshops that ran prior to May 2022. -We kept this in the materials as a reference, but would recommend that you look at the [new version of the lesson](05-lineage_analysis.html) instead. -::: - - -# Lineage Assignment and Variant Classification - -::: highlight - -**Questions** - -- What are the main variant annotation conventions for SARS-CoV-2? -- How can I assign sequences to lineages and classify them as variants of interest/concern? -- How can I visually explore the results of my variant analysis? - -**Learning Objectives** - -- Understand variant annotation conventions used by Gisaid, Pango, Nextstrain and WHO and how they relate to each other. -- Assign sequences to Pango lineages using `pangolin`. -- Interactively explore mutations in the assembled genomes and their phylogenetic context using _Nexstrain_'s tools. - -::: - - -:::note -This section has an accompanying slide deck. -::: - - -## SARS-CoV-2 Variants - -As viruses (or any other organism) evolve, random DNA changes occur in the population, for example due to replication errors. -Many of these changes are likely to be _neutral_, meaning that they do not change the characteristics of the virus in any significant way. -Neutral changes tend to _drift_ in the population, increasing or decreasing in frequency in a random way, but most likely end up disappearing due to their low starting frequency in the population. - -On the other hand, _advantageous mutations_ can occur, which lead to changes in the characteristics of the virus that are beneficial for its spread in the population (e.g. high transmissibility, resistance to immune response or vaccines, etc.). -Such beneficial mutations will therefore experience _positive selection_, potentially leading to their increase in frequency in the population as they spread to more and more hosts. - -Viruses carrying those advantageous mutations may, over time, aquire other advantageous mutations that further increase their fitness and, therefore, their frequency in the population. -One way to visualise this is by looking at a _phylogenetic tree_ showing the relationship between sequences (based on their similarity) and how groups of sequences change over time. - -![Example of global phylogeny from the [Nextstrain public server](https://nextstrain.org/ncov/gisaid/global). Colours show different Nextstrain clades. (Screenshot taken Feb 2022)](images/lineages_example.svg) - -In the figure above, which shows SARS-CoV-2 samples from across the world, we can see groups of similar sequences rapidly "expanding" at certain points in time. -Such groups of sequences, which share a collection of DNA changes, are referred to as SARS-CoV-2 _variants_ (see box below about the ambiguous meaning of this term). -In an effort to understand the spread of the virus and monitor situations of increased occurrence of such variants, several groups and institutions have developed a system to classify groups of SARS-CoV-2 sequences as _variants of interest_ and _variants of concern_. - -A full explanation and definitions of such variants is given in the [World Health Organisation (WHO) variants page](https://www.who.int/en/activities/tracking-SARS-CoV-2-variants) -The main classification systems currently in use are: - -- [GISAID clades](https://www.gisaid.org/references/statements-clarifications/clade-and-lineage-nomenclature-aids-in-genomic-epidemiology-of-active-hcov-19-viruses/) -- [Nextstrain clades](https://github.com/nextstrain/ncov/blob/master/defaults/clades.tsv) -- [Pango lineages](https://cov-lineages.org/lineage_list.html) -- [World Health Organisation (WHO) variants](https://www.who.int/en/activities/tracking-SARS-CoV-2-variants) - -In practice, there is a big overlap between these different nomenclature systems, with WHO variants having a direct match to Pango lineages and Nextstrain clades. -In fact, the different teams work together to try and harmonise the nomenclature used, and thus facilitate the interpretation of sequence analysis. - -The two most popular systems - _Nextclade_ and _Pangolin_ - have slightly different levels of resolution. -_Nextclade_'s [nomenclature system](https://nextstrain.org/blog/2021-01-06-updated-SARS-CoV-2-clade-naming) was developed to highlight diversity patterns at a larger scale, allowing discussions of SARS-CoV-2 diversity at a global level and over larger time scales. -On the other hand, _Pangolin_'s [nomenclature system](https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/) is more fine-grained, aiming to follow the dynamics of the pandemic as it unfolds. -The two systems are complementary to each other, and our analysis of SARS-CoV-2 sequences should include both tools. - -:::note -**What is a variant?** - -It is important to note that the term _variant_ can be sometimes ambiguous. - -The term "**SARS-CoV-2 variant**" usually refers to the [WHO definition of variants of concern/interest](https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/) (e.g. the Alpha, Delta and Omicron variants), which includes sequences containing a _collection of several nucleotide changes_ that characterise that group. -According to this definition, we have two variants in the example below (samples 1 & 2 are one variant and samples 3 & 4 another variant). - -![](images/variants_snps_indels.svg) - -However, in bioinformatic sequence analysis, a **sequence variant** refers to an individual change in the DNA sequence (a SNP or an insertion/deletion). -Using this definition, in the example above we have 5 variants: 3 SNPs and 2 indels. -In the [Consensus Sequence](04-artic_nextflow.html) section, we mentioned one of our workflow steps was "variant calling". -This was the definition of variant we were using: identifying individual SNPs and/or indels relative to the reference genome, from our sequencing data. -This is also reflected in the one of the common file formats used to store SNP/indel information, the [_VCF_ file](https://en.wikipedia.org/wiki/Variant_Call_Format), which means "Variant Call Format". - -Sometimes the term "mutation" is used to refer to SNP/indel variants. -For example see this [definition from the COG consortium](https://www.cogconsortium.uk/what-do-virologists-mean-by-mutation-variant-and-strain/). - -Because of this ambiguity, the terms "lineages" or "clades" are often used instead of "variants" when referring to groups of similar SARS-CoV-2 sequences, because they have a phylogenetic interpretation. -::: - -## Pangolin - -Pangolin - -The first tool we will use is called `pangolin` and uses the [Pango nomenclature system](https://cov-lineages.org/). -The main steps performed by this tool are: - -- Multiple sequence alignment of our samples against the _Wuhan-Hu-1_ reference genome, using the `minimap2` software. -- Assigning our sequences to lineages based on the current global phylogeny. Two methods/software are available: - - _pangoLEARN_ (default) uses a pre-trained machine learning model. - - _UShER_ uses a more classic parsimony-based method, but highly optimised for working with large numbers of sequences. -- Classifying our sequences according to the WHO nomenclature of variants of interest/concern using the `scorpio` software. - -Because `pangolin` uses a series of tools and models internally (which change over time as more sequences become available), it is useful to know which versions are being used in the current analysis. -To do this, we can run the command: - -```console -$ pangolin --all-versions -``` - -``` -pangolin: 3.1.17 -pangolearn: 2021-11-25 -constellations: v0.0.30 -scorpio: 0.3.15 -pango-designation used by pangoLEARN/Usher: v1.2.101 -pango-designation aliases: 1.2.112 -``` - -To run _Pangolin_ we can use the following command: - -```bash -pangolin --outdir directory/of/your/choice path/to/your/sequences.fasta -``` - -This will generate a "comma-separated values" (CSV) file called `lineage_report.csv` in the output directory that we specify. -The file will contain information about the lineage assignment analysis from _Pangolin_, including WHO variants of concern identified using the _Scorpio_ software. -A detailed explanation of the columns of this file is given in the [Pangolin documentation page](https://cov-lineages.org/resources/pangolin/output.html). - -One thing to note is that first _Pangolin_ will check if the sequence is identical to a representative sequence of a lineage (a representative sequence is one that is used to train the models that assign sequences to lineages). -If it is, then `pangoline` doesn't run the lineage assignment step with _pangoLearn_/_Usher_ nor the variant classification with _Scorpio_. -In these cases, the sequence will have the note "Assigned from designation hash" in the output CSV report file. - -:::exercise - -Go to the course materials directory `03-lineages` (on our training machines `cd ~/Course_Materials/03-lineages`). -There you will find a directory called `data`, which contains a file called `all_sequences.fa`, containing both UK and India samples that we processed in the [Consensus Assembly section](04-artic_nextflow.md). - -To run `pangolin` on our sequences with default options, we would use the following command: - -```bash -pangolin --outdir results/pangolin/ data/all_sequences.fa -``` - -- Create a directory for the output (look at the command above to see what this directory should be called). -- Look at _Pangolin_'s help documentation (`pangolin --help`) and look for the options you can use to modify the command above to: - - Use _UShER_ for placing sequences on the phylogeny (instead of the default, which uses _pangoLEARN_). - - Output the multiple sequence alignment generated during the analysis. - - Use 8 CPUs (or "threads") for parallel processing. -- Run the modified command on the terminal. (**Bonus:** save the command in a new shell script for reproducibility.) -- Using the file explorer , go to the output directory and open the file named `lineage_report.csv` (this will open with the spreadsheet software). - - Check whether there any variants of concern (these are found in the column called `scorpio_call`). - - Check whether there are any problematic samples. - -
Answer - -The first thing we should do is create the output directory for our results, and also a directory for our scripts: - -```bash -mkdir -p results/pangolin/ -mkdir -p scripts/ -``` - -We can look at the tool's help by using `pangolin --help`. -From that documentation we can see the following three options that would do what we want: - -- `--usher` uses the UShER algorithm to place our sequences in the global phylogeny. -- `--alignment` outputs the multiple sequence aligned from `minimap2` as a FASTA file. -- `--threads` allows us to use multiple CPUs for the computation. - -Therefore, the command to run our analysis is: - -```bash -pangolin --usher --alignment --outdir results/pangolin/ --threads 8 data/all_sequences.fa -``` - -Using a text editor like `nano`, we could include this command in a script and save it in the `scripts` folder with the filename `pangolin.sh` (or another informative name of your choice). - -We can open the `pangolin_report.csv` file on our spreadsheet software to look at the results. -From this table, we can make several observations: - -- In the column called "scorpio_call" we can see several samples that were classified as "Alpha" and "Delta" variants. -- Looking at the column "status", we can see that one of the samples failed the quality control applied by `pangolin`. This is the nanopore sample `barcode01`, which we had already identified as being slightly problematic, with a high percentage of ambiguous 'N' bases. -- The samples `barcode03`, `barcode26` and `barcode27` were not assigned a lineage. In this case, the reason is that the _Scorpio_ result disagreed with the lineage assigned by _UShER_. This could be because of missing data in key identifying mutations that led to a discrepancy between the tools. -- Ten of our samples were identical to the representative sequence of certain lineages, which is indicated by the "note" column that says "Assigned from designation hash". In these cases, `pangolin` does not run the Scorpio analysis. These are "AY" lineages, which is the Pangolin designation for sub-lineages of B.1.617.2, the Delta variant lineage (see the [lineage list page](https://cov-lineages.org/lineage_list.html)). We could later manually annotate these as also being Delta variant. -- Some samples have a "conflict" score > 0. In these cases, we can see that UShER found several possible lineages where the sequence could fit. We should take care in checking these results later in our analysis, perhaps complementing them with other tools such as _Nextclade_ or building a phylogeny from our samples. - -
- -::: - - -:::note -**Lineage assignment: _USHER_ or _pangoLEARN_?** - -`pangolin` can use two different methods to place our sequences in the background global phylogeny. -Both methods are extremely efficient and designed to work with thousands (or millions) of sequences and generally give comparable results. -_pangoLearn_ is faster than _UShER_, but the latter seems to perform better in the presence of missing data [reference needed]. -::: - - -## Nextclade - -Nextstrain - -Another system of clade assignment is provided by `nextclade`, which is part of the broader software ecosystem [_Nextstrain_](https://clades.nextstrain.org/). -This is a complementary analysis to what can be obtained with _Pangolin_, enriching our insights into the data. - -_Nextclade_ does similar steps as _Pangolin_, with some differences in the algorithms that are used: - -- Each sequence is aligned with the _Wuhan-Hu-1_ reference genome using a local alignment algorithm. -- Samples are placed in the global phylogeny using a distance-based metric (placing the sequence on the tree where it has the highest similarity with). -- Clade assignment is done based on the previous phylogeny placement step. - -You can find more details about _Nextclade_'s methods on [its documentation](https://docs.nextstrain.org/projects/nextclade/en/stable/user/algorithm/index.html). -_Nextclade_ also provides several quality control metrics, which are very useful to identify problematic samples. - -There are two main ways to use _Nextclade_: - -- Through a web interface, which is easier to use, but cannot be configured or automated. -- Through a command line interface, which gives us more control over the analysis and can be included in a shell script to automate our analysis. - - -### Web Interface - -_Nextclade_ offers a convenient interface, which can be used to run the default analysis on a set of sequences: - -- Go to [nextclade.org](https://clades.nextstrain.org/) -- Select **SARS-CoV-2** and click **Next** -- Click **Select a file** to browse your computer and upload the FASTA file with the cleaned consensus sequences (`data/all_sequences.fa`). -- Click **Run** - -Nextclade will show a progress of its analysis at the top of the page, and the results of several quality control metrics in the main panel (see Figure). - -![Overview of the Nextclade web interface.](images/nextclade_overview.svg) - - -:::exercise - -Now that we have loaded our consensus sequences to [nextclade.org](https://clades.nextstrain.org/), answer the following questions: - -1. Are there any samples that were classified as "bad" quality? If so, what is the main reason for their low quality? How does this relate with your previous look at the sequencing coverage in these datasets?
HintUse the data filters to help you answer this question.
-1. Can you identify any sequences classified as a WHO variant of concern? Does this agree with the previous analysis from _Pangolin_?
HintAlthough this information is available in the main panel, it might be easier to switch to the Phylogeny view, using the button on the top-right.
-1. The image below shows the positions of the mutations that characterise the Delta variant. On the Nextclade browser, select "Protein S" and in the right panel confirm that those mutations occur in the sequences classified as Delta.
HintTo make this exploration easier, try filtering your samples for clade "21".
- -
- - - -

- Mutations in the Delta variant Spike protein. The position of each mutation relative to the Wuhan-Hu-1 reference genome is shown. For example: "T19R" means that a T (Threonine) changed to a R (Arginine) in position 19 of the protein; "Δ157-158" means that there is a deletion in position 157-158 of the protein. Click the image to open a larger size. (source: [Wikipedia](https://upload.wikimedia.org/wikipedia/commons/thumb/4/4f/SARS-CoV-2_Delta_variant.svg/6878px-SARS-CoV-2_Delta_variant.svg.png)) -

-
- - - -
Answer - -**A1.** - -To look at how many samples are classified as different quality categories, we can use the filter button on the top-right - -. -We can tick/untick based on the way _Nextclade_ classified the sequences in quality categories. -By selecting only those samples that have "Bad Quality", we can see that nearly all of them are from the nanopore data. -One of the main errors is due to too much missing data (ambiguous bases, "N"). -We can see these as grey boxes in the variant panel on the right. - -**A2.** - -In the column **Clade** we can see which clades each sequence was assigned to, following _Nextclade_'s own nomenclature. -However, we also get information about the WHO variants of concern, which use the greek alphabet nomenclature. -We can see "Alpha" and "Delta" variants in the data. -This is perhaps even better visualised in the phylogeny view, by pressing the - -phylogeny button on the top-right. - -From there, we can see several variants classifed as "Delta" and "Alpha". -Looking at some specific ones, we can see they are the same samples that _Pangolin_ identified as variants of concern. -For example, sample ERR5921254 is classified as "21J (Delta)" by _Nextclade_ and as "Delta (AY.4-like)" by _Pangolin/Scorpio_. - -There were some samples that _Pangolin_ had not assigned a lineage because there was a conflict between _UShER_ and _Scorpio_ (samples `barcode03`, `barcode26` and `barcode27`). -_Nexclade_ does classify these sequences as B.1.1.7 (Alpha), which seems to agree with the _UShER_ assignment. -However, these samples are highlighted as having too much missing data, which may be the reason why _Scorpio_ did not classify them as a variant of concern. - - -**A3.** - -Going back to the main panel, we can use the - -filter button to show only sequences from clade "21" (this includes "21J", "21I" and "21A", which are all sub-clades of _Delta_). - -Comparing the mutation panel on the right with the mutations available from _Wikipedia_, we can see that they are all present in our sequences. -Some other mutations seem to be more specific to the sub-clades. -For example "A222V" seems to be specific to clade "21I". - -
- -::: - - -:::note -When using the _Nextclade_ web tools, the data does not leave your computer, so privacy concerns are not an issue. -::: - - -### Command-line Interface - -To use the command line interface of _Nextclade_ we need to first download "background" data, which will be used to classify and integrate our samples in the global phylogeny. - -We can check the available data for SARS-CoV-2 by running: - -```console -$ nextclade dataset list --name sars-cov-2 -``` - -The output of this command will inform us that we can run the following command to obtain the current (most up-to-date) version of the background dataset: - -```console -$ nextclade dataset get --name sars-cov-2 --output-dir resources/nextclade_dataset -``` - -This command downloads data used by `nextclade` to compare our sequences with a pre-computed tree of publicly available sequences (so-called [reference tree](https://docs.nextstrain.org/projects/nextclade/en/stable/user/terminology.html#reference-tree-concept)). - -Now that we have downloaded our data, we are ready to run our `nextclade` analysis: - -```bash -nextclade run \ - --in-order \ - --input-fasta data/all_sequences.fa \ - --input-dataset resources/nextclade_dataset/ \ - --include-reference \ - --output-dir results/nextclade \ - --output-basename uk_india \ - --output-json results/nextclade/uk_india.json \ - --output-tsv results/nextclade/uk_india.tsv \ - --output-tree results/nextclade/uk_india.auspice.json \ - --min-length 27000 -``` - -The result from this analysis will generate several files in the output folder `results/nextclade`: - -- `uk_india.aligned.fasta` - aligned nucleotide sequences. -- `uk_india.gene.*.fasta` - aligned peptides. -- `uk_india.tsv` - analysis results, including clade assignment and several quality-control metrics. - - `uk_india.json` - analysis results in a machine-readable format. -- `uk_india.auspice.json` - phylogenetic tree in a format that is compatible with [_Auspice_](https://auspice.us/) for interactive visualisation. -- `uk_india.insertions.csv` - nexclade removes insertions so that they no longer appear in the aligned peptide sequences. This CSV file contains the position and sequence of all those insertions. -- `uk_india.errors.csv` - CSV file with of errors and warnings for each sequence. - -These files are equivalent to what the web-tool we used in the previous section gave us as the results. - -We can visualise the result of this analysis by uploading the `*.auspice.json` file to [auspice.us](https://auspice.us/). -In addition, we can upload a metadata table, which can be helpful to further explore our data. - -:::exercise - -Go to [auspice.us](https://auspice.us/) and drag-and-drop the `uk_india.auspice.json` file onto the browser window. -This should open a phylogeny panel, similar to what we previously obtained with the Web interface. - -Drag-and-drop our sample metadata file (stored in `sample_info_nextclade.csv`) into the browser window. -This will add this metadata to the interactive view, including a new map panel showing the location of our samples. - -Take some time to explore your data using this tool. - -::: - -:::note -Nextclade adds the suffix "_new" to our sample names, so we created a new metadata file where we changed the name of our samples to include "_new" in their name as well. -This is needed when adding metadata to our results on [auspice.us](https://auspice.us/). -Apart from that, the file is exactly the same as the `sample_info.csv` file we have been using so far. -::: - -:::exercise - -_Nextclade_ also outputs the protein sequences for each of the SARS-CoV-2 genes. -We can visualise these with the program AliView. - -Nextstrain - -- Open AliView (this program's icon is shown on the right) -- Go to "File > Open File" and navigate to "Course_Materials > 03-lineages > results > nextclade" to open the file "uk_india.gene.S.fasta" -- Can you see the mutations characteristic of the Delta variant? (see image below) - -
- - - -

- Mutations in the Delta variant Spike protein. Click image to open larger size. (source: [Wikipedia](https://upload.wikimedia.org/wikipedia/commons/thumb/4/4f/SARS-CoV-2_Delta_variant.svg/6878px-SARS-CoV-2_Delta_variant.svg.png)) -

-
- -
Answer - -AliView allows us to interactively visualise a FASTA file with protein or DNA alignments. - -To help us explore these data, we can right-click the first sequence (Wuhan-Hu-1 reference) and choose "Set this sequence as template when Highlighting difference". -This will make it easier to see aminoacid differences in our samples. - -We can scroll to the right to see that several samples contain the mutation at position 19 changing a T to an R, at position 157 there is a deletion and a change to a G, at position 452 a change from L to R, etc. - -If we look at these samples' names, they match the classification obtained with Nextclade and Pangolin as being the Delta variant. - -
- -::: - - - - -## Summary - -:::highlight - -**Key Points** - -- Groups of similar SARS-CoV-2 sequences are classified into lineages or clades by different groups. The main nomenclature systems in use are _Nextstrain_, _Pangolin_ and _GISAID_. -- In addition, the World Health Organisation (WHO) classifies some forms of SARS-CoV-2 as _variants of concern_ or _variants of interest_. These are forms of the virus that have been determined to have a significant public health impact. -- The software `pangolin` can be used to assign consensus sequences to lineages and WHO variants. -- _Nextclade_ can also be used to assign sequences to clades and WHO variant classification. _Nextclade_ can be run from a web interface and the command line (`nextclade`). - -::: - diff --git a/100-homework.md b/100-homework.md deleted file mode 100644 index fc0371b..0000000 --- a/100-homework.md +++ /dev/null @@ -1,680 +0,0 @@ ---- -pagetitle: "SARS-CoV-2 Genomics" ---- - -# Extra Exercises - -This set of exercises are provided as extra practice material that can be done outside of the course. -It uses a larger set of data, which is more representative of what you may encounter in your own work. - -All the exercises run from the data directory `homework`, so make sure you change into that directory. -On our training machines this is `cd ~/Course_Materials/homework/`. - -There are two sets of data, from two countries, one sequenced using Illumina and the other Nanopore platforms. -Most of these exercises can be done on either dataset. -We advise that you pick one of the datasets (for example, the one that uses data that you are most likely to work with) and go through all the exercises. -After you complete them, you can start again with the other dataset. - - -## Unix {.tabset} - -**This exercise uses the skills covered in [Introduction to Unix](02a-unix_intro.html).** - -1. Open a terminal and change into either the `homework/switzerland/` or `homework/southafrica/` directory. -2. Create a new directory called `results`. -3. Use command line tools to find how many sequencing read files are available (these are found within the `data/fastq_pass` or `data/reads/` directories). - -### South Africa - -:::box -
Answer - -To check how many samples we have, we can combine the `ls` (list files) and `wc` (word count) commands - -For the South Africa samples we have paired-end Illumina data (i.e. two files per sample). -For this reason, we list all files that end with "_1.fastq.gz" using the `*` wildcard. -This way we ensure to only count each sample once: - -```console -$ ls data/reads/*_1.fastq.gz | wc -l -``` -``` -48 -``` - -We pipe the output of `ls` to the `wc` command using the option `-l` to count the number of lines coming out of the `ls` command. - -
-::: - -### Switzerland - -:::box -
Answer - -For the Switzerland samples we have Nanopore data. -In this case, each sample's data is within its own directory, so all we have to do is count how many directories there are in `data/fastq_pass`: - -```console -$ ls data/fastq_pass/ | wc -l -``` -``` -59 -``` - -We pipe the output of `ls` to the `wc` command using the option `-l` to count the number of lines coming out of the `ls` command. - -
-::: - - -## Metadata {.tabset} - -**This exercise uses the skills covered in [SARS-CoV-2 Genomic Surveillance](01-intro.html).** - -Open the `homework` folder using the file browser . - -1. Looking inside the each country's folder, open the `README.txt` file to understand what kind of sequencing platform was used for these samples. -1. Open the metadata sheet (`sample_info.csv`) for one of the countries and look at the dates when these samples were collected. -1. Visit [outbreak.info](https://outbreak.info/location-reports) and look at the location report for the country you chose. What variants might you expect to be find in these data? - -### South Africa - -:::box -
Answer - -From the README we can see that using paired-end Illumina sequencing. -This will need to be considered when we run our consensus assembly workflow. - -Double-clicking the `sample_info.csv` file opens it with our spreadsheet software (on our training machines this is LibreOffice, but Excel would also open CSV files). -The second column contains the sample collection dates, and we can see these samples are from Nov 2021 to Jan 2022. - -We visit [outbreak.info](https://outbreak.info/location-reports) and search for "South Africa". -If we scroll down to the section "Tracked lineages over time in South Africa" we can see that in late 2021 and the start of 2022 we have mostly the "Omicron" SARS-CoV-2 variant circulating in the population. -This suggests that our samples may mostly be this variant. - -
-::: - -### Switzerland - -:::box -
Answer - -From the README we can see that these samples were sequenced using a Nanopore MinION platform. -The raw Nanopore signal data was already processed to generate FASTQ files using Guppy. -This will need to be considered when we run our consensus assembly workflow. - -Double-clicking the `sample_info.csv` file opens it with our spreadsheet software (on our training machines this is LibreOffice, but Excel would also open CSV files). -The second column contains the sample collection dates, and we can see these samples are from Nov/Dec 2021 to Jan 2022. - -We visit [outbreak.info](https://outbreak.info/location-reports) and search for "Switzerland". -If we scroll down to the section "Tracked lineages over time in Switzerland" we can see that around that period of 2021-2022 we had both Delta and Omicron variants circulating in the population, so we may expect to see these in our samples. - -
-::: - - -## FASTQ Quality Control {.tabset} - -**This exercise uses the skills covered in [Introduction to NGS](03-intro_ngs.html).** - -:::warning -This exercise can only be done on the South Africa samples. -::: - -The South Africa samples were sequenced on an Illumina platform. -We will run a basic quality control of these reads using FastQC and compile the results using MultiQC. - -1. Runs FastQC on all the samples. Output the results to a directory called `results/fastqc/`. Make sure to create the directory first. -2. Compile the results of FastQC using MultiQC and output its results in a directory called `results/multiqc/`. -3. Analyse the reports and make a note of any samples that you worry about in terms of producing a high-quality assembly. - -:::box -
Answer - -To check the data quality of our reads, we can use the following commands. -Make sure these commands are run from within the `homework/southafrica` directory. - -```bash -# move to the directory -cd ~/Course_Materials/homework/southafrica/ - -# make directory -mkdir -p results/fastqc -mkdir -p results/multiqc - -# run FastQC on all the files -# using 8 threads in parallel since we have 8 CPUs available -fastqc -t 8 --outdir results/fastqc/ data/reads/*.fastq.gz - -# run MultiQC on the output of FastQC -multiqc --outdir results/multiqc/ results/fastqc/ -``` - -This script starts by having some code to create the necessary output directories. -The `-p` option ensures that we don't get an error in case the directory already exists. - -We then run `fastqc`, being careful to specify that we have 8 CPUs (with the `-t` option) to process the data in parallel. -We also use the `*` _wildcard_ to pattern-match all the files ending with the file extension ".fastq.gz", so that FastQC will automatically process all the files. - -We then use the _output_ directory of the `fastqc` step as the _input_ for `multiqc`. -This is just like a mini bioinformatics workflow or our own: outputs of one tool feeding into the next tool! - -After looking at the quality report from `multiqc`, we can notice that: - -- Some samples have a very low number of sequences. For example, sample SRR17461700 only has 46717 reads (you can open the individual FastQC report for this sample to see the exact number). -- Generally the read 2 files (file names ending `_2.fastq.gz`) have lower quality than the read 1 files. This is often seen in Illumina data. -- In the "Adapter Content" section of the MultiQC report we can see that some of the reads contain some of the Illumina sequencing adapter within them. - -These last two points are generally solved with quality filtering and adapter removal, which is part of the consensus workflow we will use. -However, it will still affect the quality of the assembly because by filtering low-quality reads we will, effectively be loosing data. - -We should keep an eye on the samples with very low read numbers and check if they pass the QC thresholds from our consensus assembly workflow. - -
-::: - - -## Consensus Assembly {.tabset} - -**This exercise uses the skills covered in [Consensus Assembly](04-consensus.html).** - -We will now produce a consensus assembly from our sequencing data using the `nf-core/viralrecon` Nextflow pipeline. -Consult the [full documentation](https://nf-co.re/viralrecon/2.4.1) of this pipeline if you need to remind yourself what the different available options are. - -1. Write a command to run our samples through the pipeline. (Note: this may take a couple of hours to complete.) - - The input CSV file with sample names and FASTQ file locations has already been prepared and is called `samplesheet.csv`. - - Output the results to a directory called `results/viralrecon/`. - - **Bonus:** Save the command used for your analysis in a script (create a `scripts/` folder and save it in there with a name of your choice). -1. How long did the pipeline take to run, and did it give any warnings? -1. Open the _MultiQC_ report file and explore the following questions: - - What can you say about the quality of the consensus sequences in terms of the fraction of the genome that is covered? What do you think are reasons why some samples may be better than others? - - Do you detect any issues with amplicon dropouts? Does this affect all samples or some samples specifically? - - What types of mutation variants do you get, and are most of them silent or missence mutations? - -
Hint -Some of the files you may want to use are: - -- The _MultiQC_ report: - - Illumina pipeline: `results/viralrecon/multiqc_report.html` - - Nanopore medaka pipeline: `results/viralrecon/medaka/multiqc_report.html` -- The mutation variants file: - - Illumina pipeline: `results/viralrecon/variants/ivar/variants_long_table.csv` - - Nanopore medaka pipeline: `results/viralrecon/medaka/variants_long_table.csv` -- Open some of the BAM alignment files in IGV: - - Illumina pipeline: `variants/bowtie2/*.ivar_trim.sorted.bam` - - Nanopore medaka pipeline: `results/viralrecon/medaka/*.primertrimmed.rg.sorted.bam` -- The primer BED files can also be imported to IGV: - - These are found in `resources/primers/artic_version3_pool1.bed` and `resources/primers/artic_version3_pool2.bed`. - -
- - -### South Africa - -:::box -
Answer - -**Question 1** - -In order to run this pipeline, we need the following pieces of information: - -- The platform used for sequencing (illumina or nanopore). This information is given to us in the `README.txt` file. -- The primer set used and its version. This is also detailed in the `README.txt` file. -- For Illumina data we also need an input CSV file with 3 columns: name of the sample, location of FASTQ read 1 file, location of FASTQ read 2 file. We have this information in the file `samplesheet.csv`. - -Our samples come from Illumina sequencing and used the ARTIC primer scheme version 3. -Therefore, our command is: - -```bash -nextflow run nf-core/viralrecon \ - --input samplesheet.csv \ - --outdir results/viralrecon \ - --protocol amplicon \ - --genome 'MN908947.3' \ - --primer_set artic \ - --primer_set_version 3 \ - --skip_assembly \ - --platform illumina \ - -profile singularity -``` - -This step may take some hours to complete on our training machines, but we can see its progress printed on the console while it runs. -Once it completes we should get a message printed on our screen, similar to: - -``` --[nf-core/viralrecon] 46/48 samples passed Bowtie2 1000 mapped read threshold: - 262615: ZA45 - 225773: ZA32 - 707111: ZA11 - 292838: ZA35 - 100358: ZA46 - 2570: ZA43 - ..see pipeline reports for full list - --[nf-core/viralrecon] 2 samples skipped since they failed Bowtie2 1000 mapped read threshold: - 176: ZA12 - 201: ZA09 - --[nf-core/viralrecon] Pipeline completed successfully- -Waiting files transfer to complete (1 files) -Completed at: 04-May-2022 13:07:20 -Duration : 26m 56s -CPU hours : 15.3 -Succeeded : 1'901 -``` - -This tells us how long the pipeline took to run (in our example it was 26 minutes, but yours might be longer than this). -We can also see a warning indicating that 2 samples were skipped due to an insufficient number of reads needed for downstream analysis. -We will investigate this in the next question. - -**Question 2** - -After our pipeline completes, we can open the _MultiQC_ report generated by the pipeline and located in `results/viralrecon/multiqc/multiqc_report.html`. - -One of the first things we can look at is the summary table "Variant Calling Metrics" at the top of the report. -We can start by looking at the two samples that were skipped in the analysis - ZA09 and ZA12 - and see that while they had a moderate number of starting reads (>300 thousand), only around 200 reads were successfully mapped to the SARS-CoV-2 reference genome by `bowtie2`. -Further looking at the table, we can see that this is because only a small percentage of reads - less than 2% - were non-host reads (i.e. non-human reads), which is determined by the _Kraken 2_ software. - -We can further explore this table to see that several other samples have very low % of non-human reads, suggesting several of these samples suffered from contamination of human DNA during preparation in the lab. -We can see this in more detail in the section "PREPROCESS: Kraken 2". - -If we sort the table by the column "% Coverage > 10x", we can see that only 37 of the samples have this depth of coverage in more than 80% of the genome. - -The "Amplicon coverage heatmap" also shows how several samples have low depth of coverage across most of the amplicons. -There are also some amplicons that seem to have lower depth of coverage across multiple samples. - -Finally, looking at the "Variants by Functional Class" section, we can disply the plot by as a percentage, which reveals most mutations are missense (i.e. they result in an amino acid change). -Some samples have very low number of mutations (ZA10, ZA27, ZA30 and ZA31), making it difficult to draw conclusions from them. - -
-::: - - -### Switzerland - -:::box -
Answer - -**Question 1** - -In order to run this pipeline, we need the following pieces of information: - -- The platform used for sequencing (illumina or nanopore). This information is given to us in the `README.txt` file. -- The primer set used and its version. This is also detailed in the `README.txt` file. -- For Nanopore data we also need an input CSV file with 2 columns: name of the sample, and the sample barcode number. We have this information in the file `samplesheet.csv`. -- For basecalled data we also need to specify a model for the `medaka` software, in the format `{pore}_{device}_{caller variant}_{caller version}` (see [documentation here](https://github.com/nanoporetech/medaka#models)). We are given this information in the `README.txt` file: R9.4.1 flowcell, MinION sequencer, high accuracy mode, Guppy version 3.6.0. - -Because we are starting with basecalled data (FASTQ files), we will use the pipeline with the `medaka` variant caller. -Therefore, our command is: - -```bash -nextflow run nf-core/viralrecon \ - --input samplesheet.csv \ - --outdir results/viralrecon \ - --protocol amplicon \ - --genome 'MN908947.3' \ - --primer_set artic \ - --primer_set_version 3 \ - --skip_assembly \ - --platform nanopore \ - --artic_minion_caller medaka \ - --artic_minion_medaka_model r941_min_high_g360 \ - --fastq_dir fastq_pass/ \ - -profile singularity -``` - -This step may take some hours to complete on our training machines, but we can see its progress printed on the console while it runs. -Once it completes we should get a message printed on our screen, similar to: - -``` --[nf-core/viralrecon] Pipeline completed successfully- -Completed at: 04-May-2022 13:03:23 -Duration : 20m 2s -CPU hours : 35.2 -Succeeded : 1'312 -``` - -This tells us how long the pipeline took to run (in our example it was 20 minutes, but yours might be longer than this). -We didn't get any warnings, indicating that all samples were processed successfully. - -**Question 2** - -After our pipeline completes, we can open the _MultiQC_ report generated by the pipeline and located in `results/viralrecon/multiqc/medaka/multiqc_report.html`. - -One of the first things we can look at is the summary table "Variant Calling Metrics" at the top of the report. -We can see that most samples had very good genome coverage at a depth > 10%. -If we sort the table by the column "% Coverage > 10x", we can see that the lowest sample had 75% of the genome covered at this depth, which is still a good fraction. - -However, we can also note that two of the samples with the lowest genome coverage (CH07 and CH59) were not assigned a lineage by _Pangolin_, which suggests they did not pass the minimum thresholds for that program (we will explore more about lineage assignment in the next exercise). -This may be due to a low number of reads, requiring re-sequencing these samples to achieve good genome coverage. - -The "Amplicon coverage heatmap" shows an interesting pattern of amplicon dropout, with largely two groups of samples showing different patterns of amplicon depth of coverage. -For example, amplicon "nCoV-2019_64" has extremely low depth of coverage in several samples such as CH20, CH43, CH24, CH40, CH44 amongst many others. -Further investigation of this table reveals that these samples were classified as "Delta" by Nextclade. -The other group of samples was classfied as "Omicron", which may suggest that mutations in each SARS-CoV-2 variant are causing issues amplifying some of the PCR amplicons using the ARTIC v3 protocol. - -A tip to explore these samples is to use the "highlight" button on MultiQC (on the right toolbar) and highlight certain samples of interest. - -Further investigation of the amplicon dropout could be done by looking at the mutation file generated by the pipeline: - -- Open the mutation variant file in a spreadhseet program - `results/viralrecon/medaka/variants_long_table.csv`. -- Sort the table by position ("POS" column). -- Check the location of the primers for amplicon "nCoV-2019_64", for example from the [BED file available online](https://github.com/artic-network/artic-ncov2019/blob/master/primer_schemes/nCoV-2019/V3/nCoV-2019.scheme.bed). We can see that these primers are in positions 19204-19232 and 19591-19616. -- Looking at the sorted table of variants we can see many samples have a SNP in position 19220, which overlaps the left primer. This could be a reason for this amplicon dropout. - -Finally, looking at the "Variants by Functional Class" section, we can disply the plot by as a percentage, which reveals most mutations are missense (i.e. they result in an amino acid change). -Three samples have 1 nonsense mutation each (i.e. causing a new stop codon), which tend to be quite disruptive. -Looking back at our mutation table, we can look at mutations with EFFECT column = "stop_gained" and will see that two of the samples share the same mutation (position 28209). -This could be an indication that this is a true mutation rather than an error (since the chance of the same error occurring twice independently is low). -However, this should be confirmed with new experiments as sequencing errors can sometimes be biased. - -
-::: - - -## Cleaning FASTA files {.tabset} - -**This exercise uses the skills covered in [Consensus Assembly - Cleaning FASTA Files](04-consensus.html#Cleaning_FASTA_Files).** - -The `nf-core/viralrecon` pipeline generates a separate consensus sequence FASTA file for sample. -However, for downstream analysis (variant analysis and phylogeny), it is more useful to have all the consensus sequecences combined into a single file. - -1. Using command-line tools, combine all the FASTA files into a single file. Save the resulting file as `results/clean_consensus_sequences.fa`. (Note: see the hint below if you need a reminder of where these files are and what commands you can use.) -2. (Bonus - advanced) Using the `sed` command line tool, clean the names of the sequences such that only the original sample name is present in the final FASTA file. For example: - - An illumina sample named as `>ZA01 MN908947.3` should become `>ZA01` - - A nanopore sample named as `>CH01/ARTIC/medaka MN908947.3` should become `>CH01` - -
Hint -The FASTA files can be found in different directories, depending on whether you are processing the Nanopore or Illumina data: - -- Illumina: fasta files are found in `results/viralrecon/variants/ivar/consensus/bcftools/` with `.fa` extension. -- Nanopore: fasta files are found in `results/viralrecon/medaka/` with `.fasta` extension. - -Remember that all of the information about output files is [extensively detailed in the pipeline documentation](https://nf-co.re/viralrecon/2.4.1/output). - -Some of the command-line tricks you can use to combine the files in this exercise include: - -- Using the `cat` command to combine text files -- Using the `*` wildcard to simultaneously select files matching a particular pattern in their name -- Using the `>` redirect operator to send the output of a command to a file (instead of printing it on the console). - -
- -### South Africa - -:::box -
Answer - -**Question 1** - -For the South Africa samples (Illumina pipeline) the FASTA files can be found in the folder `results/viralrecon/variants/ivar/consensus/bcftools/`. - -We can list all the files ending with `.fa` extension found in this folder: - -```console -$ ls results/viralrecon/variants/ivar/consensus/bcftools/*.fa -``` - -``` -results/viralrecon/variants/ivar/consensus/bcftools/ZA01.consensus.fa -results/viralrecon/variants/ivar/consensus/bcftools/ZA02.consensus.fa -results/viralrecon/variants/ivar/consensus/bcftools/ZA03.consensus.fa -results/viralrecon/variants/ivar/consensus/bcftools/ZA04.consensus.fa -results/viralrecon/variants/ivar/consensus/bcftools/ZA05.consensus.fa -[... more output not shown...] -``` - -We can also count how many files we have and confirm this matches the number of samples in this dataset: - -```console -$ ls results/viralrecon/variants/ivar/consensus/bcftools/*.fa | wc -l -``` - -``` -45 -``` - -Finally, we can combine all the files using the `cat` command and redirecting the output with `>` to a new file: - -```console -$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa > results/clean_consensus_sequences.fa -``` - -We can confirm that all the samples are present in this file: - -```console -$ cat results/clean_consensus_sequences.fa | grep ">" | wc -l -``` - -``` -45 -``` - -- We print the content of the file with `cat`. -- We find lines of the file that contain the `>` character (the sequence names in FASTA files always start with this character). -- We use the `wc -l` to count the number of lines in the output. - - -**Question 2** - -Looking at the content of one of the files using `head`, we can see how the sequences are named: - -```console -$ head -n 3 results/viralrecon/variants/ivar/consensus/bcftools/ZA01.consensus.fa -``` - -``` ->ZA01 MN908947.3 -NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNAGATCT -GTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTGCACT -``` - -We can remove the " MN908947.3" text from the sample names using the `sed` text-replacement function. -This would be our modified command to combine all samples and remove that text: - -```console -$ cat results/viralrecon/medaka/*.fasta | sed 's/ MN908947.3//' > results/clean_consensus_sequences.fa -``` - -Some notes about the `sed` command: - -- The syntax to substitute text is `sed 's/old text/new text/'`. -- If we want to _remove_ text we can leave the new text field empty: `sed 's/old text//'` -- Notice that we want to also substitute the _space_ before "MN908947.3", so we include that in our pattern to substitute. - -
-::: - -### Switzerland - -:::box -
Answer - -**Question 1** - -For the Switzerland samples (Nanopore `medaka` pipeline) the FASTA files can be found in the folder `results/viralrecon/medaka/`. - -We can list all the files ending with `.fasta` extension found in this folder: - -```console -$ ls results/viralrecon/medaka/*.fasta -``` - -``` -results/viralrecon/medaka/CH01.consensus.fasta -results/viralrecon/medaka/CH03.consensus.fasta -results/viralrecon/medaka/CH05.consensus.fasta -results/viralrecon/medaka/CH06.consensus.fasta -results/viralrecon/medaka/CH07.consensus.fasta -[... more output not shown...] -``` - -We can also count how many files we have and confirm this matches the number of samples in this dataset: - -```console -$ ls results/viralrecon/medaka/*.fasta | wc -l -``` - -``` -59 -``` - -Finally, we can combine all the files using the `cat` command and redirecting the output with `>` to a new file: - -```console -$ cat results/viralrecon/medaka/*.fasta > results/clean_consensus_sequences.fa -``` - -We can confirm that all the samples are present in this file: - -```console -$ cat results/clean_consensus_sequences.fa | grep ">" | wc -l -``` - -``` -59 -``` - -- We print the content of the file with `cat`. -- We find lines of the file that contain the `>` character (the sequence names in FASTA files always start with this character). -- We use the `wc -l` to count the number of lines in the output. - - -**Question 2** - -Looking at the content of one of the files using `head`, we can see how the sequences are named: - -```console -$ head -n 3 results/viralrecon/medaka/CH01.consensus.fasta -``` - -``` ->CH01/ARTIC/medaka MN908947.3 -NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNAGATCT -GTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTGCACT -``` - -We can remove the "/ARTIC/medaka MN908947.3" text from the sample names using the `sed` text-replacement function. -This would be our modified command to combine all samples and remove that text: - -``` -cat results/viralrecon/medaka/*.fasta | sed 's/\/ARTIC\/medaka MN908947.3//' > results/clean_consensus_sequences.fa -``` - -Some notes about the `sed` command: - -- The syntax to substitute text is `sed 's/old text/new text/'. -- If we want to _remove_ text we can leave the new text field empty: `sed 's/old text//'` -- Because `/` is used to separate the different parts of the `sed` command, when we want to replace the actual "/" letter we need to prefix it `\` (this is called "escaping"). Therefore, we use "\/ARTIC\/medaka MN908947.3" as the text to replace. - -
-::: - -## Lineages & Variants {.tabset} - -We will now investigate in more detail the SARS-CoV-2 lineages/clades and variants of concern in our samples. - -1. Looking at the _MultiQC_ report, answer the following: - - How many different variants of concern were detected in your samples? - - Looking at the _Pangolin_ analysis section, where there any problematic samples and, if so, what was the reason? -1. Run a full _Nextclade_ analysis using the web application: https://clades.nextstrain.org/. Upload the clean FASTA file you generated in the previous exercise. - - What quality issues do you detect using this tool? Does it agree with your previous conclusions? - -### South Africa - -:::box -
Answer - -**Question 1** - -There are two main tools used to assign consensus sequences to lineages/clades and classify them as variants of concern: _Nextclade_ and _Pangolin_. - -We can look at the first table of the _MultiQC_ report and look at the column "Nextclade clade" to see which SARS-CoV-2 variants were detected using this tool. -We can see that all samples were classified as Omicron, except one sample, which was not classified as a known variant of concern: ZA10 was assigned to _Nextclade_ clade 20C. - -Looking at the section of the _MultiQC_ report called "VARIANTS: Pangolin" we can see what variants were detected with this tool (which uses `scorpio` to do the classification of variants of concern). -Again, the analysis for this tool agrees with the _Nextclade_ analysis, in that all samples were classified as Omicron. - -However, _Pangolin_ seems to have a higher number of samples that were not classified, as they did not pass the minimum genome coverage threshold required for its analysis. -These are highlighted as "Fail" in the column "QC Status". -In the column "Note" we can see the reason was a high proportion of missing data ("N"). - - -**Question 2** - -We go to https://clades.nextstrain.org/ and: - -- Click **Select a file** to browse your computer and upload the FASTA file with the cleaned consensus sequences (`results/clean_consensus_sequences.fa`). -- Click **Run** - -We are then presented with the _Nextclade_ interactive results panel, similar to the one shown in this picture: - -![](images/nextclade_overview.svg) - -We can sort the table by the column "QC" (quality control) to look at the most problematic samples. -We can see the main reason for low QC score is a high proportion of missing data, which is in agreement with the information we previously looked at from _Pangolin_. - -However, other issues are also present in some of the samples: - -- The occurrence of private mutations, which are mutations that are not present in any other sample and so could be due to sequencing errors. -- the occurrence of frame shifts, which are insertion/deletion mutations that change the translation frame of a protein. Such a mutation would be very disruptive and likely lethal, so it may also be caused by sequencing errors. - -
- -### Switzerland - -:::box -
Answer - -**Question 1** - -There are two main tools used to assign consensus sequences to lineages/clades and classify them as variants of concern: _Nextclade_ and _Pangolin_. - -We can look at the first table of the _MultiQC_ report and look at the column "Nextclade clade" to see which SARS-CoV-2 variants were detected using this tool. -We can see that samples were either classified as Omicron or Delta. - -Looking at the section of the _MultiQC_ report called "Pangolin" we can see what variants were detected with this tool (which uses `scorpio` to do the classification of variants of concern): - -- The analysis for this tool agrees with the _Nextclade_ analysis, in that samples were classified either as Omicron or Delta. -- However, some samples (CH22, CH47, CH58 and CH62) were classified as "Probable Omicron", suggesting that there were not enough known SNPs from these SARS-CoV-2 variants present in the consensus sequences. -This is indicated by the column "S support", which has a lower score for these samples. -- Two samples (CH07 and CH59) were not classified. These are highlighted as "Fail" in the column "QC Status". In the column "Note" we can see the reason was a high proportion of missing data ("N"). - - -**Question 2** - -We go to https://clades.nextstrain.org/ and: - -- Click **Select a file** to browse your computer and upload the FASTA file with the cleaned consensus sequences (`results/clean_consensus_sequences.fa`). -- Click **Run** - -We are then presented with the _Nextclade_ interactive results panel, similar to the one shown in this picture: - -![](images/nextclade_overview.svg) - -We can sort the table by the column "QC" (quality control) to look at the most problematic samples. -We can see the main reason for low QC score is a high proportion of missing data affecting many of the samples. - -Two samples (CH38 and CH26) have also been highlighted as having too many private mutations, which are mutations that are not present in any other sample and so could be due to sequencing errors. - -
- - diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..e1febe8 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,29 @@ +The University of Cambridge Bioinformatics Training Facility releases these training materials under the Creative Commons Attribution license. + +---- + +This is a human-readable summary of (and not a substitute for) the [full legal text of the CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode). + +## You are free to: + +- **Share** - copy and redistribute the material in any medium or format +- **Adapt** - remix, transform, and build upon the material for any purpose, even commercially. + +The licensor cannot revoke these freedoms as long as you follow the license terms. + + +## Under the following terms: + +- **Attribution** - You must give appropriate credit (by providing the name of the authors of these materials, citing the work as indicated in the course homepage and linking to the course website), provide a [link to the license](https://creativecommons.org/licenses/by/4.0/), and indicate if changes were made. + You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. + +- **No additional restrictions** - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. + + +## Notices: + +You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. + +No warranties are given. +The license may not give you all of the permissions necessary for your intended use. +For example, other rights such as publicity, privacy, or moral rights may limit how you use the material. diff --git a/README.md b/README.md index bd5c9c0..e8270c4 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,8 @@ -# Introduction to SARS-CoV-2 Sequencing Data Analysis +# Course Name -:construction: **These materials are under active development and not ready to be used for teaching.** :construction: +This repository contains the materials for the course. -## Build Website Locally -### Requirements -* R - [https://www.r-project.org/](https://www.r-project.org/) -* pandoc - [https://pandoc.org/installing.html](https://pandoc.org/installing.html) +**Course Developers**: see our [guidelines page](https://cambiotraining.github.io/quarto-course-template/materials.html) if contributing materials. -### Step 1 -First you need to install `rmarkdown` package. To install run: -```bash -Rscript -e 'install.packages("rmarkdown")' -``` -> Note: On Windows make sure that R is added to Windows PATH. +These materials are released under a [CC BY 4.0](LICENSE.md) license. -### Step 2 -To build the website run: -```bash -Rscript -e 'rmarkdown::render_site()' -``` -If there is no error then you will see a `_site` directory, under which you will find all the html files. - -## License - -These materials have been developed under a contract between the [Bioinformatics Training Facility (University of Cambridge)](https://bioinfotraining.bio.cam.ac.uk/) and the [New Variant Assessment Platform (NVAP)](https://www.gov.uk/guidance/new-variant-assessment-platform) from Public Health England. - -If you want to use these materials please get in touch with us at `bioinfotraining @ bio.cam.ac.uk`. diff --git a/_extensions/cambiotraining/courseformat/_extension.yml b/_extensions/cambiotraining/courseformat/_extension.yml new file mode 100644 index 0000000..76eee23 --- /dev/null +++ b/_extensions/cambiotraining/courseformat/_extension.yml @@ -0,0 +1,16 @@ +title: Course Page Format +author: Cambridge Informatics Training +version: 1.0.6 +contributes: + formats: + html: + theme: [default, theme.scss] + toc: true + number-sections: true + number-depth: 3 + code-link: true + code-copy: true + shortcodes: + - star_levels.lua + filters: + - callout_exercise.lua \ No newline at end of file diff --git a/_extensions/cambiotraining/courseformat/callout_exercise.lua b/_extensions/cambiotraining/courseformat/callout_exercise.lua new file mode 100644 index 0000000..e4334ed --- /dev/null +++ b/_extensions/cambiotraining/courseformat/callout_exercise.lua @@ -0,0 +1,45 @@ +function Div(div) + -- process exercise + if div.classes:includes("callout-exercise") then + -- default title + local title = "Exercise" + -- Use first element of div as title if this is a header + if div.content[1] ~= nil and div.content[1].t == "Header" then + title = pandoc.utils.stringify(div.content[1]) + div.content:remove(1) + end + -- return a callout instead of the Div + return quarto.Callout({ + type = "exercise", + content = { pandoc.Div(div) }, + title = title, + collapse = false + }) + end + + -- process answer + if div.classes:includes("callout-answer") then + -- default title + local title = "Answer" + -- return a callout instead of the Div + return quarto.Callout({ + type = "answer", + content = { pandoc.Div(div) }, + title = title, + collapse = true + }) + end + + -- process hint + if div.classes:includes("callout-hint") then + -- default title + local title = "Hint" + -- return a callout instead of the Div + return quarto.Callout({ + type = "hint", + content = { pandoc.Div(div) }, + title = title, + collapse = true + }) + end +end diff --git a/_extensions/cambiotraining/courseformat/footer.html b/_extensions/cambiotraining/courseformat/footer.html new file mode 100644 index 0000000..670c0a9 --- /dev/null +++ b/_extensions/cambiotraining/courseformat/footer.html @@ -0,0 +1,36 @@ + + + + + + + +
+
+
+ UoC Bioinformatics Training Facility +

Licensed CC BY 4.0

+
+
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" + elseif nstars == "2" then + star_icons = "Level:
" + elseif nstars == "3" then + star_icons = "Level:
" + end + + return pandoc.RawInline("html", star_icons) + end +} diff --git a/_extensions/cambiotraining/courseformat/theme.scss b/_extensions/cambiotraining/courseformat/theme.scss new file mode 100644 index 0000000..2c2755e --- /dev/null +++ b/_extensions/cambiotraining/courseformat/theme.scss @@ -0,0 +1,139 @@ +/*-- Importing fa icons --*/ +@import url("https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css"); + + +/*-- scss:defaults --*/ + +// UoC green colour scheme (from dark to light) +// #003a41 +// #0c5963 +// #106470 +// #28828A +$navbar-bg: #106470; +$navbar-fg: white; +$body-color: black; +$link-color: #003a41; + +//make font size a bit larger than default +// $font-size-root: 1.2rem; +// $toc-font-size: 0.9rem; +// $sidebar-font-size: 0.9rem; +// $sidebar-font-size-section: 0.9rem !default; + +//tweak callout colours to match our scheme +//based on https://coolors.co/28828a-106470-003a41-d62828-f77f00 +$callout-color-note: #C6C5B9; +$callout-color-tip: #28828A; +$callout-color-caution: #D62828; +$callout-color-warning: #D62828; +$callout-color-important: #28828A; + + +/*-- scss:rules --*/ + +// headings colour +title, h1, h2, h3, h4, h5, h6 { + color: #003a41; +} + +// active section heading - affects content navigation bar on the left +div.sidebar-item-container .active { + font-weight: bold; +} + +// active links - affects header and TOC navigation bar +.sidebar .nav-link.active { + font-weight: bold; +} + +// active links on the header specifically +.navbar-dark .navbar-nav .nav-link.active { + color: white; + text-decoration: underline; +} + +// links on the header upon hover or after visited +.navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-nav .nav-link:visited { + text-decoration: underline; + color: white; +} + +// font size on footer - make larger than default +.nav-footer, .nav-footer-left, .nav-footer-right, .nav-footer-center { + font-size: 1rem; +} + +// figure display +.figure { + text-align: center; + text-indent: 0; + border-bottom: 1px solid #dee2e6; +} + +// course title +.navbar-title { + padding-left: 20px; +} + +// Download button +// Style buttons +.btn { + background-color: #106470; + border: none; + color: white; + padding: 12px 30px; + cursor: pointer; + font-size: 20px; +} + +// Darker background on mouse-over +.btn:hover { + color: white; + background-color: #003a41; +} + + +// Exercise callout styling +div.callout-exercise.callout { + border-left-color: #EA7125; +} + +div.callout-exercise.callout-style-default > .callout-header { + background-color: #F3BD48; +} + +.callout-exercise > .callout-header::before { + font-family: "Font Awesome 5 Free"; + content: "\f303"; + margin-right: 10px; +} + +// Answer callout styling +.callout-answer > .callout-header::before { + font-family: "Font Awesome 5 Free"; + content: "\f002 "; + margin-right: 10px; +} + +div.callout-answer.callout { + border-left-color: #6c757d; +} + +div.callout-answer.callout-style-default > .callout-header { + background-color: #f9f9f8; +} + +// Hint callout styling +.callout-hint > .callout-header::before { + font-family: "Font Awesome 5 Free"; + content: "\f12e "; + margin-right: 10px; +} + +div.callout-hint.callout { + border-left-color: #6c757d; +} + +div.callout-hint.callout-style-default > .callout-header { + background-color: #f9f9f8; +} \ No newline at end of file diff --git a/_extensions/cambiotraining/fontawesome/_extension.yml b/_extensions/cambiotraining/fontawesome/_extension.yml new file mode 100644 index 0000000..cdd0a27 --- /dev/null +++ b/_extensions/cambiotraining/fontawesome/_extension.yml @@ -0,0 +1,6 @@ +title: Font Awesome support +author: Carlos Scheidegger +version: 0.0.1 +contributes: + shortcodes: + - fontawesome.lua diff --git a/_extensions/cambiotraining/fontawesome/assets/css/all.css b/_extensions/cambiotraining/fontawesome/assets/css/all.css new file mode 100644 index 0000000..9c2adee --- /dev/null +++ b/_extensions/cambiotraining/fontawesome/assets/css/all.css @@ -0,0 +1,7831 @@ +/*! + * Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2022 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fas, +.fa-solid, +.far, +.fa-regular, +.fal, +.fa-light, +.fat, +.fa-thin, +.fad, +.fa-duotone, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + transition-delay: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-a::before { + content: "\41"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-address-card::before { + content: "\f2bb"; } + +.fa-contact-card::before { + content: "\f2bb"; } + +.fa-vcard::before { + content: "\f2bb"; } + +.fa-align-center::before { + content: "\f037"; } + +.fa-align-justify::before { + content: "\f039"; } + +.fa-align-left::before { + content: "\f036"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-anchor::before { + content: "\f13d"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-anchor-circle-exclamation::before { + content: "\e4ab"; } + +.fa-anchor-circle-xmark::before { + content: "\e4ac"; } + +.fa-anchor-lock::before { + content: "\e4ad"; } + +.fa-angle-down::before { + content: "\f107"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-angle-right::before { + content: "\f105"; } + +.fa-angle-up::before { + content: "\f106"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-angles-left::before { + content: "\f100"; } + +.fa-angle-double-left::before { + content: "\f100"; } + +.fa-angles-right::before { + content: "\f101"; } + +.fa-angle-double-right::before { + content: "\f101"; } + +.fa-angles-up::before { + content: "\f102"; } + +.fa-angle-double-up::before { + content: "\f102"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-apple-whole::before { + content: "\f5d1"; } + +.fa-apple-alt::before { + content: "\f5d1"; } + +.fa-archway::before { + content: "\f557"; } + +.fa-arrow-down::before { + content: "\f063"; } + +.fa-arrow-down-1-9::before { + content: "\f162"; } + +.fa-sort-numeric-asc::before { + content: "\f162"; } + +.fa-sort-numeric-down::before { + content: "\f162"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-arrow-down-a-z::before { + content: "\f15d"; } + +.fa-sort-alpha-asc::before { + content: "\f15d"; } + +.fa-sort-alpha-down::before { + content: "\f15d"; } + +.fa-arrow-down-long::before { + content: "\f175"; } + +.fa-long-arrow-down::before { + content: "\f175"; } + +.fa-arrow-down-short-wide::before { + content: "\f884"; } + +.fa-sort-amount-desc::before { + content: "\f884"; } + +.fa-sort-amount-down-alt::before { + content: "\f884"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-arrow-down-up-lock::before { + content: "\e4b0"; } + +.fa-arrow-down-wide-short::before { + content: "\f160"; } + +.fa-sort-amount-asc::before { + content: "\f160"; } + +.fa-sort-amount-down::before { + content: "\f160"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-arrow-left::before { + content: "\f060"; } + +.fa-arrow-left-long::before { + content: "\f177"; } + +.fa-long-arrow-left::before { + content: "\f177"; } + +.fa-arrow-pointer::before { + content: "\f245"; } + +.fa-mouse-pointer::before { + content: "\f245"; } + +.fa-arrow-right::before { + content: "\f061"; } + +.fa-arrow-right-arrow-left::before { + content: "\f0ec"; } + +.fa-exchange::before { + content: "\f0ec"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-arrow-right-long::before { + content: "\f178"; } + +.fa-long-arrow-right::before { + content: "\f178"; } + +.fa-arrow-right-to-bracket::before { + content: "\f090"; } + +.fa-sign-in::before { + content: "\f090"; } + +.fa-arrow-right-to-city::before { + content: "\e4b3"; } + +.fa-arrow-rotate-left::before { + content: "\f0e2"; } + +.fa-arrow-left-rotate::before { + content: "\f0e2"; } + +.fa-arrow-rotate-back::before { + content: "\f0e2"; } + +.fa-arrow-rotate-backward::before { + content: "\f0e2"; } + +.fa-undo::before { + content: "\f0e2"; } + +.fa-arrow-rotate-right::before { + content: "\f01e"; } + +.fa-arrow-right-rotate::before { + content: "\f01e"; } + +.fa-arrow-rotate-forward::before { + content: "\f01e"; } + +.fa-redo::before { + content: "\f01e"; } + +.fa-arrow-trend-down::before { + content: "\e097"; } + +.fa-arrow-trend-up::before { + content: "\e098"; } + +.fa-arrow-turn-down::before { + content: "\f149"; } + +.fa-level-down::before { + content: 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content: "\f885"; } + +.fa-sort-amount-up-alt::before { + content: "\f885"; } + +.fa-arrow-up-wide-short::before { + content: "\f161"; } + +.fa-sort-amount-up::before { + content: "\f161"; } + +.fa-arrow-up-z-a::before { + content: "\f882"; } + +.fa-sort-alpha-up-alt::before { + content: "\f882"; } + +.fa-arrows-down-to-line::before { + content: "\e4b8"; } + +.fa-arrows-down-to-people::before { + content: "\e4b9"; } + +.fa-arrows-left-right::before { + content: "\f07e"; } + +.fa-arrows-h::before { + content: "\f07e"; } + +.fa-arrows-left-right-to-line::before { + content: "\e4ba"; } + +.fa-arrows-rotate::before { + content: "\f021"; } + +.fa-refresh::before { + content: "\f021"; } + +.fa-sync::before { + content: "\f021"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-arrows-to-dot::before { + content: "\e4be"; } + +.fa-arrows-to-eye::before { + content: 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{ + content: "\f049"; } + +.fa-backward-step::before { + content: "\f048"; } + +.fa-step-backward::before { + content: "\f048"; } + +.fa-bacon::before { + content: "\f7e5"; } + +.fa-bacteria::before { + content: "\e059"; } + +.fa-bacterium::before { + content: "\e05a"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-bahai::before { + content: "\f666"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-ban::before { + content: "\f05e"; } + +.fa-cancel::before { + content: "\f05e"; } + +.fa-ban-smoking::before { + content: "\f54d"; } + +.fa-smoking-ban::before { + content: "\f54d"; } + +.fa-bandage::before { + content: "\f462"; } + +.fa-band-aid::before { + content: "\f462"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-bars-progress::before { + content: "\f828"; } + +.fa-tasks-alt::before { + content: "\f828"; } + 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content: "\f243"; } + +.fa-battery-2::before { + content: "\f243"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-bed::before { + content: "\f236"; } + +.fa-bed-pulse::before { + content: "\f487"; } + +.fa-procedures::before { + content: "\f487"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-bell::before { + content: "\f0f3"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-bell-slash::before { + content: "\f1f6"; } + +.fa-bezier-curve::before { + content: "\f55b"; } + +.fa-bicycle::before { + content: "\f206"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-biohazard::before { + content: "\f780"; } + +.fa-bitcoin-sign::before { + content: "\e0b4"; } + +.fa-blender::before { + content: "\f517"; } + +.fa-blender-phone::before { + content: "\f6b6"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-bold::before { + content: "\f032"; } + +.fa-bolt::before { + content: "\f0e7"; } + +.fa-zap::before { + content: "\f0e7"; } + +.fa-bolt-lightning::before { + content: "\e0b7"; } + +.fa-bomb::before { + content: "\f1e2"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-book::before { + content: "\f02d"; } + +.fa-book-atlas::before { + content: "\f558"; } + +.fa-atlas::before { + content: "\f558"; } + +.fa-book-bible::before { + content: "\f647"; } + +.fa-bible::before { + content: "\f647"; } + +.fa-book-bookmark::before { + content: "\e0bb"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-book-medical::before { + content: "\f7e6"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-open-reader::before { + content: "\f5da"; } + +.fa-book-reader::before { + content: "\f5da"; } + +.fa-book-quran::before { + content: "\f687"; } + 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content: "\f5df"; } + +.fa-car-burst::before { + content: "\f5e1"; } + +.fa-car-crash::before { + content: "\f5e1"; } + +.fa-car-on::before { + content: "\e4dd"; } + +.fa-car-rear::before { + content: "\f5de"; } + +.fa-car-alt::before { + content: "\f5de"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-car-tunnel::before { + content: "\e4de"; } + +.fa-caravan::before { + content: "\f8ff"; } + +.fa-caret-down::before { + content: "\f0d7"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-caret-up::before { + content: "\f0d8"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-cart-arrow-down::before { + content: "\f218"; } + +.fa-cart-flatbed::before { + content: "\f474"; } + +.fa-dolly-flatbed::before { + content: "\f474"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-cart-shopping::before { + content: "\f07a"; } + +.fa-shopping-cart::before { + content: "\f07a"; } + +.fa-cash-register::before { + content: "\f788"; } + +.fa-cat::before { + content: "\f6be"; } + +.fa-cedi-sign::before { + content: "\e0df"; } + +.fa-cent-sign::before { + content: "\e3f5"; } + +.fa-certificate::before { + content: "\f0a3"; } + +.fa-chair::before { + content: "\f6c0"; } + +.fa-chalkboard::before { + content: "\f51b"; } + +.fa-blackboard::before { + content: "\f51b"; } + +.fa-chalkboard-user::before { + content: "\f51c"; } + +.fa-chalkboard-teacher::before { + content: "\f51c"; } + +.fa-champagne-glasses::before { + content: "\f79f"; } + +.fa-glass-cheers::before { + content: "\f79f"; } + +.fa-charging-station::before { + content: "\f5e7"; } + +.fa-chart-area::before { + content: "\f1fe"; } + +.fa-area-chart::before { + content: "\f1fe"; } + +.fa-chart-bar::before { + content: "\f080"; } + +.fa-bar-chart::before { + content: "\f080"; } + +.fa-chart-column::before { + content: "\e0e3"; } + 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content: "\e4e3"; } + +.fa-clipboard-user::before { + content: "\f7f3"; } + +.fa-clock::before { + content: "\f017"; } + +.fa-clock-four::before { + content: "\f017"; } + +.fa-clock-rotate-left::before { + content: "\f1da"; } + +.fa-history::before { + content: "\f1da"; } + +.fa-clone::before { + content: "\f24d"; } + +.fa-closed-captioning::before { + content: "\f20a"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-cloud-arrow-down::before { + content: "\f0ed"; } + +.fa-cloud-download::before { + content: "\f0ed"; } + +.fa-cloud-download-alt::before { + content: "\f0ed"; } + +.fa-cloud-arrow-up::before { + content: "\f0ee"; } + +.fa-cloud-upload::before { + content: "\f0ee"; } + +.fa-cloud-upload-alt::before { + content: "\f0ee"; } + +.fa-cloud-bolt::before { + content: "\f76c"; } + +.fa-thunderstorm::before { + content: "\f76c"; } + +.fa-cloud-meatball::before { + content: "\f73b"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-cloud-moon-rain::before { + content: "\f73c"; } + +.fa-cloud-rain::before { + content: "\f73d"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-cloud-showers-water::before { + content: "\e4e4"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-code::before { + content: "\f121"; } + +.fa-code-branch::before { + content: "\f126"; } + +.fa-code-commit::before { + content: "\f386"; } + +.fa-code-compare::before { + content: "\e13a"; } + +.fa-code-fork::before { + content: "\e13b"; } + +.fa-code-merge::before { + content: "\f387"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-coins::before { + content: "\f51e"; } + +.fa-colon-sign::before { + content: "\e140"; } + +.fa-comment::before { + content: "\f075"; } + +.fa-comment-dollar::before { + content: "\f651"; } + +.fa-comment-dots::before { + content: "\f4ad"; } + +.fa-commenting::before { + content: "\f4ad"; } + 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"\f390"; } + +.fa-dharmachakra::before { + content: "\f655"; } + +.fa-diagram-next::before { + content: "\e476"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-diagram-project::before { + content: "\f542"; } + +.fa-project-diagram::before { + content: "\f542"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-diamond::before { + content: "\f219"; } + +.fa-diamond-turn-right::before { + content: "\f5eb"; } + +.fa-directions::before { + content: "\f5eb"; } + +.fa-dice::before { + content: "\f522"; } + +.fa-dice-d20::before { + content: "\f6cf"; } + +.fa-dice-d6::before { + content: "\f6d1"; } + +.fa-dice-five::before { + content: "\f523"; } + +.fa-dice-four::before { + content: "\f524"; } + +.fa-dice-one::before { + content: "\f525"; } + +.fa-dice-six::before { + content: "\f526"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-dice-two::before { + content: "\f528"; } + +.fa-disease::before { + content: "\f7fa"; } + +.fa-display::before { + content: "\e163"; } + +.fa-divide::before { + content: "\f529"; } + +.fa-dna::before { + content: "\f471"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-dolly::before { + content: "\f472"; } + +.fa-dolly-box::before { + content: "\f472"; } + +.fa-dong-sign::before { + content: "\e169"; } + +.fa-door-closed::before { + content: "\f52a"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-dove::before { + content: "\f4ba"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-down-long::before { + content: "\f309"; } + +.fa-long-arrow-alt-down::before { + content: "\f309"; } + +.fa-download::before { + content: "\f019"; } + +.fa-dragon::before { + content: "\f6d5"; } + +.fa-draw-polygon::before { + content: "\f5ee"; } + +.fa-droplet::before { + content: "\f043"; } + 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content: "\f578"; } + +.fa-fish-fins::before { + content: "\e4f2"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-flag-usa::before { + content: "\f74d"; } + +.fa-flask::before { + content: "\f0c3"; } + +.fa-flask-vial::before { + content: "\e4f3"; } + +.fa-floppy-disk::before { + content: "\f0c7"; } + +.fa-save::before { + content: "\f0c7"; } + +.fa-florin-sign::before { + content: "\e184"; } + +.fa-folder::before { + content: "\f07b"; } + +.fa-folder-blank::before { + content: "\f07b"; } + +.fa-folder-closed::before { + content: "\e185"; } + +.fa-folder-minus::before { + content: "\f65d"; } + +.fa-folder-open::before { + content: "\f07c"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-folder-tree::before { + content: "\f802"; } + +.fa-font::before { + content: "\f031"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-forward::before { + content: 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content: "\f629"; } + +.fa-gauge-simple-med::before { + content: "\f629"; } + +.fa-tachometer-average::before { + content: "\f629"; } + +.fa-gauge-simple-high::before { + content: "\f62a"; } + +.fa-tachometer::before { + content: "\f62a"; } + +.fa-tachometer-fast::before { + content: "\f62a"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-gear::before { + content: "\f013"; } + +.fa-cog::before { + content: "\f013"; } + +.fa-gears::before { + content: "\f085"; } + +.fa-cogs::before { + content: "\f085"; } + +.fa-gem::before { + content: "\f3a5"; } + +.fa-genderless::before { + content: "\f22d"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-gift::before { + content: "\f06b"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-glass-water::before { + content: "\e4f4"; } + +.fa-glass-water-droplet::before { + content: "\e4f5"; } + +.fa-glasses::before { + content: "\f530"; } + +.fa-globe::before { + content: "\f0ac"; } + 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content: "\e05e"; } + +.fa-hands-clapping::before { + content: "\e1a8"; } + +.fa-hands-holding::before { + content: "\f4c2"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-hands-holding-circle::before { + content: "\e4fb"; } + +.fa-hands-praying::before { + content: "\f684"; } + +.fa-praying-hands::before { + content: "\f684"; } + +.fa-handshake::before { + content: "\f2b5"; } + +.fa-handshake-angle::before { + content: "\f4c4"; } + +.fa-hands-helping::before { + content: "\f4c4"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-handshake-simple-slash::before { + content: "\e05f"; } + +.fa-handshake-alt-slash::before { + content: "\e05f"; } + +.fa-handshake-slash::before { + content: "\e060"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-hard-drive::before { + content: "\f0a0"; } + +.fa-hdd::before { + content: "\f0a0"; } + +.fa-hashtag::before { + content: "\23"; } + 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content: "\e500"; } + +.fa-heart-circle-xmark::before { + content: "\e501"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-heart-pulse::before { + content: "\f21e"; } + +.fa-heartbeat::before { + content: "\f21e"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-helicopter-symbol::before { + content: "\e502"; } + +.fa-helmet-safety::before { + content: "\f807"; } + +.fa-hard-hat::before { + content: "\f807"; } + +.fa-hat-hard::before { + content: "\f807"; } + +.fa-helmet-un::before { + content: "\e503"; } + +.fa-highlighter::before { + content: "\f591"; } + +.fa-hill-avalanche::before { + content: "\e507"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-hippo::before { + content: "\f6ed"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-holly-berry::before { + content: "\f7aa"; } + +.fa-horse::before { + content: "\f6f0"; } + +.fa-horse-head::before { + content: "\f7ab"; } + 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} + +.fa-house-chimney::before { + content: "\e3af"; } + +.fa-home-lg::before { + content: "\e3af"; } + +.fa-house-chimney-crack::before { + content: "\f6f1"; } + +.fa-house-damage::before { + content: "\f6f1"; } + +.fa-house-chimney-medical::before { + content: "\f7f2"; } + +.fa-clinic-medical::before { + content: "\f7f2"; } + +.fa-house-chimney-user::before { + content: "\e065"; } + +.fa-house-chimney-window::before { + content: "\e00d"; } + +.fa-house-circle-check::before { + content: "\e509"; } + +.fa-house-circle-exclamation::before { + content: "\e50a"; } + +.fa-house-circle-xmark::before { + content: "\e50b"; } + +.fa-house-crack::before { + content: "\e3b1"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-house-flag::before { + content: "\e50d"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-house-flood-water-circle-arrow-right::before { + content: "\e50f"; } + +.fa-house-laptop::before { + content: "\e066"; } + +.fa-laptop-house::before { + content: "\e066"; } + +.fa-house-lock::before { + content: "\e510"; } + +.fa-house-medical::before { + content: "\e3b2"; } + +.fa-house-medical-circle-check::before { + content: "\e511"; } + +.fa-house-medical-circle-exclamation::before { + content: "\e512"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-house-medical-flag::before { + content: "\e514"; } + +.fa-house-signal::before { + content: "\e012"; } + +.fa-house-tsunami::before { + content: "\e515"; } + +.fa-house-user::before { + content: "\e1b0"; } + +.fa-home-user::before { + content: "\e1b0"; } + +.fa-hryvnia-sign::before { + content: "\f6f2"; } + +.fa-hryvnia::before { + content: "\f6f2"; } + +.fa-hurricane::before { + content: "\f751"; } + +.fa-i::before { + content: "\49"; } + +.fa-i-cursor::before { + content: "\f246"; } + +.fa-ice-cream::before { + content: "\f810"; } + +.fa-icicles::before { + content: "\f7ad"; } + +.fa-icons::before { + content: "\f86d"; } + 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content: "\f66f"; } + +.fa-landmark-dome::before { + content: "\f752"; } + +.fa-landmark-alt::before { + content: "\f752"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-language::before { + content: "\f1ab"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-laptop-file::before { + content: "\e51d"; } + +.fa-laptop-medical::before { + content: "\f812"; } + +.fa-lari-sign::before { + content: "\e1c8"; } + +.fa-layer-group::before { + content: "\f5fd"; } + +.fa-leaf::before { + content: "\f06c"; } + +.fa-left-long::before { + content: "\f30a"; } + +.fa-long-arrow-alt-left::before { + content: "\f30a"; } + +.fa-left-right::before { + content: "\f337"; } + +.fa-arrows-alt-h::before { + content: "\f337"; } + +.fa-lemon::before { + content: "\f094"; } + +.fa-less-than::before { + content: "\3c"; } + +.fa-less-than-equal::before { + content: "\f537"; } + +.fa-life-ring::before { + content: "\f1cd"; } + 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"\f227"; } + +.fa-mars-stroke::before { + content: "\f229"; } + +.fa-mars-stroke-right::before { + content: "\f22b"; } + +.fa-mars-stroke-h::before { + content: "\f22b"; } + +.fa-mars-stroke-up::before { + content: "\f22a"; } + +.fa-mars-stroke-v::before { + content: "\f22a"; } + +.fa-martini-glass::before { + content: "\f57b"; } + +.fa-glass-martini-alt::before { + content: "\f57b"; } + +.fa-martini-glass-citrus::before { + content: "\f561"; } + +.fa-cocktail::before { + content: "\f561"; } + +.fa-martini-glass-empty::before { + content: "\f000"; } + +.fa-glass-martini::before { + content: "\f000"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-mask-ventilator::before { + content: "\e524"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-mattress-pillow::before { + content: "\e525"; } + +.fa-maximize::before { + content: "\f31e"; } + +.fa-expand-arrows-alt::before { + content: "\f31e"; } + +.fa-medal::before { + content: "\f5a2"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-mercury::before { + content: "\f223"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-meteor::before { + content: "\f753"; } + +.fa-microchip::before { + content: "\f2db"; } + +.fa-microphone::before { + content: "\f130"; } + +.fa-microphone-lines::before { + content: "\f3c9"; } + +.fa-microphone-alt::before { + content: "\f3c9"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-mill-sign::before { + content: "\e1ed"; } + +.fa-minimize::before { + content: "\f78c"; } + +.fa-compress-arrows-alt::before { + content: "\f78c"; } + +.fa-minus::before { + content: "\f068"; } + 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content: "\f53a"; } + +.fa-money-bill-wheat::before { + content: "\e52a"; } + +.fa-money-bills::before { + content: "\e1f3"; } + +.fa-money-check::before { + content: "\f53c"; } + +.fa-money-check-dollar::before { + content: "\f53d"; } + +.fa-money-check-alt::before { + content: "\f53d"; } + +.fa-monument::before { + content: "\f5a6"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-mortar-pestle::before { + content: "\f5a7"; } + +.fa-mosque::before { + content: "\f678"; } + +.fa-mosquito::before { + content: "\e52b"; } + +.fa-mosquito-net::before { + content: "\e52c"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-mound::before { + content: "\e52d"; } + +.fa-mountain::before { + content: "\f6fc"; } + +.fa-mountain-city::before { + content: "\e52e"; } + +.fa-mountain-sun::before { + content: "\e52f"; } + +.fa-mug-hot::before { + content: "\f7b6"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-music::before { + content: "\f001"; } + +.fa-n::before { + content: "\4e"; } + +.fa-naira-sign::before { + content: "\e1f6"; } + +.fa-network-wired::before { + content: "\f6ff"; } + +.fa-neuter::before { + content: "\f22c"; } + +.fa-newspaper::before { + content: "\f1ea"; } + +.fa-not-equal::before { + content: "\f53e"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-notes-medical::before { + content: "\f481"; } + +.fa-o::before { + content: "\4f"; } + +.fa-object-group::before { + content: "\f247"; } + +.fa-object-ungroup::before { + content: "\f248"; } + +.fa-oil-can::before { + content: "\f613"; } + +.fa-oil-well::before { + content: "\e532"; } + +.fa-om::before { + content: "\f679"; } + +.fa-otter::before { + content: "\f700"; } + +.fa-outdent::before { + content: "\f03b"; } + +.fa-dedent::before { + content: "\f03b"; } + +.fa-p::before { + content: "\50"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-paint-roller::before { + content: "\f5aa"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-palette::before { + content: "\f53f"; } + +.fa-pallet::before { + content: "\f482"; } + +.fa-panorama::before { + content: "\e209"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-paperclip::before { + content: "\f0c6"; } + +.fa-parachute-box::before { + content: "\f4cd"; } + +.fa-paragraph::before { + content: "\f1dd"; } + +.fa-passport::before { + content: "\f5ab"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-pause::before { + content: "\f04c"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-peace::before { + content: "\f67c"; } + +.fa-pen::before { + content: "\f304"; } + +.fa-pen-clip::before { + content: "\f305"; } + +.fa-pen-alt::before { + content: "\f305"; } + +.fa-pen-fancy::before { + content: "\f5ac"; } + +.fa-pen-nib::before { + content: "\f5ad"; } + 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"\f183"; } + +.fa-person-arrow-down-to-line::before { + content: "\e538"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-person-biking::before { + content: "\f84a"; } + +.fa-biking::before { + content: "\f84a"; } + +.fa-person-booth::before { + content: "\f756"; } + +.fa-person-breastfeeding::before { + content: "\e53a"; } + +.fa-person-burst::before { + content: "\e53b"; } + +.fa-person-cane::before { + content: "\e53c"; } + +.fa-person-chalkboard::before { + content: "\e53d"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-person-circle-exclamation::before { + content: "\e53f"; } + +.fa-person-circle-minus::before { + content: "\e540"; } + +.fa-person-circle-plus::before { + content: "\e541"; } + +.fa-person-circle-question::before { + content: "\e542"; } + +.fa-person-circle-xmark::before { + content: "\e543"; } + +.fa-person-digging::before { + content: "\f85e"; } + +.fa-digging::before { + content: "\f85e"; } + 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+.fa-person-rays::before { + content: "\e54d"; } + +.fa-person-rifle::before { + content: "\e54e"; } + +.fa-person-running::before { + content: "\f70c"; } + +.fa-running::before { + content: "\f70c"; } + +.fa-person-shelter::before { + content: "\e54f"; } + +.fa-person-skating::before { + content: "\f7c5"; } + +.fa-skating::before { + content: "\f7c5"; } + +.fa-person-skiing::before { + content: "\f7c9"; } + +.fa-skiing::before { + content: "\f7c9"; } + +.fa-person-skiing-nordic::before { + content: "\f7ca"; } + +.fa-skiing-nordic::before { + content: "\f7ca"; } + +.fa-person-snowboarding::before { + content: "\f7ce"; } + +.fa-snowboarding::before { + content: "\f7ce"; } + +.fa-person-swimming::before { + content: "\f5c4"; } + +.fa-swimmer::before { + content: "\f5c4"; } + +.fa-person-through-window::before { + content: "\e433"; } + +.fa-person-walking::before { + content: "\f554"; } + +.fa-walking::before { + content: "\f554"; } + +.fa-person-walking-arrow-loop-left::before { + content: "\e551"; } + +.fa-person-walking-arrow-right::before { + content: "\e552"; } + +.fa-person-walking-dashed-line-arrow-right::before { + content: "\e553"; } + +.fa-person-walking-luggage::before { + content: "\e554"; } + +.fa-person-walking-with-cane::before { + content: "\f29d"; } + +.fa-blind::before { + content: "\f29d"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-peso-sign::before { + content: "\e222"; } + +.fa-phone::before { + content: "\f095"; } + +.fa-phone-flip::before { + content: "\f879"; } + +.fa-phone-alt::before { + content: "\f879"; } + +.fa-phone-slash::before { + content: "\f3dd"; } + +.fa-phone-volume::before { + content: "\f2a0"; } + +.fa-volume-control-phone::before { + content: "\f2a0"; } + +.fa-photo-film::before { + content: "\f87c"; } + +.fa-photo-video::before { + content: "\f87c"; } + +.fa-piggy-bank::before { + content: "\f4d3"; } + +.fa-pills::before { + content: "\f484"; } + +.fa-pizza-slice::before { + content: "\f818"; } + 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content: "\e55f"; } + +.fa-plug-circle-xmark::before { + content: "\e560"; } + +.fa-plus::before { + content: "\2b"; } + +.fa-add::before { + content: "\2b"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-podcast::before { + content: "\f2ce"; } + +.fa-poo::before { + content: "\f2fe"; } + +.fa-poo-storm::before { + content: "\f75a"; } + +.fa-poo-bolt::before { + content: "\f75a"; } + +.fa-poop::before { + content: "\f619"; } + +.fa-power-off::before { + content: "\f011"; } + +.fa-prescription::before { + content: "\f5b1"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-prescription-bottle-medical::before { + content: "\f486"; } + +.fa-prescription-bottle-alt::before { + content: "\f486"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-pump-soap::before { + content: "\e06b"; } + +.fa-puzzle-piece::before { + content: "\f12e"; } + +.fa-q::before { + content: "\51"; } + +.fa-qrcode::before { + content: "\f029"; } + +.fa-question::before { + content: "\3f"; } + +.fa-quote-left::before { + content: "\f10d"; } + +.fa-quote-left-alt::before { + content: "\f10d"; } + +.fa-quote-right::before { + content: "\f10e"; } + +.fa-quote-right-alt::before { + content: "\f10e"; } + +.fa-r::before { + content: "\52"; } + +.fa-radiation::before { + content: "\f7b9"; } + +.fa-radio::before { + content: "\f8d7"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-ranking-star::before { + content: "\e561"; } + +.fa-receipt::before { + content: "\f543"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-rectangle-ad::before { + content: "\f641"; } + +.fa-ad::before { + content: "\f641"; } + +.fa-rectangle-list::before { + content: "\f022"; } + +.fa-list-alt::before { + content: "\f022"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-recycle::before { + content: "\f1b8"; } + +.fa-registered::before { + content: "\f25d"; } + +.fa-repeat::before { + content: "\f363"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-reply-all::before { + content: "\f122"; } + +.fa-mail-reply-all::before { + content: "\f122"; } + +.fa-republican::before { + content: "\f75e"; } + +.fa-restroom::before { + content: "\f7bd"; } + +.fa-retweet::before { + content: "\f079"; } + +.fa-ribbon::before { + content: "\f4d6"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-right-long::before { + content: "\f30b"; } + +.fa-long-arrow-alt-right::before { + content: "\f30b"; } + +.fa-right-to-bracket::before { + content: "\f2f6"; } + +.fa-sign-in-alt::before { + content: "\f2f6"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-road::before { + content: "\f018"; } + +.fa-road-barrier::before { + content: "\e562"; } + +.fa-road-bridge::before { + content: "\e563"; } + +.fa-road-circle-check::before { + content: "\e564"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-road-circle-xmark::before { + content: "\e566"; } + +.fa-road-lock::before { + content: "\e567"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-robot::before { + content: "\f544"; } + +.fa-rocket::before { + content: "\f135"; } + +.fa-rotate::before { + content: "\f2f1"; } + +.fa-sync-alt::before { + content: "\f2f1"; } + +.fa-rotate-left::before { + content: "\f2ea"; } + +.fa-rotate-back::before { + content: "\f2ea"; } + +.fa-rotate-backward::before { + content: "\f2ea"; } + +.fa-undo-alt::before { + content: "\f2ea"; } + +.fa-rotate-right::before { + content: "\f2f9"; } + +.fa-redo-alt::before { + content: "\f2f9"; } + +.fa-rotate-forward::before { + content: "\f2f9"; } + 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} + +.fa-scale-balanced::before { + content: "\f24e"; } + +.fa-balance-scale::before { + content: "\f24e"; } + +.fa-scale-unbalanced::before { + content: "\f515"; } + +.fa-balance-scale-left::before { + content: "\f515"; } + +.fa-scale-unbalanced-flip::before { + content: "\f516"; } + +.fa-balance-scale-right::before { + content: "\f516"; } + +.fa-school::before { + content: "\f549"; } + +.fa-school-circle-check::before { + content: "\e56b"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-school-flag::before { + content: "\e56e"; } + +.fa-school-lock::before { + content: "\e56f"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-screwdriver::before { + content: "\f54a"; } + +.fa-screwdriver-wrench::before { + content: "\f7d9"; } + +.fa-tools::before { + content: "\f7d9"; } + +.fa-scroll::before { + content: "\f70e"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-sd-card::before { + content: "\f7c2"; } + +.fa-section::before { + content: "\e447"; } + +.fa-seedling::before { + content: "\f4d8"; } + +.fa-sprout::before { + content: "\f4d8"; } + +.fa-server::before { + content: "\f233"; } + +.fa-shapes::before { + content: "\f61f"; } + +.fa-triangle-circle-square::before { + content: "\f61f"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-share-from-square::before { + content: "\f14d"; } + +.fa-share-square::before { + content: "\f14d"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-shekel-sign::before { + content: "\f20b"; } + +.fa-ils::before { + content: "\f20b"; } + +.fa-shekel::before { + content: "\f20b"; } + +.fa-sheqel::before { + content: 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content: "\f1de"; } + +.fa-sliders-h::before { + content: "\f1de"; } + +.fa-smog::before { + content: "\f75f"; } + +.fa-smoking::before { + content: "\f48d"; } + +.fa-snowflake::before { + content: "\f2dc"; } + +.fa-snowman::before { + content: "\f7d0"; } + +.fa-snowplow::before { + content: "\f7d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-socks::before { + content: "\f696"; } + +.fa-solar-panel::before { + content: "\f5ba"; } + +.fa-sort::before { + content: "\f0dc"; } + +.fa-unsorted::before { + content: "\f0dc"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-sort-up::before { + content: "\f0de"; } + +.fa-sort-asc::before { + content: "\f0de"; } + +.fa-spa::before { + content: "\f5bb"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-spell-check::before { + content: "\f891"; } + +.fa-spider::before { + content: "\f717"; } + 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+ content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-square-envelope::before { + content: "\f199"; } + +.fa-envelope-square::before { + content: "\f199"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-square-h::before { + content: "\f0fd"; } + +.fa-h-square::before { + content: "\f0fd"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-square-nfi::before { + content: "\e576"; } + +.fa-square-parking::before { + content: "\f540"; } + +.fa-parking::before { + content: "\f540"; } + +.fa-square-pen::before { + content: "\f14b"; } + +.fa-pen-square::before { + content: "\f14b"; } + +.fa-pencil-square::before { + content: "\f14b"; } + +.fa-square-person-confined::before { + content: "\e577"; } + +.fa-square-phone::before { + content: "\f098"; } + +.fa-phone-square::before { + content: "\f098"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-square-plus::before { + content: "\f0fe"; } + +.fa-plus-square::before { + content: "\f0fe"; } + +.fa-square-poll-horizontal::before { + content: "\f682"; } + +.fa-poll-h::before { + content: "\f682"; } + +.fa-square-poll-vertical::before { + content: "\f681"; } + +.fa-poll::before { + content: "\f681"; } + +.fa-square-root-variable::before { + content: "\f698"; } + +.fa-square-root-alt::before { + content: "\f698"; } + +.fa-square-rss::before { + content: "\f143"; } + +.fa-rss-square::before { + content: "\f143"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-square-virus::before { + content: "\e578"; } + +.fa-square-xmark::before { + content: "\f2d3"; } + +.fa-times-square::before { + content: "\f2d3"; } + +.fa-xmark-square::before { + content: "\f2d3"; } + 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"\e06f"; } + +.fa-store::before { + content: "\f54e"; } + +.fa-store-slash::before { + content: "\e071"; } + +.fa-street-view::before { + content: "\f21d"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-stroopwafel::before { + content: "\f551"; } + +.fa-subscript::before { + content: "\f12c"; } + +.fa-suitcase::before { + content: "\f0f2"; } + +.fa-suitcase-medical::before { + content: "\f0fa"; } + +.fa-medkit::before { + content: "\f0fa"; } + +.fa-suitcase-rolling::before { + content: "\f5c1"; } + +.fa-sun::before { + content: "\f185"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-superscript::before { + content: "\f12b"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-synagogue::before { + content: "\f69b"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-t::before { + content: "\54"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-table-cells::before { + content: "\f00a"; } + +.fa-th::before { + content: "\f00a"; } + 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content: "\f205"; } + +.fa-toilet::before { + content: "\f7d8"; } + +.fa-toilet-paper::before { + content: "\f71e"; } + +.fa-toilet-paper-slash::before { + content: "\e072"; } + +.fa-toilet-portable::before { + content: "\e583"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-toolbox::before { + content: "\f552"; } + +.fa-tooth::before { + content: "\f5c9"; } + +.fa-torii-gate::before { + content: "\f6a1"; } + +.fa-tornado::before { + content: "\f76f"; } + +.fa-tower-broadcast::before { + content: "\f519"; } + +.fa-broadcast-tower::before { + content: "\f519"; } + +.fa-tower-cell::before { + content: "\e585"; } + +.fa-tower-observation::before { + content: "\e586"; } + +.fa-tractor::before { + content: "\f722"; } + +.fa-trademark::before { + content: "\f25c"; } + +.fa-traffic-light::before { + content: "\f637"; } + +.fa-trailer::before { + content: "\e041"; } + +.fa-train::before { + content: "\f238"; } + +.fa-train-subway::before { + content: "\f239"; } + 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content: "\e58a"; } + +.fa-truck::before { + content: "\f0d1"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-truck-droplet::before { + content: "\e58c"; } + +.fa-truck-fast::before { + content: "\f48b"; } + +.fa-shipping-fast::before { + content: "\f48b"; } + +.fa-truck-field::before { + content: "\e58d"; } + +.fa-truck-field-un::before { + content: "\e58e"; } + +.fa-truck-front::before { + content: "\e2b7"; } + +.fa-truck-medical::before { + content: "\f0f9"; } + +.fa-ambulance::before { + content: "\f0f9"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-truck-moving::before { + content: "\f4df"; } + +.fa-truck-pickup::before { + content: "\f63c"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-tty::before { + content: "\f1e4"; } + +.fa-teletype::before { + content: "\f1e4"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-turn-down::before { + content: "\f3be"; } + +.fa-level-down-alt::before { + content: "\f3be"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.fa-tv::before { + content: "\f26c"; } + +.fa-television::before { + content: "\f26c"; } + +.fa-tv-alt::before { + content: "\f26c"; } + +.fa-u::before { + content: "\55"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-umbrella-beach::before { + content: "\f5ca"; } + +.fa-underline::before { + content: "\f0cd"; } + +.fa-universal-access::before { + content: "\f29a"; } + +.fa-unlock::before { + content: "\f09c"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-up-down::before { + content: "\f338"; } + +.fa-arrows-alt-v::before { + content: "\f338"; } + +.fa-up-down-left-right::before { + content: "\f0b2"; } + +.fa-arrows-alt::before { + content: "\f0b2"; } + +.fa-up-long::before { + content: "\f30c"; } + +.fa-long-arrow-alt-up::before { + content: "\f30c"; } + +.fa-up-right-and-down-left-from-center::before { + content: "\f424"; } + +.fa-expand-alt::before { + content: "\f424"; } + +.fa-up-right-from-square::before { + content: "\f35d"; } + +.fa-external-link-alt::before { + content: "\f35d"; } + +.fa-upload::before { + content: "\f093"; } + +.fa-user::before { + content: "\f007"; } + +.fa-user-astronaut::before { + content: "\f4fb"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-user-doctor::before { + content: "\f0f0"; } + +.fa-user-md::before { + content: "\f0f0"; } + +.fa-user-gear::before { + content: "\f4fe"; } + +.fa-user-cog::before { + content: "\f4fe"; } + +.fa-user-graduate::before { + content: "\f501"; } + +.fa-user-group::before { + content: "\f500"; } + +.fa-user-friends::before { + content: "\f500"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-user-large-slash::before { + content: "\f4fa"; } + +.fa-user-alt-slash::before { + content: "\f4fa"; } + +.fa-user-lock::before { + content: "\f502"; } + +.fa-user-minus::before { + content: "\f503"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-user-pen::before { + content: "\f4ff"; } + +.fa-user-edit::before { + content: "\f4ff"; } + +.fa-user-plus::before { + content: "\f234"; } + +.fa-user-secret::before { + content: "\f21b"; } + +.fa-user-shield::before { + content: "\f505"; } + +.fa-user-slash::before { + content: "\f506"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-user-tie::before { + content: "\f508"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-users::before { + content: "\f0c0"; } + +.fa-users-between-lines::before { + content: "\e591"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-users-line::before { + content: "\e592"; } + +.fa-users-rays::before { + content: "\e593"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-users-viewfinder::before { + content: "\e595"; } + +.fa-utensils::before { + content: "\f2e7"; } + +.fa-cutlery::before { + content: "\f2e7"; } + +.fa-v::before { + content: "\56"; } + +.fa-van-shuttle::before { + content: "\f5b6"; } + +.fa-shuttle-van::before { + content: "\f5b6"; } + +.fa-vault::before { + content: "\e2c5"; } + +.fa-vector-square::before { + content: "\f5cb"; } + +.fa-venus::before { + content: "\f221"; } + +.fa-venus-double::before { + content: "\f226"; } + +.fa-venus-mars::before { + content: "\f228"; } + +.fa-vest::before { + content: "\e085"; } + +.fa-vest-patches::before { + content: "\e086"; } + +.fa-vial::before { + content: "\f492"; } + +.fa-vial-circle-check::before { + content: "\e596"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-vials::before { + content: "\f493"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-video-slash::before { + content: "\f4e2"; } + +.fa-vihara::before { + content: "\f6a7"; } + +.fa-virus::before { + content: "\e074"; } + +.fa-virus-covid::before { + content: "\e4a8"; } + +.fa-virus-covid-slash::before { + content: "\e4a9"; } + +.fa-virus-slash::before { + content: "\e075"; } + +.fa-viruses::before { + content: "\e076"; } + +.fa-voicemail::before { + content: "\f897"; } + +.fa-volcano::before { + content: "\f770"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-volume-high::before { + content: "\f028"; } + +.fa-volume-up::before { + content: "\f028"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-volume-off::before { + content: "\f026"; } + +.fa-volume-xmark::before { + content: "\f6a9"; } + +.fa-volume-mute::before { + content: "\f6a9"; } + +.fa-volume-times::before { + content: "\f6a9"; } + +.fa-vr-cardboard::before { + content: "\f729"; } + +.fa-w::before { + content: "\57"; } + +.fa-walkie-talkie::before { + content: "\f8ef"; } + +.fa-wallet::before { + content: "\f555"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-wand-magic-sparkles::before { + content: "\e2ca"; } + +.fa-magic-wand-sparkles::before { + content: "\e2ca"; } + +.fa-wand-sparkles::before { + content: "\f72b"; } + +.fa-warehouse::before { + content: "\f494"; } + +.fa-water::before { + content: "\f773"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-weight-hanging::before { + content: "\f5cd"; } + +.fa-weight-scale::before { + content: "\f496"; } + +.fa-weight::before { + content: "\f496"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-wheat-awn-circle-exclamation::before { + content: "\e598"; } + +.fa-wheelchair::before { + content: "\f193"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-whiskey-glass::before { + content: "\f7a0"; } + +.fa-glass-whiskey::before { + content: "\f7a0"; } + +.fa-wifi::before { + content: "\f1eb"; } + +.fa-wifi-3::before { + content: "\f1eb"; } + +.fa-wifi-strong::before { + content: "\f1eb"; } + +.fa-wind::before { + content: "\f72e"; } + +.fa-window-maximize::before { + content: "\f2d0"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-window-restore::before { + content: "\f2d2"; } + +.fa-wine-bottle::before { + content: "\f72f"; } + +.fa-wine-glass::before { + content: "\f4e3"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-won-sign::before { + content: "\f159"; } + +.fa-krw::before { + content: "\f159"; } + +.fa-won::before { + content: "\f159"; } + +.fa-worm::before { + content: "\e599"; } + +.fa-wrench::before { + content: "\f0ad"; } + +.fa-x::before { + content: "\58"; } + +.fa-x-ray::before { + content: "\f497"; } + +.fa-xmark::before { + content: "\f00d"; } + +.fa-close::before { + content: "\f00d"; } + +.fa-multiply::before { + content: "\f00d"; } + +.fa-remove::before { + content: "\f00d"; } + +.fa-times::before { + content: "\f00d"; } + +.fa-xmarks-lines::before { + content: "\e59a"; } + +.fa-y::before { + content: "\59"; } + +.fa-yen-sign::before { + content: "\f157"; } + +.fa-cny::before { + content: "\f157"; } + +.fa-jpy::before { + content: "\f157"; } + +.fa-rmb::before { + content: "\f157"; } + +.fa-yen::before { + content: "\f157"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-z::before { + content: "\5a"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-font-brands: normal 400 1em/1 "Font Awesome 6 Brands"; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa-42-group:before { + content: "\e080"; } + +.fa-innosoft:before { + content: "\e080"; } + +.fa-500px:before { + content: "\f26e"; } + +.fa-accessible-icon:before { + content: "\f368"; } + +.fa-accusoft:before { + content: "\f369"; } + +.fa-adn:before { + content: "\f170"; } + +.fa-adversal:before { + content: "\f36a"; } + +.fa-affiliatetheme:before { + content: "\f36b"; } + +.fa-airbnb:before { + content: "\f834"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-alipay:before { + content: "\f642"; } + +.fa-amazon:before { + content: "\f270"; } + +.fa-amazon-pay:before { + content: "\f42c"; } + +.fa-amilia:before { + content: "\f36d"; } + +.fa-android:before { + content: "\f17b"; } + +.fa-angellist:before { + content: "\f209"; } + +.fa-angrycreative:before { + content: "\f36e"; } + +.fa-angular:before { + content: "\f420"; } + +.fa-app-store:before { + content: "\f36f"; } + +.fa-app-store-ios:before { + content: "\f370"; } + +.fa-apper:before { + content: "\f371"; } + +.fa-apple:before { + content: "\f179"; } + +.fa-apple-pay:before { + content: "\f415"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-asymmetrik:before { + content: "\f372"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-audible:before { + content: "\f373"; } + +.fa-autoprefixer:before { + content: "\f41c"; } + +.fa-avianex:before { + content: "\f374"; } + +.fa-aviato:before { + content: "\f421"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-bandcamp:before { + content: "\f2d5"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-behance:before { + content: "\f1b4"; } + +.fa-behance-square:before { + content: "\f1b5"; } + +.fa-bilibili:before { + content: "\e3d9"; } + +.fa-bimobject:before { + content: "\f378"; } + +.fa-bitbucket:before { + content: "\f171"; } + +.fa-bitcoin:before { + content: "\f379"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-black-tie:before { + content: "\f27e"; } + +.fa-blackberry:before { + content: "\f37b"; } + +.fa-blogger:before { + content: "\f37c"; } + +.fa-blogger-b:before { + content: "\f37d"; } + +.fa-bluetooth:before { + content: "\f293"; } + +.fa-bluetooth-b:before { + content: "\f294"; } + +.fa-bootstrap:before { + content: "\f836"; } + +.fa-bots:before { + content: "\e340"; } + +.fa-btc:before { + content: "\f15a"; } + +.fa-buffer:before { + content: "\f837"; } + +.fa-buromobelexperte:before { + content: "\f37f"; } + +.fa-buy-n-large:before { + content: "\f8a6"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-canadian-maple-leaf:before { + content: "\f785"; } + +.fa-cc-amazon-pay:before { + content: "\f42d"; } + +.fa-cc-amex:before { + content: "\f1f3"; } + +.fa-cc-apple-pay:before { + content: "\f416"; } + +.fa-cc-diners-club:before { + content: "\f24c"; } + +.fa-cc-discover:before { + content: "\f1f2"; } + +.fa-cc-jcb:before { + content: "\f24b"; } + +.fa-cc-mastercard:before { + content: "\f1f1"; } + +.fa-cc-paypal:before { + content: "\f1f4"; } + +.fa-cc-stripe:before { + content: "\f1f5"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-centos:before { + content: "\f789"; } + +.fa-chrome:before { + content: "\f268"; } + +.fa-chromecast:before { + content: "\f838"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-cloudscale:before { + content: "\f383"; } + +.fa-cloudsmith:before { + content: "\f384"; } + +.fa-cloudversify:before { + content: "\f385"; } + +.fa-cmplid:before { + content: "\e360"; } + +.fa-codepen:before { + content: "\f1cb"; } + +.fa-codiepie:before { + content: "\f284"; } + +.fa-confluence:before { + content: "\f78d"; } + +.fa-connectdevelop:before { + content: "\f20e"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-cotton-bureau:before { + content: "\f89e"; } + +.fa-cpanel:before { + content: "\f388"; } + +.fa-creative-commons:before { + content: "\f25e"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-creative-commons-nc-eu:before { + content: "\f4e9"; } + +.fa-creative-commons-nc-jp:before { + content: "\f4ea"; } + +.fa-creative-commons-nd:before { + content: "\f4eb"; } + +.fa-creative-commons-pd:before { + content: "\f4ec"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-creative-commons-remix:before { + content: "\f4ee"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-creative-commons-sampling:before { + content: "\f4f0"; } + +.fa-creative-commons-sampling-plus:before { + content: "\f4f1"; } + +.fa-creative-commons-share:before { + content: "\f4f2"; } + +.fa-creative-commons-zero:before { + content: "\f4f3"; } + +.fa-critical-role:before { + content: "\f6c9"; } + +.fa-css3:before { + content: "\f13c"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-cuttlefish:before { + content: "\f38c"; } + +.fa-d-and-d:before { + content: "\f38d"; } + +.fa-d-and-d-beyond:before { + content: "\f6ca"; } + +.fa-dailymotion:before { + content: "\e052"; } + +.fa-dashcube:before { + content: "\f210"; } + +.fa-deezer:before { + content: "\e077"; } + +.fa-delicious:before { + content: "\f1a5"; } + +.fa-deploydog:before { + content: "\f38e"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-dev:before { + content: "\f6cc"; } + +.fa-deviantart:before { + content: "\f1bd"; } + +.fa-dhl:before { + content: "\f790"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-digg:before { + content: "\f1a6"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-discord:before { + content: "\f392"; } + +.fa-discourse:before { + content: "\f393"; } + +.fa-dochub:before { + content: "\f394"; } + +.fa-docker:before { + content: "\f395"; } + +.fa-draft2digital:before { + content: "\f396"; } + +.fa-dribbble:before { + content: "\f17d"; } + +.fa-dribbble-square:before { + content: "\f397"; } + +.fa-dropbox:before { + content: "\f16b"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-earlybirds:before { + content: "\f39a"; } + +.fa-ebay:before { + content: "\f4f4"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-edge-legacy:before { + content: "\e078"; } + +.fa-elementor:before { + content: "\f430"; } + +.fa-ello:before { + content: "\f5f1"; } + +.fa-ember:before { + content: "\f423"; } + +.fa-empire:before { + content: "\f1d1"; } + +.fa-envira:before { + content: "\f299"; } + +.fa-erlang:before { + content: "\f39d"; } + +.fa-ethereum:before { + content: "\f42e"; } + +.fa-etsy:before { + content: "\f2d7"; } + +.fa-evernote:before { + content: "\f839"; } + +.fa-expeditedssl:before { + content: "\f23e"; } + +.fa-facebook:before { + content: "\f09a"; } + +.fa-facebook-f:before { + content: "\f39e"; } + +.fa-facebook-messenger:before { + content: "\f39f"; } + +.fa-facebook-square:before { + content: "\f082"; } + +.fa-fantasy-flight-games:before { + content: "\f6dc"; } + +.fa-fedex:before { + content: "\f797"; } + +.fa-fedora:before { + content: "\f798"; } + +.fa-figma:before { + content: "\f799"; } + +.fa-firefox:before { + content: "\f269"; } + +.fa-firefox-browser:before { + content: "\e007"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-first-order-alt:before { + content: "\f50a"; } + +.fa-firstdraft:before { + content: "\f3a1"; } + +.fa-flickr:before { + content: "\f16e"; } + +.fa-flipboard:before { + content: "\f44d"; } + +.fa-fly:before { + content: "\f417"; } + +.fa-font-awesome:before { + content: "\f2b4"; } + +.fa-font-awesome-flag:before { + content: "\f2b4"; } + +.fa-font-awesome-logo-full:before { + content: "\f2b4"; } + +.fa-fonticons:before { + content: "\f280"; } + +.fa-fonticons-fi:before { + content: "\f3a2"; } + +.fa-fort-awesome:before { + content: "\f286"; } + +.fa-fort-awesome-alt:before { + content: "\f3a3"; } + +.fa-forumbee:before { + content: "\f211"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-free-code-camp:before { + content: "\f2c5"; } + +.fa-freebsd:before { + content: "\f3a4"; } + +.fa-fulcrum:before { + content: "\f50b"; } + +.fa-galactic-republic:before { + content: "\f50c"; } + +.fa-galactic-senate:before { + content: "\f50d"; } + +.fa-get-pocket:before { + content: "\f265"; } + +.fa-gg:before { + content: "\f260"; } + +.fa-gg-circle:before { + content: "\f261"; } + +.fa-git:before { + content: "\f1d3"; } + +.fa-git-alt:before { + content: "\f841"; } + +.fa-git-square:before { + content: "\f1d2"; } + +.fa-github:before { + content: "\f09b"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-github-square:before { + content: "\f092"; } + +.fa-gitkraken:before { + content: "\f3a6"; } + +.fa-gitlab:before { + content: "\f296"; } + +.fa-gitter:before { + content: "\f426"; } + +.fa-glide:before { + content: "\f2a5"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-gofore:before { + content: "\f3a7"; } + +.fa-golang:before { + content: "\e40f"; } + +.fa-goodreads:before { + content: "\f3a8"; } + +.fa-goodreads-g:before { + content: "\f3a9"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-google-drive:before { + content: "\f3aa"; } + +.fa-google-pay:before { + content: "\e079"; } + +.fa-google-play:before { + content: "\f3ab"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-google-plus-g:before { + content: "\f0d5"; } + +.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa-google-wallet:before { + content: "\f1ee"; } + +.fa-gratipay:before { + content: "\f184"; } + +.fa-grav:before { + content: "\f2d6"; } + +.fa-gripfire:before { + content: "\f3ac"; } + +.fa-grunt:before { + content: "\f3ad"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-gulp:before { + content: "\f3ae"; } + +.fa-hacker-news:before { + content: "\f1d4"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-hackerrank:before { + content: "\f5f7"; } + +.fa-hashnode:before { + content: "\e499"; } + +.fa-hips:before { + content: "\f452"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-hive:before { + content: "\e07f"; } + 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} + +.fa-weebly:before { + content: "\f5cc"; } + +.fa-weibo:before { + content: "\f18a"; } + +.fa-weixin:before { + content: "\f1d7"; } + +.fa-whatsapp:before { + content: "\f232"; } + +.fa-whatsapp-square:before { + content: "\f40c"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-wikipedia-w:before { + content: "\f266"; } + +.fa-windows:before { + content: "\f17a"; } + +.fa-wirsindhandwerk:before { + content: "\e2d0"; } + +.fa-wsh:before { + content: "\e2d0"; } + +.fa-wix:before { + content: "\f5cf"; } + +.fa-wizards-of-the-coast:before { + content: "\f730"; } + +.fa-wodu:before { + content: "\e088"; } + +.fa-wolf-pack-battalion:before { + content: "\f514"; } + +.fa-wordpress:before { + content: "\f19a"; } + +.fa-wordpress-simple:before { + content: "\f411"; } + +.fa-wpbeginner:before { + content: "\f297"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-wpforms:before { + content: "\f298"; } + +.fa-wpressr:before { + content: "\f3e4"; } + +.fa-xbox:before { + content: 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'assets/css/latex-fontsize.css'} + }) +end + +local function isEmpty(s) + return s == nil or s == '' +end + +local function isValidSize(size) + local validSizes = { + "tiny", + "scriptsize", + "footnotesize", + "small", + "normalsize", + "large", + "Large", + "LARGE", + "huge", + "Huge" + } + for _, v in ipairs(validSizes) do + if v == size then + return size + end + end + return "" +end + +return { + ["fa"] = function(args, kwargs) + + local group = "solid" + local icon = pandoc.utils.stringify(args[1]) + if #args > 1 then + group = icon + icon = pandoc.utils.stringify(args[2]) + end + + local title = pandoc.utils.stringify(kwargs["title"]) + if not isEmpty(title) then + title = " title=\"" .. title .. "\"" + end + + local size = pandoc.utils.stringify(kwargs["size"]) + + -- detect html (excluding epub which won't handle fa) + if quarto.doc.isFormat("html:js") then + ensureHtmlDeps() + if not isEmpty(size) then + size = " fa-" .. size + end + return pandoc.RawInline( + 'html', + "" + ) + -- detect pdf / beamer / latex / etc + elseif quarto.doc.isFormat("pdf") then + ensureLatexDeps() + if isEmpty(isValidSize(size)) then + return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") + else + return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") + end + else + return pandoc.Null() + end + end +} diff --git a/_freeze/materials/appendices/quick_r/execute-results/html.json b/_freeze/materials/appendices/quick_r/execute-results/html.json new file mode 100644 index 0000000..c045f69 --- /dev/null +++ b/_freeze/materials/appendices/quick_r/execute-results/html.json @@ -0,0 +1,17 @@ +{ + "hash": "b8a5917edb27f61497dc893a991abaac", + "result": { + "engine": "knitr", + "markdown": "---\npagetitle: \"SARS Genomic Surveillance\"\noutput: html_document\n---\n\n\n\n\n# R fundamentals {.unnumbered}\n\nThis section gives a (very) quick and brief introduction to R and RStudio. \nIt will not teach you the details of how to program in R, but it should serve as a basis to start you on that journey. \nR can be used for a range of data analysis applications, including statistics, machine learning, plotting, image analysis, bioinformatics, etc. \nR is also a programming language, which makes it very flexible. \nWith this flexibility, comes a somewhat steeper learning curve, compared to point-and-click programs. \nHowever, in the long run it can really help you improve as a data analyst. \n\nThese materials will not teach you the details of how to write your own R programs (we won't have time for these), but to make you familiar with some of its applications and how to use RStudio to run your code.\nIt's only the start of your journey into R programming. \n\nIf you want to learn more about this topic, we recommend:\n\n- [R for Data Science](https://r4ds.had.co.nz/) - available as a free e-book.\n- [Introduction to R/tidyverse](https://tavareshugo.github.io/r-intro-tidyverse-gapminder/) - course materials introducing R and the `tidyverse` packages. \n\n\n## RStudio\n\nR is the software and programming language itself, but the R interface is quite basic and not very user-friendly. \nFortunately, there is a companion piece of software called _RStudio_, which makes working with R a little bit easier. \n\nThere are 4 basic panels in RStudio (see image below):\n\n- The “script” panel is essentially a text editor, where we can write code and save it as a text file, which in this case, because it contains R code, we call it an R script (but remember, a script is just a text file with some code in it. We’ve been creating shell scripts that run on the command line, here we have some R scripts, which contain R code)\n- The “console” panel is where the code actually runs, or is executed. This is equivalent to the terminal, on the command line. If we want to execute a line of code, then we need to run it on the console. \n - One nice feature of RStudio is that we can edit our code on the script panel and then run a line of code from the script on the console - it's like copy/pasting that line of code from the script to the console. This makes working interactively with RStudio much easier, because we can edit our code in the script and run it as we go along. \n- On the top-right we have the “Environment”, which shows us objects that we create, that store information such as tables of data that we read into R. \n- Finally we have the bottom-right quadrant, which contains a few tabs: a file browser (allowing us to see files in our computer), a plot display panel (for plots we generate) and a help panel to look at documentation of R functions.\n\n![](images/rstudio_panels.png)\n\n\n### Setting RStudio\n\nBefore we start working with RStudio, it’s a good idea to change one of its default options. \nGo to Tools → Global Options... and change the following:\n\n![](images/rstudio_setup.png)\n\nThis will tell RStudio to NOT automatically save and load things that we have done in the past. \nYou may think this is a helpful thing, but actually it’s very inconvenient, because if you are doing multiple analysis, it might get very very confusing what the objects that you created are! \nSo, it’s always best to start R with a fresh session, and setting these options makes sure we do this. \n\n\n### Starting a Project\n\nR has a concept called **working directory**, which is the location on your computer where it is working from (looking for files and folders). \nYou can think of it as the folder that you `cd` into if you were working on the command-line. \n\nThe easiest way to ensure that R is using the correct working directory for our analysis, is to create an **R project**. \nIn RStudio: File → New Project... → Existing Directory and then click the Browse... button to navigate to the folder where your project files are located. \n\nThis will create an `.Rproj` file on your project folder. \nNext time you want to work on your analysis, you can simply double-click on this file, and it will open RStudio, with the correct working directory already set for you. \n\n\n## R Basics\n\nThis section introduces some of the basic concepts in the R programming language. \n\n### Packages/Libraries\n\nR has several extensions to the basic functionality called “packages” or “libraries”. \nA popular library for data manipulation is called `tidyverse`, which we are using in this course. \nEach time we start a new R session, we need to load the libraries we want to use: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n::: {.cell}\n\n:::\n\n\nIf you get an error like `Error in library(tidyverse) : there is no package called ‘tidyverse’`, that means that you didn't install the package. \nTo install packages you can run: \n\n\n::: {.cell}\n\n```{.r .cell-code}\ninstall.packages(\"tidyverse\")\n```\n:::\n\n\nYou only need to do this the first time you want to use a library. \nOnce it's installed, you don't need to run this command again (unless you want to update the library to its latest version -- often a good idea!).\n\n\n### Create objects\n\nCreate **objects** (something that contains a value) with `<-`.\nFor example, the following creates an object called `x` containing a single number:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx <- 53.341\n```\n:::\n\n\nWe can print the content of the object by typing its name:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 53.341\n```\n\n\n:::\n:::\n\n\n\n### Functions\n\nMost of the tasks we can achieve in R are done through the use of **functions**. \nWe can think of functions as mini-programs that take an input and give an output.\n\nFunctions are easy to identify, because they are always followed by parenthesis. \nInside the parenthesis we include the _inputs_ to the function. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nround(x) # round the the value of x\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 53\n```\n\n\n:::\n:::\n\n\nFunctions have **options** that can change their behaviour.\nSeparate options using a comma:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nround(x, digits = 1) # round to one decimal point\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 53.3\n```\n\n\n:::\n:::\n\n\n\n### Vector\n\nA **vector** is the most basic type of object in R. \nIt is a collection of values, which are _all of the same type_, for example numeric, character or logical (TRUE/FALSE).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx_chr <- c(\"dog\", \"cat\", \"goldfish\") # character vector\nx_num <- c(1, 5, 23.3, 55.2) # numeric vector\nx_log <- c(TRUE, TRUE, FALSE, TRUE) # logical vector\n```\n:::\n\n\n**Access values** inside the vector with `[]`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx_chr[2] # the second value\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"cat\"\n```\n\n\n:::\n\n```{.r .cell-code}\nx_chr[c(2, 3)] # the second and third values\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"cat\" \"goldfish\"\n```\n\n\n:::\n:::\n\n\n\n### Conditions\n\nIn many situations (for example to filter rows in a table), it's useful to evaluate a set of conditions. \nWe can create logical vectors using **conditions**:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx_num\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1.0 5.0 23.3 55.2\n```\n\n\n:::\n\n```{.r .cell-code}\n# is x_num greater than 20?\nx_num > 20\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] FALSE FALSE TRUE TRUE\n```\n\n\n:::\n\n```{.r .cell-code}\n# is x_num equal to 5?\nx_num == 5\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] FALSE TRUE FALSE FALSE\n```\n\n\n:::\n\n```{.r .cell-code}\n# is x_num contained the vector on the right?\nx_num %in% c(20, 30, 1)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE FALSE FALSE FALSE\n```\n\n\n:::\n:::\n\n\nCombine conditions with & (AND) and | (OR):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx_num\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1.0 5.0 23.3 55.2\n```\n\n\n:::\n\n```{.r .cell-code}\n# is x_num greater than or equal to 10 AND smaller than or equal to 30?\nx_num >= 10 & x_num <= 30\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] FALSE FALSE TRUE FALSE\n```\n\n\n:::\n\n```{.r .cell-code}\n# is x_num smaller than 10 OR greater than 30?\nx_num < 10 | x_num > 30\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE TRUE FALSE TRUE\n```\n\n\n:::\n:::\n\n\nTo set the filtering conditions, several _relational operators_ can be used:\n\n* `==` is equal to\n* `!=` is different from\n* `%in%` is contained in\n* `>` is greater than\n* `>=` is greater than or equal to\n* `<` is less than\n* `<=` is less than or equal to\n\nIt is also possible to combine several conditions together using the following _logical operators_:\n\n* `&` AND\n* `|` OR\n\n\n### Missing Values\n\nSometimes we have **missing values** in our data, which are encoded as `NA`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ny <- c(23, 44, NA, 212)\n```\n:::\n\n\nWe need to ensure these are dealt with properly\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# returns NA\nmean(y)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] NA\n```\n\n\n:::\n\n```{.r .cell-code}\n# removes NA and then calculates mean\nmean(y, na.rm = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 93\n```\n\n\n:::\n:::\n\n\nThe `is.na()` function is important to deal with missing values:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ny\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 23 44 NA 212\n```\n\n\n:::\n\n```{.r .cell-code}\n# create a logical that is true if value is missing\nis.na(y)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] FALSE FALSE TRUE FALSE\n```\n\n\n:::\n\n```{.r .cell-code}\n# Negate that expression using !\n!is.na(y)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE TRUE FALSE TRUE\n```\n\n\n:::\n:::\n\n\n\n### Tables: `data.frame`/`tibble`\n\nTables in R are called **data.frame**.\nThe `tidyverse` package has its own version of a data.frame called a `tibble`. \nFor the most part they are basically equivalent, but the `tibble` object has a nicer printing function to display our data on the console. \n\nAs an example for working with tables in R, let's read a TSV (tab-delimited) file that contains intervals of missing information in 5 SARS-CoV-2 consensus sequences (this data comes from the [Switzerland case study](../03-case_studies/01-switzerland.md)).\nTo import a TSV file into R as a data.frame we can use the function `read_tsv()` (for a CSV file we would use `read_csv()`):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals <- read_tsv(\"missing_intervals.tsv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nRows: 132 Columns: 6\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \"\\t\"\nchr (4): seqID, patternName, pattern, strand\ndbl (2): start, end\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n\n\n:::\n:::\n\n\nWhen we read the table in, we get a message informing us of the column types found. \nIn this case we have character columns containing text (indicated by `chr`) and numeric columns (indicated by `dbl`, which refers to the [double-precission floating point format](https://en.wikipedia.org/wiki/Double-precision_floating-point_format) that computers use to store numbers). \n\nTo see the content of the table you can type the name of the object: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 132 × 6\n seqID patternName pattern strand start end\n \n 1 CH01 N+ N+ + 1 54\n 2 CH01 N+ N+ + 1193 1264\n 3 CH01 N+ N+ + 4143 4322\n 4 CH01 N+ N+ + 6248 6294\n 5 CH01 N+ N+ + 7561 7561\n 6 CH01 N+ N+ + 9243 9311\n 7 CH01 N+ N+ + 10367 10367\n 8 CH01 N+ N+ + 11361 11370\n 9 CH01 N+ N+ + 13599 13613\n10 CH01 N+ N+ + 16699 16758\n# ℹ 122 more rows\n```\n\n\n:::\n:::\n\n\nSometimes a more convenient way is to click the name of the table on the environment, which opens a new tab to preview your data. \n\n![Data viewer in RStudio.](images/rstudio_data_viewer.svg)\n\n\n## Data Manipulation\n\nMost of the work you will do in R is with tables of data (`data.frame`/`tibble` objects). \nThere are several ways to manipulate tables in R, but we will give a quick overview of the functionality available through the `tidyverse` collection of packages. \n\n### Basic \"verbs\"\n\nThere's a set of basic functions that can be thought of as \"data manipulation verbs\". \nThey are: \n\n- `mutate()` → add a new column of modify an existing one.\n- `select()` → select columns from the table.\n- `filter()` → subset the rows from the table that fullfill a certain logical condition.\n\nHere are some examples of each: \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a new column with the missing interval lengths\nmutate(missing_intervals, \n length = (end - start) + 1)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 132 × 7\n seqID patternName pattern strand start end length\n \n 1 CH01 N+ N+ + 1 54 54\n 2 CH01 N+ N+ + 1193 1264 72\n 3 CH01 N+ N+ + 4143 4322 180\n 4 CH01 N+ N+ + 6248 6294 47\n 5 CH01 N+ N+ + 7561 7561 1\n 6 CH01 N+ N+ + 9243 9311 69\n 7 CH01 N+ N+ + 10367 10367 1\n 8 CH01 N+ N+ + 11361 11370 10\n 9 CH01 N+ N+ + 13599 13613 15\n10 CH01 N+ N+ + 16699 16758 60\n# ℹ 122 more rows\n```\n\n\n:::\n\n```{.r .cell-code}\n# select only a few columns of the table\nselect(missing_intervals,\n seqID, start, end)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 132 × 3\n seqID start end\n \n 1 CH01 1 54\n 2 CH01 1193 1264\n 3 CH01 4143 4322\n 4 CH01 6248 6294\n 5 CH01 7561 7561\n 6 CH01 9243 9311\n 7 CH01 10367 10367\n 8 CH01 11361 11370\n 9 CH01 13599 13613\n10 CH01 16699 16758\n# ℹ 122 more rows\n```\n\n\n:::\n\n```{.r .cell-code}\n# subset the table to include only intervals within the Spike protein\nfilter(missing_intervals,\n start > 21563 & end < 25384)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 15 × 6\n seqID patternName pattern strand start end\n \n 1 CH01 N+ N+ + 21621 21670\n 2 CH01 N+ N+ + 23480 23507\n 3 CH02 N+ N+ + 21620 21670\n 4 CH02 N+ N+ + 23480 23508\n 5 CH03 N+ N+ + 21621 21670\n 6 CH03 N+ N+ + 23480 23507\n 7 CH04 N+ N+ + 21620 21670\n 8 CH04 N+ N+ + 22240 22240\n 9 CH04 N+ N+ + 23480 23509\n10 CH04 N+ N+ + 24062 24395\n11 CH05 N+ N+ + 21620 22530\n12 CH05 N+ N+ + 23489 24709\n13 CH06 N+ N+ + 21605 22515\n14 CH06 N+ N+ + 23474 23525\n15 CH06 N+ N+ + 23721 24694\n```\n\n\n:::\n:::\n\n\n\n### Pipes\n\nWe can chain multiple commands together using **pipes** (similarly to pipes in _Unix_). \nIn R the pipe is represented by `|>` (or `%>%`). \nThe way the pipe works is that the output of one function is sent as the input to the next function. \n\nTaking the examples from the previous section, we could chain all those commands like this: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals |> \n mutate(length = (end - start) + 1) |> \n select(seqID, start, end, length)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 132 × 4\n seqID start end length\n \n 1 CH01 1 54 54\n 2 CH01 1193 1264 72\n 3 CH01 4143 4322 180\n 4 CH01 6248 6294 47\n 5 CH01 7561 7561 1\n 6 CH01 9243 9311 69\n 7 CH01 10367 10367 1\n 8 CH01 11361 11370 10\n 9 CH01 13599 13613 15\n10 CH01 16699 16758 60\n# ℹ 122 more rows\n```\n\n\n:::\n:::\n\n\nIf you want to _update_ the `missing_intervals` table, then you need to use `<-` at the beggining of the chain of pipes: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals <- missing_intervals |> \n mutate(length = (end - start) + 1) |> \n select(seqID, start, end, length)\n```\n:::\n\n\n### Grouped Summaries\n\nWe can calculate summaries of the data (e.g. mean, standard deviation, maximum, minimum) per group (e.g. per sample) using a pair of functions together. \nFor example: \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# mean and maximum interval length per sample\nmissing_intervals |> \n # for each sample\n group_by(seqID) |> \n # calculate summary statistics\n summarise(max_length = max(length),\n min_length = min(length),\n mean_length = mean(length))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 7 × 4\n seqID max_length min_length mean_length\n \n1 CH01 238 1 57 \n2 CH02 274 2 66 \n3 CH03 279 10 70.4\n4 CH04 1011 1 178. \n5 CH05 1221 11 200 \n6 CH06 974 1 166. \n7 CH07 5130 1 343. \n```\n\n\n:::\n:::\n\n\nAs before, if we wanted to save this result in a new object, we would need to use `<-`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nintervals_summary <- missing_intervals |> \n # for each sample\n group_by(seqID) |> \n # calculate summary statistics\n summarise(max_length = max(length),\n min_length = min(length),\n mean_length = mean(length)) |> \n # and rename the seqID column\n rename(sample = seqID)\n```\n:::\n\n\nNotice in this case we also renamed the column called `seqID` to be named `sample` instead (this will be useful for the exercise later on). \n\nAnother useful function is `count()`, which counts how many times the values in a column appear on a data frame. \nFor example, if we wanted to know how many missing intervals each sample had, we could do it like this: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals |> \n count(seqID)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 7 × 2\n seqID n\n \n1 CH01 16\n2 CH02 19\n3 CH03 16\n4 CH04 21\n5 CH05 15\n6 CH06 16\n7 CH07 29\n```\n\n\n:::\n:::\n\n\n:::{.callout-exercise}\n\nModify the following code: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals |> \n count(seqID)\n```\n:::\n\n\nTo also: \n\n- rename the column `seqID` to be named `sample` instead. \n- save the output in an object called `intervals_count`. \n\n:::{.callout-answer}\nTo rename the column, we could use the `rename()` function: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmissing_intervals |> \n count(seqID) |> \n rename(sample = seqID)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 7 × 2\n sample n\n \n1 CH01 16\n2 CH02 19\n3 CH03 16\n4 CH04 21\n5 CH05 15\n6 CH06 16\n7 CH07 29\n```\n\n\n:::\n:::\n\n\nAnd to save this output to a new object, we need to use `<-`: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nintervals_count <- missing_intervals |> \n count(seqID) |> \n rename(sample = seqID)\n```\n:::\n\n\n:::\n:::\n\n### Joining Tables\n\nWe can join multiple tables together based on a common identifier. \nThere are different types of join operations, depending on what we want to achieve. \n\nTake these two tables as an example (these tables come pre-loaded with `tidyverse`):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nband_members\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 2\n name band \n \n1 Mick Stones \n2 John Beatles\n3 Paul Beatles\n```\n\n\n:::\n\n```{.r .cell-code}\nband_instruments\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 2\n name plays \n \n1 John guitar\n2 Paul bass \n3 Keith guitar\n```\n\n\n:::\n:::\n\n\nHere are some different ways we can join these tables:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# keep all records from both tables\nfull_join(band_members, band_instruments, by = \"name\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n name band plays \n \n1 Mick Stones \n2 John Beatles guitar\n3 Paul Beatles bass \n4 Keith guitar\n```\n\n\n:::\n\n```{.r .cell-code}\n# keep all records from the first table\nleft_join(band_members, band_instruments, by = \"name\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n name band plays \n \n1 Mick Stones \n2 John Beatles guitar\n3 Paul Beatles bass \n```\n\n\n:::\n\n```{.r .cell-code}\n# keep all records from the second table\nright_join(band_members, band_instruments, by = \"name\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n name band plays \n \n1 John Beatles guitar\n2 Paul Beatles bass \n3 Keith guitar\n```\n\n\n:::\n\n```{.r .cell-code}\n# keep only the records occurring in both tables\ninner_join(band_members, band_instruments, by = \"name\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 3\n name band plays \n \n1 John Beatles guitar\n2 Paul Beatles bass \n```\n\n\n:::\n:::\n\n\nIn each case, if there was no match between the two tables, the cells are filled with missing values `NA`. \n\n:::{.callout-exercise}\n\nIn this exercise we will join the `intervals_summary` table we created earlier, with the metadata table that contains information about our samples. \n\n1. Read the your metadata table `sample_info.csv` into R and save it as an object called `sample_info`. \n

HintRemember that you can use the `read_csv()` function to read CSV files into R.
\n2. Join the `sample_info` table that you just imported with the `intervals_summary` table we created earlier. \n Save the output to the `intervals_summary` table (this will update the table). \n
You can use the `left_join()` function to achieve this, using the \"sample\" column as the identifier column used to join the two tables.
\n\n:::{.callout-answer}\n\n**Answer 1**\n\nTo read the metadata CSV file, we use the `read_csv()` function, and use `<-` to save the output in an object called `sample_info`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsample_info <- read_csv(\"sample_info.csv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nRows: 7 Columns: 4\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (3): sample, country, sequencing_instrument\ndate (1): collection_date\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n\n\n:::\n:::\n\n\nWe can see the content of our file by typing its name in the console.\nHere is the file in our example data (yours might look different):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsample_info\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 7 × 4\n sample collection_date country sequencing_instrument\n \n1 CH01 2021-11-09 Switzerland GridION \n2 CH04 2021-12-20 Switzerland GridION \n3 CH05 2021-12-20 Switzerland GridION \n4 CH06 2021-12-20 Switzerland GridION \n5 CH07 2021-12-20 Switzerland GridION \n6 CH02 2021-12-21 Switzerland GridION \n7 CH03 2021-12-22 Switzerland GridION \n```\n\n\n:::\n:::\n\n\n**Answer 2**\n\nTo join the two tables together, we can use one of the `*_join()` functions. \nIn this case it doesn't matter which function we use, because both tables have the same sample IDs. \nBut, for example, let's say we only wanted to retain the samples that are in common across _both_ tables. \nIn that case, we would use `inner_join()`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nintervals_summary <- inner_join(intervals_summary, sample_info, by = \"sample\")\n```\n:::\n\n\n:::\n:::\n\n\n## Data Visualisation\n\nFor this section, we will use another table, which contains some of the metrics that we can collect from our consensus pipeline: \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# read the table\nqc_metrics <- read_tsv(\"consensus_metrics.tsv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nRows: 7 Columns: 6\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \"\\t\"\nchr (3): sample, qc_status, lineage\ndbl (3): n_mapped_reads, median_depth, pct_missing\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n\n\n:::\n\n```{.r .cell-code}\n# preview the table\nqc_metrics\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 7 × 6\n sample n_mapped_reads median_depth pct_missing qc_status lineage\n \n1 CH01 43900 248 3.05 good BA.1 \n2 CH02 42841 159 4.20 good BA.1 \n3 CH03 40079 160 3.77 good BA.1 \n4 CH04 50902 164 12.5 bad BA.1 \n5 CH05 32020 133 10.0 bad BA.1 \n6 CH06 46277 177 8.87 mediocre BA.1.1 \n7 CH07 15867 41 33.2 bad None \n```\n\n\n:::\n:::\n\n\nWe can build plots from our tables using the `ggplot2` package (which is also part of the `tidyverse`). \n\nTo build a _ggplot_, we usually need at least three things: \n\n- The data frame we want to use for the plot (the **data**).\n- The columns we want to visualise as our x-axis, y-axis, and colours (these are called **aesthetics**).\n- The type of shape that we want to plot (these are called **geometries**).\n\nFor example, let's try to make a plot to show the relationship between total number of counts and the median depth of sequencing in these samples:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(data = qc_metrics, aes(x = n_mapped_reads, y = median_depth))\n```\n\n::: {.cell-output-display}\n![](quick_r_files/figure-html/unnamed-chunk-32-1.png){width=672}\n:::\n:::\n\n\nWhen we do this, we simply get an empty plot, with x and y axis, but nothing drawn on it. \nTo draw something on the plot, we add (literally with `+`) geometries to our plot. \nIn this case, we can use the `geom_point()` geometry, which draws \"points\" on the plot:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(data = qc_metrics, aes(x = n_mapped_reads, y = median_depth)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](quick_r_files/figure-html/unnamed-chunk-33-1.png){width=672}\n:::\n:::\n\n\nThere are many geometries available with ggplot: \n\n- `geom_point()` draws points. \n- `geom_boxplot()` draws a boxplot. \n- `geom_histogram()` draws a histogram. \n- `geom_col()` draws a barplot (this one is named a little strangely, but \"col\" means it draws \"columns\" or bars).\n\nWe can further modify the look to the plot by adding other _aesthetics_ such as the colour of the points. \nFor example, let's say we wanted to colour our points according to their \"QC Status\":\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(data = qc_metrics, \n aes(x = n_mapped_reads, y = median_depth, colour = qc_status)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](quick_r_files/figure-html/unnamed-chunk-34-1.png){width=672}\n:::\n:::\n\n\nFinally, we may sometimes want to change the _labels_ of our plot. \nIn that case, we can add the `labs()` function to our plotting code: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(data = qc_metrics, \n aes(x = n_mapped_reads, y = median_depth, colour = qc_status)) +\n geom_point() +\n labs(x = \"Number Mapped Reads\", \n y = \"Median Sequencing Depth\", \n colour = \"Nextclade QC Status\")\n```\n\n::: {.cell-output-display}\n![](quick_r_files/figure-html/unnamed-chunk-35-1.png){width=672}\n:::\n:::\n\n\n:::{.callout-exercise}\n\nModify the plot we just did, to show the relationship between median depth of sequencing (x-axis) and percentage of missing bases (y-axis). \nColour the points according to the lineage column. \n\n:::{.callout-answer}\n\nHere is the modified code:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(data = qc_metrics, \n aes(x = median_depth, y = pct_missing, colour = lineage)) +\n geom_point() +\n labs(x = \"Median Sequencing Depth\", \n y = \"% Missing Bases\", \n colour = \"Pango Lineage\")\n```\n\n::: {.cell-output-display}\n![](quick_r_files/figure-html/unnamed-chunk-36-1.png){width=672}\n:::\n:::\n\n\nThe things we have changed were the aesthetics and labels. \n\n:::\n:::\n", + "supporting": [ + "quick_r_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/_freeze/materials/appendices/quick_r/figure-html/unnamed-chunk-32-1.png b/_freeze/materials/appendices/quick_r/figure-html/unnamed-chunk-32-1.png new file mode 100644 index 0000000000000000000000000000000000000000..0453b35c815d4666f6c3dac15c12841945bd42b5 GIT binary patch literal 16790 zcmeHuXIPV2*RCN0jM7F?X#$R@h*DIV^f-u81Z;>HAP6c&iV9LfNtCgH$S7^7iBtsv zkrH|Y1VjWv#{h{+NdkrdNk9Tgz9)jnyffE1-}`>&&pF2*T-RnN`^nyGt$nY1mFNBm 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-::: - -**Learning Goals:** +::: {.callout-tip} +#### Learning Objectives - Recognise the uses of genomic surveillance to inform public health actions during a pandemic. -- Assemble high-quality SARS-CoV-2 genome sequences from raw sequencing data. +- Assemble high-quality _SARS-CoV-2_ genome sequences starting with raw sequencing data from clinical isolates. - Assign consensus sequences to lineages and identify variants of interest/concern. - Capture high-quality metadata, recognising its impact on downstream analyses. - Construct phylogenetic trees to contextualise new samples in a set of background samples. -- Produce visualisations to communicate these findings and help inform public health action. - -**Learning Objectives:** - -By the end of this course, learners should be able to: - -- Enumerate examples of how the genomic surveillance of SARS-CoV-2 has impacted public health decisions during the ongoing pandemic. -- Contrast the different technologies and protocols commonly used for SARS-CoV-2 sequencing, including the pros and cons of each. -- Understand the importance and uses of metadata such as geolocation, date of collection and protocols used. -- Use the Unix command line to navigate a filesystem, manipulate files, launch programs and write scripts for reproducible analysis. -- Recognise the structure of common sequence file formats in bioinformatics and run basic quality control tools on them. -- Summarise the steps in the bioinformatic pipeline used for assembling SARS-CoV-2 genomes from high-throughput amplicon sequencing. -- Apply the `nf-core/viralrecon` _Nextflow_ pipeline to generate a consensus sequence from Illumina and Nanopore data. -- Assess the quality of the consensus sequences and identify high-quality sequences suitable for downstream analyses and submission to public databases. -- Assign sequences to lineages and variants using `pangolin` and `nextclade`. -- Interactively explore data and produce visualisations using _Nextstrain_ tools. -- Manage and install bioinformatics software using _Conda_ and set up _Nextflow_ workflows. +- Estimate variant frequencies from mixed wastewater samples. +- Produce visualisations to communicate your findings and help inform public health action. +::: -## Target Audience +### Target Audience -This course is aimed at life scientists interested in the bioinformatic analysis of SARS-CoV-2 genomic data. -In particular it will benefit those working in SARS-CoV-2 sequencing facilities, such as public health authority labs. -Those with prior experience in bioinformatics may also benefit from the later sessions of the course, which cover specific tools for SARS-CoV-2 (earlier sessions of the course cover basic skills such as the Unix command line and could be skipped by experienced bioinformaticians). +These materials are aimed at life scientists and molecular lab technicians interested in the bioinformatic analysis of viral genomic data. +In particular, it will benefit those working in SARS-CoV-2 sequencing facilities, such as public health labs. -## Prerequisites +### Prerequisites We assume no prior bioinformatics experience or experience with the tools introduced in this course. An elementary knowledge of molecular and viral biology is assumed (concepts such as: DNA, RNA, PCR, primers, SNPs). - - -## Acknowledgements - -These materials have been developed as a collaboration between the [Bioinformatics Training Facility](https://bioinfotraining.bio.cam.ac.uk/) at the University of Cambridge and the [New Variant Assessment Platform (NVAP)](https://www.gov.uk/guidance/new-variant-assessment-platform) program from the _UK Health Security Agency_. - -Our partners also include [COG Train](https://www.cogconsortium.uk/cog-train/about-cog-train/). -We also thank the wider community for publicly sharing training resources, including: +## Citation -- The [workshop video series from _CLIMB BIG DATA_](https://www.youtube.com/channel/UCdiGIIyryQL3x-Og5uiY1rw). -- The [Carpentries](https://carpentries.org/) project, in particular for their [Unix Shell](https://swcarpentry.github.io/shell-novice/) lesson, which we adapted for this workshop. + +Please cite these materials if: -## Citation & Authors +- You adapted or used any of them in your own teaching. +- These materials were useful for your research work. For example, you can cite us in the methods section of your paper: "We carried our analyses based on the recommendations in Tavares et al (2022).". -You can cite these materials as: +You can reference these materials as: -Tavares H, Kumar A, Castle M & UKHSA New Variant Assessment Platform Team (2022) "cambiotraining/sars-cov-2-genomics: Introduction to Sars-CoV-2 Genomics", https://cambiotraining.github.io/sars-cov-2-genomics +> Tavares H, Salehe B, Kumar A, Castle M & UKHSA New Variant Assessment Platform Team (2022) "cambiotraining/sars-cov-2-genomics: Introduction to Sars-CoV-2 Genomics", https://cambiotraining.github.io/sars-cov-2-genomics Or, in BibTeX format: ``` @Misc{, - author = {Tavares, Hugo and Kumar, Ankit and Castle, Matt and UKHSA New Variant Assessment Platform Team}, + author = {Tavares, Hugo and Salehe, Bajuna and Kumar, Ankit and Castle, Matt and UKHSA New Variant Assessment Platform Team}, title = {cambiotraining/sars-cov-2-genomics: Introduction to Sars-CoV-2 Genomics}, month = {March}, year = {2022}, @@ -106,6 +70,7 @@ The contributing members from University of Cambridge Bioinformatics Training Fa - Matt Castle, Bioinformatics Training Manager - Hugo Tavares, Senior Teaching Associate +- Bajuna Salehe, Teaching Associate - Ankit Kumar, Teaching Assistant The UKHSA's NVAP Team members that supported these materials are: @@ -117,3 +82,15 @@ The UKHSA's NVAP Team members that supported these materials are: - Sam Sims, Bioinformatician - Kate Edington, Bioinformatician - Constantina Laou, Specialist Lab Advisor + + +## Acknowledgements + +These materials have been developed as a collaboration between the [Bioinformatics Training Facility](https://bioinfotraining.bio.cam.ac.uk/) at the University of Cambridge and the [New Variant Assessment Platform (NVAP)](https://www.gov.uk/guidance/new-variant-assessment-platform) program from the _UK Health Security Agency_. + +Our partners also include [COG Train](https://www.cogconsortium.uk/cog-train/about-cog-train/). +We also thank the wider community for publicly sharing training resources, including: + +- The [workshop video series from _CLIMB BIG DATA_](https://www.youtube.com/channel/UCdiGIIyryQL3x-Og5uiY1rw). +- The [Carpentries](https://carpentries.org/) project, in particular for their [Unix Shell](https://swcarpentry.github.io/shell-novice/) lesson, which we adapted for this workshop. + diff --git a/01-intro.md b/materials/01-intro/01-surveillance.md similarity index 97% rename from 01-intro.md rename to materials/01-intro/01-surveillance.md index cf92026..c30a014 100644 --- a/01-intro.md +++ b/materials/01-intro/01-surveillance.md @@ -1,19 +1,11 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# SARS-CoV-2 Genomic Surveillance +# SARS-CoV-2 genomic surveillance -:::highlight - -**Questions** - -- How can genomic surveillance help during a pandemic? -- What protocols and technologies are used for SARS-Cov-2 genome sequencing? -- What factors should be considered when collecting samples for sequencing? -- What are some of the main SARS-CoV-2 data repositories and projects? - -**Learning Objectives** +:::{.callout-tip} +#### Learning Objectives - Enumerate some examples of how the genomic surveillance of SARS-CoV-2 has impacted public health decisions during the ongoing pandemic. - Describe what a variant of concern (VOC) is and how it differs from a variant of interest (VOI). @@ -24,10 +16,10 @@ pagetitle: "SARS-CoV-2 Genomics" ::: -:::note +:::{.callout-note} +#### Slides This section has an accompanying slide deck. - ::: ## What is SARS-CoV-2? @@ -88,11 +80,10 @@ In addition to the WHO variant nomenclature, there are three main projects that - The [Pango nomenclature](https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/) is based on the phylogeny of SARS-CoV-2 and defined as groups of sequences that share a common ancestor and a distinctive sequence feature (for example, all share the same single nucleotide change). This nomenclature is also sometimes referred as "[Rambaut et al. 2020](https://doi.org/10.1038/s41564-020-0770-5)" after the respective publication. - The [Nextstrain nomenclature](https://nextstrain.org/blog/2020-06-02-SARSCoV2-clade-naming) is slightly more informal than the above, its main purpose being to facilitate public health discussions. Despite this, it is still informed by the phylogenetic placement of sequences in a tree and therefore has a large overlap with the _Pango_ nomenclature. -We will learn more about how sequences are classified into _lineages_ and _variants of concern_ in the section about [Lineage Assignment](05-lineage_assignment.html). +We will learn more about how sequences are classified into _lineages_ and _variants of concern_ in the section about [Lineage Assignment](02-lineages.md). -:::note - -**What is the difference: strain, lineage, clade, variant?** +:::{.callout-note} +#### What is the difference: strain, lineage, clade, variant? These terms are sometimes used interchangeably in informal conversations about the different forms of SARS-CoV-2. For our purposes, these are the definitions we will use: @@ -109,18 +100,7 @@ Since a phylogenetic tree is inherently hierarchical, there isn't always a clear Finally, the term **variant** is usually used to refer to WHO's _variants of interest_ or _variants of concern_ (e.g. the Alpha, Delta and Omicron variants). Variants are distinct from each other by the _combination of all sequence changes_ in their genomes. -The term "variant" can be ambiguous when used in the field of bioinformatics, and we return to this in the section about [Lineage Assigment](05-lineage_assignment.md) - -::: - - -:::exercise - -- Looking through the [WHO variants page](https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/), can you find what the difference is between a _Variant of Interest_ (VOI) and a _Variant of Concern_ (VOC)? -- Can you find the correspondence between the VOCs and their respective lineages in other classification systems (GISAID, Nextstrain and Pango)? -- Go to the [outbreak.info](https://outbreak.info/location-reports) website and search for a country of your choice (for example, your country of origin or where you live). -- How many sequences are available from the last 360 days? (Note: by default only the last 60 days are shown. You can change this in the text box found on the right of the report page.) -- What were the most common lineages of the virus in circulation in the last 360 days? Do you notice sharp changes in the frequency of WHO _Variants of Concern_? +The term "variant" can be ambiguous when used in the field of bioinformatics, and we return to this in the section about [Lineage Assigment](02-lineages.md) ::: @@ -134,8 +114,8 @@ For example, the [outbreak.info](https://outbreak.info/location-reports) website At the end of this course you too will be able to contribute to this database, by producing full genome sequences assembled from sequencing data. -:::note -**Create a GISAID account** +:::{.callout-note} +#### Create a GISAID account Go to the [GISAID registration page](https://www.gisaid.org/registration/register/) and create an account, so you can gain access to the data stored in GISAID as well as the ability to submit your own sequences in the future. @@ -176,8 +156,8 @@ Besides amplicon sequencing, other methods can also be used to obtain SARS-Cov-2 Despite these alternative methods, _amplicon sequencing_ remains one of the most popular methods for large-scale viral surveillance due to its **low cost** and **high-throughput**. The data generated from this method will be the focus of this course. -:::note -**Illumina or Nanopore?** +:::{.callout-note} +#### Illumina or Nanopore? Both of these sequencing platforms can be used to sequence amplicon samples. @@ -190,8 +170,8 @@ However, they require substantial upfront cost to setup and equip in the lab and ::: -:::note -**Alternative Amplicon Sequencing Protocols** +:::{.callout-note} +#### Alternative Amplicon Sequencing Protocols Although the ARTIC protocol is one of the most popular used in routine SARS-CoV-2 sequencing, there are alternative sets of primers and protocols available. @@ -247,7 +227,34 @@ For example, epidemiological analysis will require individual-level metadata ("p This is the general advice when it comes to metadata collection: **record as much information about each sample as possible**! -:::exercise + +## SARS-CoV-2 Bioinformatics + +What bioinformatic skills do we need in order to analyse SARS-CoV-2 genome sequencing data? +While there are several software tools that have been specifically developed for SARS-CoV-2 analysis (and we will see some of them in this course), there is a set of foundational skills that are applicable to any bioinformatics application: + +- The use of the **Unix command line**. Linux is the most common operating system for computational work and most of the bioinformatic software only runs on it. +- Getting familiar with common **file formats in bioinformatics**. This includes files to store nucleotide sequences, sequence alignments to a reference genome, gene annotations, phylogenetic trees, amongst others. +- Understand software tools' **documentation** and how to configure different options when running our analyses. + +We will turn to these topics in the following sessions. + + +## Exercises + +:::{.callout-exercise} +#### WHO Variants + +- Looking through the [WHO variants page](https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/), can you find what the difference is between a _Variant of Interest_ (VOI) and a _Variant of Concern_ (VOC)? +- Can you find the correspondence between the VOCs and their respective lineages in other classification systems (GISAID, Nextstrain and Pango)? +- Go to the [outbreak.info](https://outbreak.info/location-reports) website and search for a country of your choice (for example, your country of origin or where you live). +- How many sequences are available from the last 360 days? (Note: by default only the last 60 days are shown. You can change this in the text box found on the right of the report page.) +- What were the most common lineages of the virus in circulation in the last 360 days? Do you notice sharp changes in the frequency of WHO _Variants of Concern_? + +::: + +:::{.callout-exercise} +#### Metadata Open the folder under "Course_Materials > 01-Unix > metadata", where you will find several CSV files with information about samples that were sequenced and will be analysed by us later in the course. Double-click to open one of these files (any of them is fine), which should open them on a spreadsheet software (on our training machines it is LibreOffice, but Excel would also work). @@ -259,7 +266,7 @@ The column names of these files are based on the PHA4GE nomenclature system. - Based on this information, can you match the column names from our sample information table to the fields required by GISAID? - What do you think is the naming convention for the isolate name? How would you adjust this when uploading the data to GISAID? -
Answer +:::{.callout-answer} The first step of this protocol provides with a link to a GitHub repository containing several template files: https://github.com/pha4ge/SARS-CoV-2-Contextual-Data-Specification. @@ -294,27 +301,14 @@ Looking at this spreadhseet, we can also see this explanation about the "Virus N So, in order to upload our data to GISAID we would need to change the name of the virus accordingly. -
- ::: - - -## SARS-CoV-2 Bioinformatics - -What bioinformatic skills do we need in order to analyse SARS-CoV-2 genome sequencing data? -While there are several software tools that have been specifically developed for SARS-CoV-2 analysis (and we will see some of them in this course), there is a set of foundational skills that are applicable to any bioinformatics application: - -- The use of the **Unix command line**. Linux is the most common operating system for computational work and most of the bioinformatic software only runs on it. -- Getting familiar with common **file formats in bioinformatics**. This includes files to store nucleotide sequences, sequence alignments to a reference genome, gene annotations, phylogenetic trees, amongst others. -- Understand software tools' **documentation** and how to configure different options when running our analyses. - -We will turn to these topics in the following sessions. +::: ## Summary -:::highlight -**Key Points** +:::{.callout-tip} +#### Key Points - The sequencing of SARS-CoV-2 genomes has allowed the tracking of new variants throughout the pandemic. - The World Health Organisation defines _Variants of Concern_ as SARS-CoV-2 forms with characteristics of public health concern. This includes increased transmissibility, virulence, disease symptoms or vaccine resistance. diff --git a/03-intro_ngs.md b/materials/01-intro/02-ngs.md similarity index 97% rename from 03-intro_ngs.md rename to materials/01-intro/02-ngs.md index 7beef7c..e1a4930 100644 --- a/03-intro_ngs.md +++ b/materials/01-intro/02-ngs.md @@ -1,18 +1,11 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Introduction to NGS Sequencing Data +# Introduction to NGS -::: highlight - -**Questions** - -- What are the main technologies available for high-throughput sequencing? -- What are some of the common file formats used in bioinformatics? -- How do I assess the quality of Illumina sequencing data? - -**Learning Objectives** +:::{.callout-tip} +#### Learning Objectives - Describe differences between sequencing data produced by Illumina and Nanopore platforms. - Recognise the structure of common file formats in bioinformatics, in particular FASTA and FASTQ files. @@ -23,7 +16,9 @@ pagetitle: "SARS-CoV-2 Genomics" ::: -:::note +:::{.callout-note} +#### Slides + This section has an accompanying slide deck. ::: @@ -71,8 +66,8 @@ This makes it an ideal technology to use in situations where it is not possible One of the bigger challenges in effectively using this technology is to produce sequencing libraries that contain high molecular weight, intact, DNA. Another disadvantage is that, compared to Illumina sequencing, the error rates at higher, at around 5%. -:::note -**Illumina or Nanopore for SARS-CoV-2 sequencing?** +:::{.callout-note} +#### Illumina or Nanopore for SARS-CoV-2 sequencing? Both of these platforms have been widely popular for SARS-CoV-2 sequencing. They can both generate data with high-enough quality for the assembly and analysis of SARS-CoV-2 genomes. @@ -88,7 +83,7 @@ They require less up-front cost allowing getting started with sequencing very qu In this section we will demonstrate two common tasks in sequencing data analysis: sequence quality control and mapping to a reference genome. There are many other tasks involved in analysing sequencing data, but looking at these two examples will demonstrate the principles of running bioinformatic programs. -We will later see how bioinformaticians can automate more complex analyses in the [consensus assembly section](04-consensus.html). +We will later see how bioinformaticians can automate more complex analyses in the [consensus assembly section](../02-isolates/01-consensus.md). One of the main features in bioinformatic analysis is the use of standard file formats. It allows software developers to create tools that work well with each other. @@ -150,62 +145,6 @@ fastqc --outdir PATH_TO_OUTPUT_DIRECTORY PATH_TO_SEQUENCES FastQC can process several samples at once, and often we can use the `*` wildcard to do this. We will see an example of this in the following exercise. -:::exercise - -In the course materials directory `02-ngs/` we have several FASTQ files that we will use to assemble SARS-CoV-2 genomes. -But first, we will run FastQC to check the quality of these files. - -This is the basic command we could use in our samples: - -```bash -fastqc --outdir results/fastqc data/reads/*.fastq.gz -``` - -- Create the output directory for the analysis (`results/fastqc`). -
Hint -The command to create directories is `mkdir`. -By default, the `mkdir` directory only creates one directory at a time. -In this case we need to create first the `results` directory and then the `results/fastqc` within it. -Alternatively, both directories can be created at once using the `-p` option. -
-- Modify the `fastqc` command shown above to add an option to run the analysis using 8 threads in parallel (or CPUs). Check the tool's help (`fastqc --help`) to see what the option to do this is called. -- Run the command. You will know it is running successfully because it prints progress of its analysis on the screen. - - -
Answer - -First, we can create a directory to output our results: - -```bash -mkdir -p results/fastqc -``` - -The `-p` option ensures that both directories are created in one step. -Otherwise, since the parent directory `results` did not exist, `mkdir` would throw an error. - -To check the options available with _FastQC_ we can run `fastqc --help` to get the complete documentation. -As we scroll through the options, we can see the relevant one for running the analysis in parallel: - -``` --t --threads Specifies the number of files which can be processed - simultaneously. Each thread will be allocated 250MB of - memory so you shouldn't run more threads than your - available memory will cope with, and not more than - 6 threads on a 32 bit machine -``` - -Although the documentation is a little technical, this means that if we have multiple CPUs available on our computer, we can set this option to allow multiple files to be processed in parallel. -Our training machines have 8 CPUs, so we can run the command as follows: - -```bash -fastqc -t 8 --outdir results/fastqc data/reads/*.fastq.gz -``` - -The analysis report generated by FastQC is given as a `.html` file (opens in a web browser). -We will go through the details of this below. -
-::: - Each FastQC HTML report contains a section with a different quality assessment plot. Each of these are explained in the online documentation: @@ -225,8 +164,8 @@ For example, looking at the "Per base sequence quality" section for one of our s ![Sequence quality plot from FastQC for one of our samples. The blue line shows the average across all samples. This sample is very high quality as all sequences have quality > 20 across the entire length of the reads.](images/fastqc_quality.png) -:::note -**Quality Control Nanopore Reads** +:::{.callout-note} +#### Quality Control Nanopore Reads Although FastQC can run its analysis on any FASTQ files, it has mostly been designed for Illumina data. You can still run FastQC on basecalled Nanopore data, but some of the output modules may not be as informative. @@ -339,7 +278,7 @@ We have just seen two of these file formats: FASTQ for sequencing reads and BAM Another very common file format is the FASTA file, which is the format that our reference genome is stored as. The consensus sequences that we will generate are also stored as FASTA files. We detail this format below, but there are many other formats. -Check out our page on [Extras → File Formats](106-file_formats.html) to learn more about them. +Check out our appendix page on [File Formats](../appendices/file_formats.md) to learn more about them. ### FASTA Files @@ -390,10 +329,71 @@ In two steps: We will see FASTA files several times throughout this course, so it's important to be familiar with them. +## Exercises + +:::{.callout-exercise} +#### Sequence quality control: FASTQC + +In the course materials directory `02-ngs/` we have several FASTQ files that we will use to assemble SARS-CoV-2 genomes. +But first, we will run FastQC to check the quality of these files. + +This is the basic command we could use in our samples: + +```bash +fastqc --outdir results/fastqc data/reads/*.fastq.gz +``` + +- Create the output directory for the analysis (`results/fastqc`). +
Hint +The command to create directories is `mkdir`. +By default, the `mkdir` directory only creates one directory at a time. +In this case we need to create first the `results` directory and then the `results/fastqc` within it. +Alternatively, both directories can be created at once using the `-p` option. +
+- Modify the `fastqc` command shown above to add an option to run the analysis using 8 threads in parallel (or CPUs). Check the tool's help (`fastqc --help`) to see what the option to do this is called. +- Run the command. You will know it is running successfully because it prints progress of its analysis on the screen. + + +:::{.callout-answer} + +First, we can create a directory to output our results: + +```bash +mkdir -p results/fastqc +``` + +The `-p` option ensures that both directories are created in one step. +Otherwise, since the parent directory `results` did not exist, `mkdir` would throw an error. + +To check the options available with _FastQC_ we can run `fastqc --help` to get the complete documentation. +As we scroll through the options, we can see the relevant one for running the analysis in parallel: + +``` +-t --threads Specifies the number of files which can be processed + simultaneously. Each thread will be allocated 250MB of + memory so you shouldn't run more threads than your + available memory will cope with, and not more than + 6 threads on a 32 bit machine +``` + +Although the documentation is a little technical, this means that if we have multiple CPUs available on our computer, we can set this option to allow multiple files to be processed in parallel. +Our training machines have 8 CPUs, so we can run the command as follows: + +```bash +fastqc -t 8 --outdir results/fastqc data/reads/*.fastq.gz +``` + +The analysis report generated by FastQC is given as a `.html` file (opens in a web browser). +We will go through the details of this below. + +::: +::: + + ## Summary -:::highlight -**Key Points** +:::{.callout-tip} +#### Key Points - Illumina sequencing produces short reads (50bp - 200bp), typically from both ends of a DNA fragment. It is a comparatively cheap sequencing platform which produces very high-quality sequences. - Nanopore sequencing produces very long reads (typically hundreds of kilobases long). It is comparatively more expensive and has higher error rates. However, it is more flexible with some of its platforms being fully portable. diff --git a/materials/01-intro/03-workflows.md b/materials/01-intro/03-workflows.md new file mode 100644 index 0000000..427624f --- /dev/null +++ b/materials/01-intro/03-workflows.md @@ -0,0 +1,11 @@ +--- +pagetitle: "SARS Genomic Surveillance" +--- + +# Bioinformatic workflows + +:::{.callout-warning} + +Under development + +::: \ No newline at end of file diff --git a/materials/01-intro/images b/materials/01-intro/images new file mode 120000 index 0000000..5e67573 --- /dev/null +++ b/materials/01-intro/images @@ -0,0 +1 @@ +../images \ No newline at end of file diff --git a/04-consensus.md b/materials/02-isolates/01-consensus.md similarity index 95% rename from 04-consensus.md rename to materials/02-isolates/01-consensus.md index 459c9fb..f774e4c 100644 --- a/04-consensus.md +++ b/materials/02-isolates/01-consensus.md @@ -1,24 +1,11 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -:::warning -**Update May 2022** +# Consensus assembly -For participants who attended our workshops prior to May 2022, please note that this lesson has been updated to use a more recent _Nextflow_ pipeline. -We recommend switching to the new pipeline introduced here, but if you want to use the older pipeline you can still access the [previous version of the lesson here](04-artic_nextflow.html). -::: - -# Consensus Assembly - -::: highlight - -**Questions** - -- What are the steps involved in assembling SARS-CoV-2 genome from amplicon sequencing (Illumina and Nanopore)? -- How can I do reference-based assembly of SARS-CoV-2 genomes? - -**Learning Objectives** +:::{.callout-tip} +#### Learning Objectives - Recognise what the main steps are in processing raw sequencing data to generate consensus genome sequences, including sequence alignment, primer trimming and consensus generation. - Recognise the differences between Illumina and Nanopore pipelines. @@ -27,14 +14,16 @@ We recommend switching to the new pipeline introduced here, but if you want to u ::: -:::note +:::{.callout-note} +#### Slides + This section has an accompanying slide deck. ::: ## SARS-CoV-2 Consensus Assembly -As we discussed [earlier in the course](01-intro.html), the starting material for sequencing SARS-CoV-2 samples from infected patients is PCR-amplified DNA generated with a panel of primers that covers the whole SARS-CoV-2 genome (for example the primers developed by the ARTIC network). +As we discussed [earlier in the course](../01-intro/01-surveillance.md), the starting material for sequencing SARS-CoV-2 samples from infected patients is PCR-amplified DNA generated with a panel of primers that covers the whole SARS-CoV-2 genome (for example the primers developed by the ARTIC network). This material can then be sequenced using either _Illumina_ or _Nanopore_ platforms. Although different sotware tools are used depending on which kind of sequencing platform was used, the main goal is the same: to align the sequencing reads to the reference genome, and identify any DNA changes (SNPs or Indels) relative to the reference genome (_Wuhan-Hu-1_). @@ -51,7 +40,9 @@ The general data processing steps are: ![Overview of the consensus assembly procedure from amplicon sequencing reads. In this schematic, each read spans the whole length of a PCR amplicon, which is what is expected from Nanopore reads. With Illumina data, there would be two pairs of reads starting at each end of the PCR amplicon.](images/workflow_overview.svg) -:::note +:::{.callout-note} +#### Primer trimming + **Primer trimming** is a key step of the data processing, otherwise SNPs might be missed at the primer sites, on the final consensus sequence. This is because the primer sequence is retained during PCR instead of the original sequence of the sample. Because the PCR amplicons overlap with each other, we can trim the primers from each read and do variant calling after trimming. @@ -97,7 +88,7 @@ These are some of the key advantages of using a standardised workflow for our an - Checkpoint and resume - if a workflow fails in one of the tasks, it can be resumed at a later time. -## SARS-CoV-2 Pipeline {.tabset} +## SARS-CoV-2 Pipeline To generate consensus SARS-CoV-2 genomes from these data, we will use a pipeline that was developed by the _Nextflow_ core team called [`nf-core/viralrecon`](https://nf-co.re/viralrecon) (which was itself inspired by a [previous pipeline from the Connor Lab](https://github.com/connor-lab/ncov2019-artic-nf)). Its objective is to harmonise the assembly of SARS-CoV-2 genomes from both Illumina and Nanopore amplicon sequencing data. @@ -122,14 +113,14 @@ This page includes many details about the pipeline: which tools are used in diff Below, we give an overview of the pipelines used for Illumina and Nanopore amplicon data. -:::note -**Reference Genome and Primer Locations** +:::{.callout-note} +#### Reference Genome and Primer Locations The _Wuhan-Hu-1_ reference genome sequence and the amplicon primer locations (in BED file format) can all be found on the ARTIC [Primer Schemes repository](https://github.com/artic-network/primer-schemes/tree/master/nCoV-2019). The pipeline we are using takes care of downloading these files for us automatically, however it can be useful to know where to find them, in case you want to use other tools that require these files. ::: - +::: {.panel-tabset group="platform"} ### Illumina The Illumina sub-workflow is based on several standard bioinformatic tools and, importantly, on the [iVar](https://andersen-lab.github.io/ivar/html/) software, which was developed for analysing amplicon-based sequencing data. @@ -173,7 +164,7 @@ In summary, the steps performed by the Illumina sub-workflow are: -### Nanopore/medaka (basecalled FASTQ) +### Nanopore (FASTQ) The nanopore sub-workflow is based on the [ARTIC bioinformatics protocol](https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html) and uses several of the tools from the accompanying [`artic` software package](https://artic.readthedocs.io/en/latest/). @@ -223,7 +214,7 @@ In summary, the steps performed by the Medaka sub-workflow are: -### Nanopore/nanopolish (signal-level FAST5) +### Nanopore (FAST5) The nanopore sub-workflow is based on the [ARTIC bioinformatics protocol](https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html) and uses several of the tools from the accompanying [`artic` software package](https://artic.readthedocs.io/en/latest/). @@ -273,16 +264,15 @@ Some of the key options are: - `--fastq_dir` specifies the directory containing the FASTQ files generated by the `guppy_basecaller` program (this is the standard software from Nanopore that processes the raw signal data from the sequencing device). This directory should contain sub-directories for each barcoded sample following the naming convention `barcodeXXXX` (where X is a number between 0 and 9). By default, `guppy_basecaller` generates a folder called "fastq_pass" which follows this convention. - `--fast5_dir` specifies the directory containing the FAST5 files generated by `guppy_basecaller`. This directory follows the same naming convention as above and is usually in a folder called "fast5_pass". - `--sequencing_summary` is a path to the "sequencing_summary.txt" text file generated by `guppy_basecaller`. - -## {.unlisted .unnumbered} +::: Apart from the specific options used by each sub-workflow, there are some general options that are used: -- `--input` specifies a CSV file with details about our samples. The format of this file depends on the specific sub-workflow you are using. See the details in the [samplesheet documentation page](https://nf-co.re/viralrecon/2.4.1/usage#samplesheet-format). +- `--input` specifies a CSV file with details about our samples. The format of this file depends on the specific sub-workflow you are using. See the details in the [samplesheet documentation page](https://nf-co.re/viralrecon/2.6.0/docs/usage#samplesheet-format). - `--outdir` specifies the output directory to store all our results. - `--protocol amplicon` sets the pipeline for PCR amplicon data (the other option is `--protocol metagenomic`, which we do not cover in this course). - `--genome 'MN908947.3'` this is the standard name of the [Wuhan-Hu-1 reference genome](https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3). -- `--primer_set artic` at the moment only "artic" primers are available by default. It is possible to use custom primers with the Illumina workflow (see details [here](https://nf-co.re/viralrecon/2.4.1/parameters#primer_bed)). +- `--primer_set artic` at the moment only "artic" primers are available by default. It is possible to use custom primers with the Illumina workflow (see details [here](https://nf-co.re/viralrecon/2.6.0/parameters#primer_bed)). - `--primer_set_version` the version of the [ARTIC primer scheme](https://github.com/artic-network/primer-schemes) used. The [viralrecon primer config file](https://github.com/nf-core/configs/blob/master/conf/pipeline/viralrecon/genomes.config) indicates the available primer shemes are: `1`, `2`, `3`, `4`, `4.1` and also `1200` (the 1200bp amplicon protocol, also known as "midnight"). - `--skip_assembly` this is used to skip de-novo assembly of the genome. This step is unnecessary in amplicon protocols, which instead rely on mapping reads to the reference genome (reference-based assembly). De-novo assembly is necessary for metagenomic protocols. @@ -290,8 +280,8 @@ There is one more option we used: `-profile`. This is a general `nextflow` optio In our case, we are using a software called _Singularity_, which creates a "virtual operating system" (called a container) where all the necessary software is run from. This ensures that all of the software is automatically installed and runs on any Linux computer. -:::note -**Conda, Singularity, Docker?** +:::{.callout-note} +#### Conda, Singularity, Docker? Generally speaking, workflow management software such as _Nextflow_ or _Snakemake_ support three solutions for managing software dependencies: @@ -347,8 +337,8 @@ Succeeded : 343 You should also get several output files in the results folder specified with our `nextflow` command. We will detail what these files are in the next section. -:::note -**Running the pipeline on our training computers** +:::{.callout-note} +#### Running the pipeline on our training computers Our training computers don't have the high specifications needed for routine bioinformatic analysis, so the _Illumina_ pipeline takes up to 1h to complete. @@ -356,15 +346,15 @@ We provide already pre-processed results for 48 samples in the folder `03-consen ::: -## Output Files {.tabset} +## Output Files After running our pipeline, we get several output directories and files. The directories we get depend on which version of the workflow was used (Illumina or Nanopore). -The description of the output is detailed in the [pipeline documentation](https://nf-co.re/viralrecon/2.4.1/output). +The description of the output is detailed in the [pipeline documentation](https://nf-co.re/viralrecon/2.6.0/docs/output). Although there are many output files, most of these contain results that are aggregated in an interactive _MultiQC_ report, which makes their analysis easier. We highlight some of the main files of interest below. - +:::{.panel-tabset group="platform"} ### Illumina - The file `multiqc/multiqc_report.html` contains a MultiQC quality and analysis report for the consensus assemblies generated by the pipeline. @@ -373,22 +363,20 @@ We highlight some of the main files of interest below. - The file `variants/ivar/variants_long_table.csv` contains a table with the aggregated results of all the variants detected in each sample. - -### Nanopore/medaka +### Nanopore (FASTQ) - The file `multiqc/medaka/multiqc_report.html` contains a MultiQC quality and analysis report for the consensus assemblies generated by the pipeline. - The folder `medaka/` contains: - Individual BAM files (named `*.primertrimmed.rg.sorted.bam`), which can be visualised with _IGV_ if we want to look at the reads mapped to the reference genome. - Individual FASTA files (named `*.consensus.fasta`) for each sample's consensus genome sequence. - A file called `variants_long_table.csv` with a table of all the variants detected in each sample. - -## {.unlisted .unnumbered} +::: ## Quality Control The `viralrecon` pipeline produces many quality control metrics, which are conveniently compiled in an interactive report with _MultiQC_, as mentioned above. -We will not detail here every section of the report (check the [pipeline documentation](https://nf-co.re/viralrecon/2.4.1/output) for a full description), but only highlight some of the sections that can be used for a first assessment of the quality of our samples. +We will not detail here every section of the report (check the [pipeline documentation](https://nf-co.re/viralrecon/2.6.0/docs/output) for a full description), but only highlight some of the sections that can be used for a first assessment of the quality of our samples. ### Variant Calling Metrics @@ -400,20 +388,20 @@ There is also the ability to produce simple scatterplots from the data in this t This table also contains information about the lineage/clade assigned to each sample by the programs _Pangolin_ and _Nextclade_. This gives us an idea of which samples may be more similar to each other, and where they fit in the global context of other sequences available publicly. -We will talk more about this topic in the [lineage assignment section](05-lineage_analysis.html). +We will talk more about this topic in the [lineage assignment section](02-lineages.md). -:::warning -**Lineage Versions** +:::{.callout-warning} +#### Lineage Versions Although the _Viralrecon_ pipeline runs _Pangolin_ and _Nextclade_ to perform lineage assignment, it does not use the latest version of these programs (because lineages evolve so fast, the nomenclature constantly changes). Therefore, this information should mostly be ignored at this stage, and instead we should run our analysis on the most up-to-date versions of these programs. -We will detail this in the [Lineages and Variants](05-lineage_analysis.html) section of the materials. +We will detail this in the [Lineages and Variants](02-lineages.md) section of the materials. ::: ![Snapshot of the "Variant Metrics" section of the `viralrecon` _MultiQC_ report. Simple scatterplots can be made from the data on this table using the Plot button. For example, at the bottom we show a scatterplot showing the relationship between the median depth of coverage and the number of ambiguous bases 'N' per 100kb. For the data in this example, we can see that when the average depth of coverage drops below around 200 reads we start getting higher number of missing bases in the assembly.](images/viralrecon_multiqc_variant_metrics.svg) -:::note -**Terminology Alert!** +:::{.callout-note} +#### Terminology Alert! The word **coverage** is sometimes used in an ambiguous way by bioinformaticians. It can mean two things: @@ -491,8 +479,8 @@ Therefore, to see which mutations are actually present in our consensus sequence Again, we note that this only applies to the Illumina pipeline. For the **Nanopore pipeline** (`--platform nanopore`) all the mutations included in this table are retained in the consensus sequence. -:::warning -**ORF1b Annotation Issues** +:::{.callout-warning} +#### ORF1b Annotation Issues The annotation of the _ORF1b_ gene had some changes between the first original assembly and newer versions of this annotation. In particular, there is a frameshift that is not considered in the annotation used by most software, and this causes a discrepancy in the results between `viralrecon` and other tools such as _Nextclade_ (which we will cover in the next section). @@ -502,8 +490,8 @@ This issue is [under investigation](https://github.com/nf-core/viralrecon/issues For now, the advice is to **ignore the variant effects of ORF1b** as these correspond to an older version of the annotated gene. ::: -:::note -**Keep Original Files Intact** +:::{.callout-note} +#### Keep Original Files Intact When you analyse the results of a CSV file in a spreadsheet software (such as Excel), it may be a good idea to create a copy of your file to avoid accidentally modifying the original table. For example, if you accidentally sort one column of the table but forget to sort other columns, of if you accidentally filter the results and delete some of the rows from the table, etc. @@ -513,7 +501,73 @@ You may want to save the copy as an Excel file format, to include graphs and col (Remember that the CSV format is a plain text file - it does not support graphs or coloured cells.) ::: -:::exercise + +## Cleaning FASTA Files (Optional) + +To proceed with our analysis, we need a FASTA file containing _all_ of our consensus sequences. +However, our `viralrecon` pipeline outputs _separate_ FASTA files for each sample. +We can see this by running (from within the `03-consensus/uk_illumina/` directory): + +```console +$ ls results/viralrecon/variants/ivar/consensus/bcftools/ +``` + +Also, the workflow modifies our original sample names in the FASTA file, by adding extra information to the sequence name. +For example: + +```console +$ head -n 1 results/viralrecon/variants/ivar/consensus/bcftools/ERR5728910.consensus.fa +``` + +``` +>ERR5728910 MN908947.3 +``` + +What we want to do is clean these sample names, so that we end up with: + +``` +>ERR5728910 +``` + +We also want to make sure to combine all the samples into a single FASTA file. + +We can the command-line skills we acquired so far, in particular the use of the `cat` command to combine (or _concatenate_) the individual files and the `sed` command to replace text and clean our sample names. +Let's do this step by step. + +First, we can use the `*` _wildcard_ to combine all the FASTA files with the `cat` command: + +```console +$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa +``` + +Running this command will print all of the sequences on the screen! +To see what happened a little better, we could _pipe_ this command to `less` to browse up-and-down through the file: + +```console +$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa | less +``` + +We could also check that we now have all our samples combined, we could pass the results to `grep` and search for the word `>`, which in the FASTA format indicates the sequence name: + +```console +$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa | grep ">" | wc -l +``` + +This should give us 7 as the result (which makes sense, since we have 7 samples). + +We can now proceed with cleaning the names of the sequences, by using `sed`: + +```bash +cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa | sed 's/ MN908947.3//' > results/viralrecon/clean_sequences.fa +``` + +Notice that in this last command we make sure to redirect the result to a new file using `>`. + + +## Exercises + +:::{.callout-exercise} +#### Running `nf-core/viralrecon`: ONT data Go to the course materials directory `03-consensus/india_nanopore` (on our training machines `cd ~/Course_Materials/03-consensus/india_nanopore`). This contains **Nanopore sequencing** data for several samples collected in India. @@ -526,7 +580,7 @@ We already provide this file in `samplesheet.csv`. - Using `nano`, open the script found in `scripts/run_medaka_workflow.sh`. - Fix the code in the script where you see the word "_FIXME_": - Output the results to a directory called `results/viralrecon/`. - - The input sample sheet is in the file `samplesheet.csv` (check the [pipeline documentation](https://nf-co.re/viralrecon/2.4.1/usage#nanopore-input-format) to review what the format of this samplesheet should be for the Nanopore pipeline). + - The input sample sheet is in the file `samplesheet.csv` (check the [pipeline documentation](https://nf-co.re/viralrecon/2.6.0/usage#nanopore-input-format) to review what the format of this samplesheet should be for the Nanopore pipeline). - The FASTQ files are in a folder `data/fastq_pass`. - Run the script using `bash`. This may take ~15 minutes to complete. 2. While you wait for the pipeline to complete, use the file explorer to open the folder called `preprocessed`, which contains already pre-processed results from a larger run with 48 samples. @@ -544,7 +598,7 @@ We already provide this file in `samplesheet.csv`. - Using IGV, open the BAM file for one of those samples. Note: BAM files are located in `preprocessed/medaka/SAMPLE.primertrimmed.rg.sorted.bam` (were 'SAMPLE' is the sample name). - Go to the location of those mutations. From the read alignment, how confident are you about it? What could you do to confirm it? -
Answer +:::{.callout-answer} **Question 1** @@ -578,7 +632,7 @@ nextflow run nf-core/viralrecon \ What we did to fix the code was: -- Set `--input` as `samplesheet.csv`, which is the CSV file with two columns: sample name and Nanopore barcode number. The format of this file is detailed in the [`nf-core/viralrecon` documentation](https://nf-co.re/viralrecon/2.4.1/usage#samplesheet-format). +- Set `--input` as `samplesheet.csv`, which is the CSV file with two columns: sample name and Nanopore barcode number. The format of this file is detailed in the [`nf-core/viralrecon` documentation](https://nf-co.re/viralrecon/2.6.0/usage#samplesheet-format). - Set `--outdir` as `results/viralrecon/`, which is the directory where we want to output our results. - Set `--fastq_dir` as `data/fastq_pass`, which is the directory containing the FASTQ file folders named as `barcodeXX` (where `XX` is a number). This is the standard output from the _Guppy_ software used to do basecalling of Nanopore data to produce FASTQ files. @@ -612,7 +666,7 @@ For example, in the section "Job Duration" we can see a graph that looks like th This indicates that the step running the `artic minion` tool takes the longest. This is not surprising as this is the step where most of the work is happening (mapping, primer trimming and making a consensus sequence). -You can revise the steps of the workflow in the [respective section above](04-consensus.html#SARS-CoV-2_Workflow) +You can revise the steps of the workflow in the respective section above. ---- @@ -678,74 +732,11 @@ One possibility is that the deletion interfered with the primer efficiency in th Generally, we don't need to confirm every single mutation obtained from our analysis. But if we see a mutation occurring many times, then it may be worth further investigation, especially if it is disruptive and in an important gene such as the Spike gene. -
- +::: ::: - -## Cleaning FASTA Files (Optional) - -To proceed with our analysis, we need a FASTA file containing _all_ of our consensus sequences. -However, our `viralrecon` pipeline outputs _separate_ FASTA files for each sample. -We can see this by running (from within the `03-consensus/uk_illumina/` directory): - -```console -$ ls results/viralrecon/variants/ivar/consensus/bcftools/ -``` - -Also, the workflow modifies our original sample names in the FASTA file, by adding extra information to the sequence name. -For example: - -```console -$ head -n 1 results/viralrecon/variants/ivar/consensus/bcftools/ERR5728910.consensus.fa -``` - -``` ->ERR5728910 MN908947.3 -``` - -What we want to do is clean these sample names, so that we end up with: - -``` ->ERR5728910 -``` - -We also want to make sure to combine all the samples into a single FASTA file. - -We can the command-line skills we acquired so far, in particular the use of the `cat` command to combine (or _concatenate_) the individual files and the `sed` command to replace text and clean our sample names. -Let's do this step by step. - -First, we can use the `*` _wildcard_ to combine all the FASTA files with the `cat` command: - -```console -$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa -``` - -Running this command will print all of the sequences on the screen! -To see what happened a little better, we could _pipe_ this command to `less` to browse up-and-down through the file: - -```console -$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa | less -``` - -We could also check that we now have all our samples combined, we could pass the results to `grep` and search for the word `>`, which in the FASTA format indicates the sequence name: - -```console -$ cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa | grep ">" | wc -l -``` - -This should give us 7 as the result (which makes sense, since we have 7 samples). - -We can now proceed with cleaning the names of the sequences, by using `sed`: - -```bash -cat results/viralrecon/variants/ivar/consensus/bcftools/*.fa | sed 's/ MN908947.3//' > results/viralrecon/clean_sequences.fa -``` - -Notice that in this last command we make sure to redirect the result to a new file using `>`. - - -:::exercise +:::{.callout-exercise} +#### Clean FASTA file: ONT data In this exercise we will create a clean FASTA file for the samples collected in India. These are found in the `03-consensus/india_nanopore` directory, so make sure to change to that directory first (on our training machines you can do: `cd ~/Course_Materials/03-consensus/india_nanopore`) @@ -772,7 +763,7 @@ We want to clean the name of the sequences so that the result is:
HintRemember the syntax for pattern replacement with `sed` is: `sed 's/replace this/with that/'`. Also remember that if you want to replace the character "/", you need to use the special _escape character_, for example: `sed 's/replace \/ slash//'`
-
Answer +:::{.callout-answer} The complete code to achieve the desired outcome is: @@ -785,15 +776,14 @@ This is because `/` alone is used by `sed` to separate different parts of the co Look at the [section about pattern replacement](02-unix-sed.html) for a reminder of how the `sed` command works. -
+::: ::: ## Summary -:::highlight - -**Key Points** +:::{.callout-tip} +#### Key Points - The main steps to generate SARS-CoV-2 consensus sequences are: filter high-quality reads, map reads to reference genome, trim PCR primers and variant/mutation calling, which is finally used to produce a consensus sequence. - Other steps that can be done include annotating the variants/mutations (what their effects in each gene might be) and assigning sequencences to known lineages/clades. @@ -803,6 +793,6 @@ Look at the [section about pattern replacement](02-unix-sed.html) for a reminder - The output of the pipeline includes, among others: - A detailed _MultiQC_ report, including information about genome coverage, the depth of sequencing for each amplicon, as well as other quality metrics that can help to troubleshoot problematic samples. - A table with SNP and indel mutation variants identified in each sample. - - Information about SARS-CoV-2 lineages/clades/variants, which is detailed in the [next section](05-lineage_analysis.html). + - Information about SARS-CoV-2 lineages/clades/variants, which is detailed in the [next section](02-lineages.md). ::: diff --git a/05-lineage_analysis.md b/materials/02-isolates/02-lineages.md similarity index 94% rename from 05-lineage_analysis.md rename to materials/02-isolates/02-lineages.md index 64fd648..6924c09 100644 --- a/05-lineage_analysis.md +++ b/materials/02-isolates/02-lineages.md @@ -1,34 +1,21 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -:::warning -**Update May 2022** +# Lineages and variants -For participants who attended our workshops prior to May 2022, please note that this lesson has been updated to use a more recent _Nextflow_ pipeline. -We recommend switching to the new pipeline introduced here, but if you want to use the older pipeline you can still access the [previous version of the lesson here](05-lineage_assignment.html). -::: - -# Lineage Assignment and Variant Classification - -::: highlight - -**Questions** - -- What are the main variant annotation conventions for SARS-CoV-2? -- How can I assign sequences to lineages and classify them as variants of interest/concern? -- How can I visually explore the results of my variant analysis? - -**Learning Objectives** +:::{.callout-tip} +#### Learning Objectives - Understand variant annotation conventions used by Gisaid, Pango, Nextstrain and WHO and how they relate to each other. - Assign sequences to Pango lineages using `pangolin`. - Interactively explore mutations in the assembled genomes and their phylogenetic context using _Nexstrain_'s tools. - ::: -:::note +:::{.callout-note} +#### Slides + This section has an accompanying slide deck. ::: @@ -67,8 +54,8 @@ _Nextclade_'s [nomenclature system](https://nextstrain.org/blog/2021-01-06-updat On the other hand, _Pangolin_'s [nomenclature system](https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/) is more fine-grained, aiming to follow the dynamics of the pandemic as it unfolds. The two systems are complementary to each other, and our analysis of SARS-CoV-2 sequences should include both tools. -:::note -**What is a variant?** +:::{.callout-note} +#### What is a variant? It is important to note that the term _variant_ can be sometimes ambiguous. @@ -79,7 +66,7 @@ According to this definition, we have two variants in the example below (samples However, in bioinformatic sequence analysis, a **sequence variant** refers to an individual change in the DNA sequence (a SNP or an insertion/deletion). Using this definition, in the example above we have 5 variants: 3 SNPs and 2 indels. -In the [Consensus Sequence](04-consensus.html) section, we saw that one of our workflow steps was "variant calling". +In the [Consensus Sequence](01-consensus.md) section, we saw that one of our workflow steps was "variant calling". This was the definition of variant we were using: identifying individual SNPs and/or indels relative to the reference genome, from our sequencing data. This is also reflected in the one of the common file formats used to store SNP/indel information, the [_VCF_ file](https://en.wikipedia.org/wiki/Variant_Call_Format), which means "Variant Call Format". @@ -157,14 +144,19 @@ Nextclade will show a progress of its analysis at the top of the page, and the r ![Overview of the Nextclade web interface.](images/nextclade_overview.svg) -:::note +:::{.callout-note} +#### Nextclade and data privacy + When using the _Nextclade_ web application, the data does not leave your computer, so privacy concerns are not an issue. ::: -:::exercise +## Exercises + +:::{.callout-exercise} +#### Nextclade -In this exercise we will work with 48 consensus sequences from the UK, processed with the `nf-core/viralrecon` pipeline and covered in the [previous section](04-consensus.html). +In this exercise we will work with 48 consensus sequences from the UK, processed with the `nf-core/viralrecon` pipeline and covered in the [previous section](01-consensus.md). Go to [nextclade.org](https://clades.nextstrain.org/) and load the sequences provided in `03-consensus/uk_illumina/preprocessed/clean_consensus_sequences.fa`. @@ -176,7 +168,7 @@ Go to [nextclade.org](https://clades.nextstrain.org/) and load the sequences pro - Go to the position where this deletion was identified and investigate if it seems clear from the mapped reads. -
Answer +:::{.callout-answer} **Question 1** @@ -215,16 +207,14 @@ If we look at an _Alpha_ variant sample (for example GB43) we can see that this If we thought this sample was crucial for public health investigation, then this would require further investigation by doing a PCR with new primers and Sanger-sequencing the fragment, for example. -
- +::: ::: ## Summary -:::highlight - -**Key Points** +:::{.callout-tip} +#### Key Points - Groups of similar SARS-CoV-2 sequences are classified into lineages or clades by different groups. The main nomenclature systems in use are _Nextstrain_, _Pangolin_ and _GISAID_. - In addition, the World Health Organisation (WHO) classifies some forms of SARS-CoV-2 as _variants of concern_ or _variants of interest_. These are forms of the virus that have been determined to have a significant public health impact. diff --git a/06-phylogeny.md b/materials/02-isolates/03-phylogeny.md similarity index 96% rename from 06-phylogeny.md rename to materials/02-isolates/03-phylogeny.md index ec5ecc9..f129120 100644 --- a/06-phylogeny.md +++ b/materials/02-isolates/03-phylogeny.md @@ -1,18 +1,11 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Building a Phylogenetic Tree +# Building phylogenetic trees -::: highlight - -**Questions** - -- How can I produce multiple sequence alignment of the consensus sequences? -- How can I build a phylogenetic tree from my consensus sequences? -- How can I scale my tree based on sampling dates? - -**Learning Objectives** +:::{.callout-tip} +#### Learning Objectives - Understand the basics of how phylogeny trees are constructed using maximum likelihood methods. - Describe what branch lengths represent in typical SARS-CoV-2 phylogenetic tree. @@ -22,7 +15,9 @@ pagetitle: "SARS-CoV-2 Genomics" - Use _TimeTree_ to obtain time-scaled phylogenetic trees using sample collection date metadata. ::: -:::note +:::{.callout-note} +#### Slides + This section has an accompanying slide deck. ::: @@ -115,8 +110,8 @@ Optionally, you can also add a collection of sequences from around the world (an As an input, these programs need a _multiple sequence alignment_ FASTA file, which is where we start our analysis. -:::note -**Data for this section** +:::{.callout-note} +#### Data for this section We will work from the course materials folder called `04-phylogeny`, which contains the following files: @@ -169,8 +164,8 @@ Visualising the alignment can be useful for example to identify regions with mis ![Snapshop of an alignment visualised with AliView. In this case we are looking at the end of the alignment of our sequences, which shows a typical high number of missing ('N') bases.](images/alignment_aliview.png) -:::note -**Other Alignment Strategies** +:::{.callout-note} +#### Other Alignment Strategies There are other commonly used alignment tools used for SARS-CoV-2 genomes: @@ -217,8 +212,8 @@ We can look at the output folder (specified with `--prefix`) where we see severa The main files of interest are the report file (`.iqtree`) and the NEWICK tree file (`.treefile`). -:::note -**Inference of very large trees** +:::{.callout-note} +#### Inference of very large trees Although running _IQ-Tree_ with default options is fine for most applications, there will be some bottlenecks once the number of samples becomes too large. In particular, the _ModelFinder_ step may be very slow and so it's best to set a model of our choice based on other people's work. @@ -303,25 +298,7 @@ We can visualise the tree we just produced with _FigTree_. We can also import the output from _Pangolin_ to annotate our tree by variant (**note:** unfortunately FigTree wants a TSV file, but pangolin exports CSV. I did this, but maybe there's a less confusing way to do this? `cat results/pangolin/run1_report.csv | sed 's/,/\t/g' > results/pangolin/run1_report.tsv`) -:::note -
Installing `fasttree` and `iqtree` - -You can install both these programs using _Conda_ (more about this package manager in [Reproducible Workflows](08-nextflow_conda_setup.html)). -For example, we can create a new environment called "phylogenetics" with both these programs as well as _FigTree_ (for tree visualisation). - -```console -$ mamba create --name phylogenetics fasttree iqtree figtree -``` - -We can then activate this environment by running `conda activate phylogenetics`, making the programs available to use. - -Note that _FigTree_ is also available on Windows and Mac (see the [software page](http://tree.bio.ed.ac.uk/software/figtree/) for download links). - -
-::: - - -:::exercise +:::{.callout-exercise} - Looking at the tree we just constructed, do you think the Delta variant emerged from the Alpha variant? - Produce a new phylogenetic tree but this time using both the sequences we processed and a sample of other public sequences that represent the global diversity of SARS-CoV-2 genomes. @@ -379,7 +356,77 @@ More precise tree dating may be achieved by using public sequences across a rang Time-scaled trees of this sort can therefore be useful to infer if there is a recent spread of a new clade in the population. -:::exercise +## Missing Data & Problematic Sites + +So far, we have been using all of our assembled samples in the phylogenetic analysis. +However, we know that some of these have poorer quality (for example the "IN01" sample from India had low genome coverage). +Although, generally speaking, sequences with missing data are unlikely to substantially affect the phylogenetic results, their placement in the phylogeny will be more uncertain (since several variable sites may be missing data). +Therefore, for phylogenetic analysis, it is best if we remove samples with low sequencing coverage, and instead focus on high-quality samples (e.g. with >80% coverage). + +A more serious issue affecting phylogenies is the presence of recurrent errors in certain positions of the genome. +One of the regions with a higher prevalence of errors is the start and end of the consensus sequence, which also typically contains many missing data (see example in Figure 2). +Therefore, it is common to _mask_ the first and last few bases of the alignment, to avoid including spurious variable sites in the analysis. + +:::{.callout-note} +#### Sequence masking + +The term **masking** is often used to refer to the process of converting sequence bases to the ambiguous character 'N'. +You may come across this term in the documentation of certain tools, for example: +"Positions with less than 20x depth of sequencing are masked." + +Masks are not limited to depth of sequencing. For example, [reference genomes from ENSEMBL](https://ensemblgenomes.org/) are available with masked repeat or low-complexity sequences (e.g. around centromeres, transposon-rich regions, etc.). + +The term **soft masking** is also used to refer to cases where, instead of using the ambiguous character 'N', sequences are masked with a lowercase. +For example: + +``` +>seq_with_soft_masking +ACAGACTGACGCTGTcatgtatgtcgacGATAGGCTGATGGCGAGTGACTCGAG +>seq_with_hard_masking +ACAGACTGACGCTGTNNNNNNNNNNNNNGATAGGCTGATGGCGAGTGACTCGAG +``` +::: + +Additionally, work by [Turakhia, de Maio, Thornlow, et al. (2020)](https://doi.org/10.1371/journal.pgen.1009175) has identified several sites that show an unexpected mutation pattern. +This includes, for example, mutations that unexpectedly occur multiple times in different parts of the tree ([homoplasies](https://en.wikipedia.org/wiki/Homoplasy)) and often coincide with primer binding sites (from amplicon-based protocols) and can even be lab-specific (e.g. due to their protocols and data processing pipelines). +The work from this team has led to the creation of a list of [problematic sites](https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480), which are recommended to be _masked_ before running the phylogenetic analysis. + +![Example of errors in phylogenetic inference due to recurrent sequencing errors. Source: [Figure 1 in Turakhia, de Maio, Thornlow et al. (2020)](https://journals.plos.org/plosgenetics/article/figure?id=10.1371/journal.pgen.1009175.g001)](https://journals.plos.org/plosgenetics/article/figure/image?size=inline&id=10.1371/journal.pgen.1009175.g001) + +So, let's try to improve our alignment by masking the problematic sites, which are [provided as a VCF file](https://raw.githubusercontent.com/W-L/ProblematicSites_SARS-CoV2/master/problematic_sites_sarsCov2.vcf). +This file also includes the first and last positions of the genome as targets for masking (positions 1–55 and 29804–29903, relative to the Wuhan-Hu-1 reference genome MN908947.3). +The authors also provide a [python script](https://github.com/W-L/ProblematicSites_SARS-CoV2/blob/master/src/mask_alignment_using_vcf.py) for masking a multiple sequence alignment. +We have already downloaded these files to our course materials folder, so we can go ahead and use the script to mask our file: + +```bash +python scripts/mask_alignment_using_vcf.py --mask -v resources/problematic_sites.vcf -i results/mafft/uk_alignment.fa -o results/mafft/uk_alignment_masked.fa +``` + +If we open the output file with AliView, we can confirm that the positions specified in the VCF file have now been masked with the missing 'N' character. + +We could then use this masked alignment for our tree-inference, just as we did before. + + +:::{.callout-note} +#### Using Python Scripts + +Bioinformaticians often write custom scripts for particular tasks. +In this example, the authors of the "problematic sites" wrote a _Python_ script that takes as input the FASTA file we want to mask as well as a VCF with the list of sites to be masked. + +Python scripts are usually run with the `python` program and often accept options in a similar way to other command-line tools, using the syntax `--option` (this is not always the case, but most professionally written scripts follow this convention). +To see how to use the script we can use the option `--help`. +For our case, we could run: + +```console +$ python scripts/mask_alignment_using_vcf.py --help +``` +::: + + +## Exercises + +:::{.callout-exercise} +#### Building phylogenies So far we have focused our analysis on the samples from the UK. In this exercise you will be able to practice these steps on the samples from India. @@ -405,7 +452,7 @@ But if you feel comfortable with `nano`, as a bonus, you can try to save the com - Is the collection date metadata for these samples compatible with the lineage information from the _Pangolin_ website? Can you hypothesise what may have happened with these samples? -
Answer +:::{.callout-answer} **Question 1** @@ -501,80 +548,14 @@ And by mistake the date of sequencing was recorded as the date of collection. This is an example how important accurate metadata is for our analysis. -
-::: - - -## Missing Data & Problematic Sites - -So far, we have been using all of our assembled samples in the phylogenetic analysis. -However, we know that some of these have poorer quality (for example the "IN01" sample from India had low genome coverage). -Although, generally speaking, sequences with missing data are unlikely to substantially affect the phylogenetic results, their placement in the phylogeny will be more uncertain (since several variable sites may be missing data). -Therefore, for phylogenetic analysis, it is best if we remove samples with low sequencing coverage, and instead focus on high-quality samples (e.g. with >80% coverage). - -A more serious issue affecting phylogenies is the presence of recurrent errors in certain positions of the genome. -One of the regions with a higher prevalence of errors is the start and end of the consensus sequence, which also typically contains many missing data (see example in Figure 2). -Therefore, it is common to _mask_ the first and last few bases of the alignment, to avoid including spurious variable sites in the analysis. - -:::note -The term **masking** is often used to refer to the process of converting sequence bases to the ambiguous character 'N'. -You may come across this term in the documentation of certain tools, for example: -"Positions with less than 20x depth of sequencing are masked." - -Masks are not limited to depth of sequencing. For example, [reference genomes from ENSEMBL](https://ensemblgenomes.org/) are available with masked repeat or low-complexity sequences (e.g. around centromeres, transposon-rich regions, etc.). - -The term **soft masking** is also used to refer to cases where, instead of using the ambiguous character 'N', sequences are masked with a lowercase. -For example: - -``` ->seq_with_soft_masking -ACAGACTGACGCTGTcatgtatgtcgacGATAGGCTGATGGCGAGTGACTCGAG ->seq_with_hard_masking -ACAGACTGACGCTGTNNNNNNNNNNNNNGATAGGCTGATGGCGAGTGACTCGAG -``` ::: - -Additionally, work by [Turakhia, de Maio, Thornlow, et al. (2020)](https://doi.org/10.1371/journal.pgen.1009175) has identified several sites that show an unexpected mutation pattern. -This includes, for example, mutations that unexpectedly occur multiple times in different parts of the tree ([homoplasies](https://en.wikipedia.org/wiki/Homoplasy)) and often coincide with primer binding sites (from amplicon-based protocols) and can even be lab-specific (e.g. due to their protocols and data processing pipelines). -The work from this team has led to the creation of a list of [problematic sites](https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480), which are recommended to be _masked_ before running the phylogenetic analysis. - -![Example of errors in phylogenetic inference due to recurrent sequencing errors. Source: [Figure 1 in Turakhia, de Maio, Thornlow et al. (2020)](https://journals.plos.org/plosgenetics/article/figure?id=10.1371/journal.pgen.1009175.g001)](https://journals.plos.org/plosgenetics/article/figure/image?size=inline&id=10.1371/journal.pgen.1009175.g001) - -So, let's try to improve our alignment by masking the problematic sites, which are [provided as a VCF file](https://raw.githubusercontent.com/W-L/ProblematicSites_SARS-CoV2/master/problematic_sites_sarsCov2.vcf). -This file also includes the first and last positions of the genome as targets for masking (positions 1–55 and 29804–29903, relative to the Wuhan-Hu-1 reference genome MN908947.3). -The authors also provide a [python script](https://github.com/W-L/ProblematicSites_SARS-CoV2/blob/master/src/mask_alignment_using_vcf.py) for masking a multiple sequence alignment. -We have already downloaded these files to our course materials folder, so we can go ahead and use the script to mask our file: - -```bash -python scripts/mask_alignment_using_vcf.py --mask -v resources/problematic_sites.vcf -i results/mafft/uk_alignment.fa -o results/mafft/uk_alignment_masked.fa -``` - -If we open the output file with AliView, we can confirm that the positions specified in the VCF file have now been masked with the missing 'N' character. - -We could then use this masked alignment for our tree-inference, just as we did before. - - -:::note -**Using Python Scripts** - -Bioinformaticians often write custom scripts for particular tasks. -In this example, the authors of the "problematic sites" wrote a _Python_ script that takes as input the FASTA file we want to mask as well as a VCF with the list of sites to be masked. - -Python scripts are usually run with the `python` program and often accept options in a similar way to other command-line tools, using the syntax `--option` (this is not always the case, but most professionally written scripts follow this convention). -To see how to use the script we can use the option `--help`. -For our case, we could run: - -```console -$ python scripts/mask_alignment_using_vcf.py --help -``` ::: ## Summary -:::highlight - -**Key Points** +:::{.callout-tip} +#### Key Points - Methods for phylogenetic inference include _parsimony_ and _maximum likelihood_. Maximum likelihood methods are preferred because they include more features of the evolutionary process. However, they are computationally more demanding than parsimony-based methods. - To build a phylogenetic tree we need a _multiple sequence alignment_ of the sequences we want to infer a tree from. diff --git a/materials/02-isolates/images b/materials/02-isolates/images new file mode 120000 index 0000000..5e67573 --- /dev/null +++ b/materials/02-isolates/images @@ -0,0 +1 @@ +../images \ No newline at end of file diff --git a/08-case_study_switzerland.md b/materials/03-case_studies/01-switzerland.md similarity index 99% rename from 08-case_study_switzerland.md rename to materials/03-case_studies/01-switzerland.md index 2fcb04a..dde486a 100644 --- a/08-case_study_switzerland.md +++ b/materials/03-case_studies/01-switzerland.md @@ -1,10 +1,12 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Case Study: Switzerland (Nanopore) +# Switzerland (Nanopore) + +:::{.callout-tip} +#### Learning Objectives -:::highlight This section demonstrates a start-to-finish analysis of a dataset sequenced on a _Nanopore_ platform, using the concepts and tools covered in previous sections. You can download the data from these links (two versions available): diff --git a/08-case_study_southafrica.md b/materials/03-case_studies/02-southafrica.md similarity index 99% rename from 08-case_study_southafrica.md rename to materials/03-case_studies/02-southafrica.md index 59ce03e..0ad62d7 100644 --- a/08-case_study_southafrica.md +++ b/materials/03-case_studies/02-southafrica.md @@ -1,10 +1,12 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Case Study: South Africa (Illumina) +# South Africa (Illumina) + +:::{.callout-tip} +#### Learning Objectives -:::highlight This section demonstrates a start-to-finish analysis of a dataset sequenced on an _Illumina_ platform, using the concepts and tools covered in previous sections. You can download the data from these links (two versions available): diff --git a/08-case_study_eqa.md b/materials/03-case_studies/03-eqa.md similarity index 91% rename from 08-case_study_eqa.md rename to materials/03-case_studies/03-eqa.md index 3c32dbf..cae4e62 100644 --- a/08-case_study_eqa.md +++ b/materials/03-case_studies/03-eqa.md @@ -1,10 +1,12 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Case Study: EQA +# EQA (Exercise) + +:::{.callout-tip} +#### Learning Objectives -:::highlight This section is an **extended self-paced practice** applying the concepts covered in previous sections. By the end of this practice, you should be able to: @@ -24,8 +26,8 @@ _External Quality Assessment_ (EQA) is a procedure that allows laboratories to a For genomic analysis of SARS-CoV-2, a standard panel of samples has been developed by the [GenQA](https://genqa.org/) consortium, which is part of [UK NEQAS](https://ukneqas.org.uk/), a non-profit that designs EQA protocols for a range of applications. _GenQA_'s panel of samples includes lab-cultured SARS-CoV-2 samples of known origin, enabling laboratories to assess whether their sequencing and bioinformatic pipelines correctly identify expected mutations and lineage assignments for each of the samples in the panel. -:::note -
More about EQA +:::{.callout-note collapse=true} +#### More about EQA EQA programs such as this one help to provide assurance of the diagnostic testing results obtained by a lab. One of the key things to consider is that, for reliable assessment, **samples should be processed in the same way as patient samples**. @@ -38,7 +40,6 @@ This may include staff training, purchase of new equipment, or updating the reag EQA panels are often updated, to reflect any new and emerging pathogens, allowing the labs to assess the suitability of the protocols used to detect them. It also helps raise awareness of any atypical variants of pathogens that may exist in the environment. -
::: In this case study, we are going to analyse samples from the _GenQA_ panel, to helps us assess the quality of our bioinformatic analysis and extract key pieces of information to be reported. @@ -101,7 +102,7 @@ We already include the following: - `scripts` → where we include some scripts that we will use towards the end of the workshop. You should also create several scripts during the workshop, which you will save here. - `sample_info.csv` → a table with some metadata for our samples. -:::exercise +:::{.callout-exercise} Your first task is to **create two new directories** in the project folder called `report` and `results`. You can do this either using the file explorer or from the command line (using the `mkdir` command). @@ -109,11 +110,12 @@ You can do this either using the file explorer FASTQ](03-intro_ngs.html#FASTQ_Files) section of the materials). +Regardless of which platform you used to sequence your samples, the analysis starts with FASTQ files (if you need a reminder of what a FASTQ file is, look at the [Introduction to NGS](../01-intro/02-ngs.md#fasta-files) section). However, the organisation of these files is slightly different depending on the platform, and is detailed below. +:::{.panel-tabset group="platform"} #### Nanopore Typically, Nanopore data is converted to FASTQ format using the program _Guppy_. @@ -140,7 +142,7 @@ You can look at the files you have available from the command line using: ```bash ls data/ ``` - +::: ### Metadata @@ -156,7 +158,7 @@ For effective genomic surveillance, we need at the very minimum three pieces of Of course, this is the _minimum_ metadata we need for a useful analysis. However, the more information you collect about each sample, the more questions you can ask from your data -- so always remember to record as much information as possible for each sample. -:::exercise +:::{.callout-exercise} **Note: If you are using our pre-sequenced data, you can skip this exercise.** @@ -182,8 +184,8 @@ Open this file in _Excel_ and create the following columns: This will ensure that in the future people have sufficient information to re-run the analysis on your data. ::: -:::warning -**Dates in Spreadsheet Programs** +:::{.callout-warning} +#### Dates in Spreadsheet Programs Note that programs such as _Excel_ often convert date columns to their own format, and this can cause problems when analysing data later on. For example, GISAID wants dates in the format YYYY-MM-DD, but by default _Excel_ displays dates as DD/MM/YYYY. @@ -199,15 +201,15 @@ To make sure no date information is lost due to _Excel_'s behaviour, it's a good At this point we are ready to start our analysis with the first step: generating a consensus genome for our samples. We will use a standardised pipeline called _viralrecon_, which automates most of this process for us, helping us be more efficient and reproducible in our analysis. -If you need to revise how the `nf-core/viralrecon` pipeline works, please consult the [Consensus Assembly](04-consensus.html) section of the materials. +If you need to revise how the `nf-core/viralrecon` pipeline works, please consult the [Consensus Assembly](../02-isolates/01-consensus.md) section of the materials. ### Samplesheet The first step in this process is to prepare a CSV file with information about our sequencing files, which will be used as the input to the _viralrecon_ pipeline. -The pipeline's documentation gives details about the format of this samplesheet, depending on whether you are working with Illumina or Nanopore data: https://nf-co.re/viralrecon/2.5/usage. +The [pipeline's documentation](https://nf-co.re/viralrecon/2.6.0/usage) gives details about the format of this samplesheet, depending on whether you are working with Illumina or Nanopore data. -:::exercise +:::{.callout-exercise} **Note: If you are using our pre-sequenced data, you can skip this exercise.** @@ -244,13 +246,13 @@ Now, you can open this file in _Excel_ and continue editing it to add a new colu ::: -### Running Viralrecon {.tabset} +### Running Viralrecon The next step in our analysis is to run the `nf-core/viralrecon` pipeline. The way the command is structured depends on which kind of data we are working with. -There are [many options](https://nf-co.re/viralrecon/2.5/parameters) that can be used to customise the pipeline, but typical commands are shown below for each platform. - +There are [many options](https://nf-co.re/viralrecon/2.6.0/parameters) that can be used to customise the pipeline, but typical commands are shown below for each platform. +:::{.panel-tabset group="platform"} #### Nanopore ``` @@ -301,11 +303,9 @@ nextflow run nf-core/viralrecon -profile singularity \ --skip_nextclade \ --platform illumina ``` +::: - -### {.unlisted .unnumbered} - -:::exercise +:::{.callout-exercise} Your next task is to run the pipeline on your data. However, rather than run the command directly from the command line, let's save it in a **shell script** -- for reproducibility and as a form of documenting our analysis. @@ -316,15 +316,15 @@ You can either use the command-line text editor `nano` or use _Gedit_, which com In this script, include the Nextflow command based on the command shown above, adjusting it to fit your input files and type of data. Once your command is ready, save the script and run it from the command line using `bash scripts/01-run_viralrecon.sh`. -If you need a reminder of how to work with shell scripts, revise the [Shell Scripts section](02d-unix_pipes.html#Shell_Scripts) of the materials. +If you need a reminder of how to work with shell scripts, revise the [Shell Scripts section](https://cambiotraining.github.io/unix-shell/materials/02-programming/01-scripts.html) of the accompanying Unix materials. **Note for Nanopore:** if you are using our pre-sequenced Nanopore data, you can can use the following medaka model: `r941_prom_high_g360`. ::: -:::note -**Maximum Memory and CPUs** +:::{.callout-note} +#### Maximum Memory and CPUs In our _Nextflow_ command above we have set `--max_memory '15.GB' --max_cpus 8` to limit the resources used in the analysis. This is suitable for the computers we are using in this workshop. @@ -340,9 +340,9 @@ At this stage we want to identify issues such as: - Any samples which have critically low coverage. There is no defined threshold, but samples with less than 85% coverage should be considered carefully. - Any problematic regions that systematically did not get amplified (amplicon dropout). -:::exercise +:::{.callout-exercise} To assess the quality of our assemblies, we can use the **_MultiQC_ report** generated by the pipeline, which compiles several pieces of information about our samples. -If you need a reminder of where to find this file, consult the [Consensus > Output Files](04-consensus.html#Output_Files) section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.5/output). +If you need a reminder of where to find this file, consult the "[Consensus assembly](../02-isolates/01-consensus.md#output-files)" section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.6.0/output). Open the quality report and try to answer the following questions: @@ -361,9 +361,9 @@ You can discuss with your colleagues and compare your results/conclusions to see The _viralrecon_ pipeline outputs a table with information about SNP/Indel variants as a CSV file named `variants_long_table.csv`. It is important to inspect the results of this file, to identify any mutations with severe effects on annotated proteins, or identify samples with an abnormal high number of "mixed" bases. -:::exercise +:::{.callout-exercise} Open the `variants_long_table.csv` file and answer the following questions. -If you need a reminder of where to find this file, consult the [Consensus > Output Files](04-consensus.html#Output_Files) section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.5/output). +If you need a reminder of where to find this file, consult the "[Consensus assembly](../02-isolates/01-consensus.md#output-files)" section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.6.0/output). - How many variants have allele frequency < 75%? - Does any of the samples have a particularly high number of these low-frequency variants, compared to other samples? (This could indicate cross-contamination of samples) @@ -379,13 +379,13 @@ Go to the position where the mutation is located, and try to see if there is evi ::: -:::exercise +:::{.callout-exercise} The samples we are using in this analysis come from a standard EQA panel. As such, we already know what lineages we expect to find in these samples. - Open IGV and load one (or more) of the alignment BAM files into it. - If you need a reminder of where to find this file, consult the [Consensus > Output Files](04-consensus.html#Output_Files) section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.5/output). + If you need a reminder of where to find this file, consult the "[Consensus assembly](../02-isolates/01-consensus.md#output-files)" section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.6.0/output). - Open your metadata table (`sample_info.csv`) and check which lineages your loaded samples belong to. - Do a web search to find what mutations characterise those lineages. - On IGV, go to the location of some of the expected mutations, and see if you can see it in the respective samples. @@ -393,7 +393,7 @@ As such, we already know what lineages we expect to find in these samples. ::: -## Clean FASTA {.tabset} +## Clean FASTA The _viralrecon_ pipeline outputs each of our consensus sequences as individual FASTA files for each sample (look at the [FASTA Files](03-intro_ngs.html#FASTA_Files) section if you need a reminder of what these files are). However, by default, the sample names in this file have extra information added to them, which makes some downstream analysis less friendly (because the names will be too long and complicated). @@ -403,10 +403,11 @@ To clean up these files, we will do two things: - Combine all the individual FASTA files together. - Remove the extra text from the sequence names. -:::exercise +:::{.callout-exercise} We will use the programs `cat` (concatenate) and `sed` (text replacement) to clean our FASTA files. Consider the commands given below: +:::{.panel-tabset group="platform"} ### Nanopore ```bash @@ -419,15 +420,15 @@ cat | sed 's/\/ARTIC\/medaka MN908947.3//' > report/consensus.fa cat | sed 's/ MN908947.3//' > report/consensus.fa ``` -## {.unlisted .unnumbered} +::: The `sed` command shown is used to remove the extra pieces of text added by the pipeline to each sample name. This is a slightly more advanced program to use, so we already give you the code. -(If you want to learn more about it, check the [Unix > Text Manipulation > Text Replacement](02c-unix_text_manipulation.html#Text_Replacement) section of the materials.) +If you want to learn more about it, check the "[Text Replacement]([02c-unix_text_manipulation.html#Text_Replacement](https://cambiotraining.github.io/unix-shell/materials/04-misc/01-sed.html))" section of the accompanying Unix materials. Copy the command above to a new shell script called `scripts/02-clean_fasta.sh`. Fix the code by replacing `` with the path to all the FASTA files generated by the pipeline (remember you can use the `*` wildcard). -If you need a reminder of where to find the FASTA consensus files from _viralrecon_, consult the [Consensus > Output Files](04-consensus.html#Output_Files) section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.5/output). +If you need a reminder of where to find the FASTA consensus files from _viralrecon_, consult the "[Consensus assembly](../02-isolates/01-consensus.md#output-files)" section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.6.0/output). Once you fixed the script, run it with `bash` and check that the output file is generated. @@ -455,7 +456,7 @@ These regions are missing data, and are denoted as 'N' in our sequences. Although we already determined the percentage of each sequence that is missing from the _MultiQC_ report, we don't have the **intervals of missing data**, which can be a useful quality metric to have. In particular, we may want to know what is the largest continuous stretch of missing data in each sample. -:::exercise +:::{.callout-exercise} We will use the software tool `seqkit` to find intervals where the 'N' character occurs. This tool has several functions available, one of which is called `locate`. @@ -471,7 +472,7 @@ Then run the script you created using `bash`. ::: -:::exercise +:::{.callout-exercise} Open the file you created in the previous step (`results/consensus_miss_intervals.tsv`) in a spreadsheet program. Create a new column with the length of each interval (`end - start + 1`). @@ -482,20 +483,20 @@ Note if any missing intervals are larger than 1Kb, and whether they overlap with ::: -### Lineage Assignment {.tabset} +### Lineage Assignment Although the _Viralrecon_ pipeline runs _Pangolin_ and _Nextclade_ on our samples, it does not use the latest version of these programs (because lineages evolve so fast, the nomenclature constantly changes). Therefore, it is good practice to re-run our samples through these tools, to make sure we get the most up-to-date lineage assignment. -Although it is possible to [configure _viralrecon_](https://nf-co.re/viralrecon/2.5/usage#updating-containers) to use more recent versions of these tools, it requires more advanced use of configuration files with the pipeline. +Although it is possible to [configure _viralrecon_](https://nf-co.re/viralrecon/2.6.0/docs/usage#updating-containers-advanced-users) to use more recent versions of these tools, it requires more advanced use of configuration files with the pipeline. Alternatively, we can run our consensus sequences through the latest versions of _Nextclade_ and _Pangolin_. There are two ways to do this: using their respective web applications, or their command-line versions. For this task, we **recommend that you use the command line tools** (this will ensure our downstream analysis works well), but we also provide the option to use the web apps for your reference. - +:::{.panel-tabset} #### Command line -:::exercise +:::{.callout-exercise} To run the command-line version of these tools, there are two steps: @@ -540,11 +541,11 @@ After the analysis completes: **These exercises are optional - during the workshop please use the command line version of the tools.** -:::exercise +:::{.callout-exercise} **Running Nextclade** Go to [clades.nextstrain.org](https://clades.nextstrain.org/) and run _Nextclade_ on the clean FASTA file you created earlier (`report/consensus.fa`). -If you need a reminder about this tool, see the [Lineages & Variants > Nextclade](05-lineage_analysis.html#Nextclade) section of the materials. +If you need a reminder about this tool, see the "[Lineages and variants](../02-isolates/02-lineages.md#nextclade)" section of the materials. Once the analysis completes, pay particular attention to the quality control column, to see what problems your samples may have (in particular those classified as "bad" quality). @@ -555,11 +556,11 @@ Then: - Save this file in the `results/nextclade` folder you created. ::: -:::exercise +:::{.callout-exercise} **Running Pangolin** Go to [pangolin.cog-uk.io](https://pangolin.cog-uk.io/) and run _Pangolin_ on the clean FASTA file you created earlier (`report/consensus.fa`). -If you need a reminder about this tool, see the [Lineages & Variants > Pangolin](05-lineage_analysis.html#Pangolin) section of the materials. +If you need a reminder about this tool, see the "[Lineages and variants](../02-isolates/02-lineages.md#nextclade)" section of the materials. Once the analysis completes, pay particular attention to any samples that failed. If there were any failed samples, check if they match the report from _Nextclade_. @@ -571,6 +572,7 @@ Then: - Save this file in the `results/pangolin` folder you created. ::: +::: ### Phylogeny @@ -581,7 +583,7 @@ This requires three steps: - Tree inference. - Tree visualisation. -:::exercise +:::{.callout-exercise} - Start by creating two directories to store the output of our analysis: `results/mafft` and `results/iqtree`. - To make our analysis more interesting, we will combine our sequences with sequences from previous workshops. @@ -589,9 +591,9 @@ This requires three steps: Save the output in a new file `results/mafft/unaligned_consensus.fa`. - Perform a multiple sequence alignment of the combined consensus sequences using the program `mafft`. Save the output in a file called `results/mafft/aligned_consensus.fa`. - Consult the [Building Phylogenies > Alignment](06-phylogeny.html#Alignment) section of the materials to see how to create the MAFFT command. + Consult the "[Building phylogenetic trees](../02-isolates/03-phylogeny.md#alignment)" section of the materials to see how to create the MAFFT command. - Infer a phylogenetic tree using the `iqtree2` program. - Consult the [Building Phylogenies > Tree Inference](06-phylogeny.html#Tree_Inference:_IQ-Tree) section of the materials to see what command to use. + Consult the "[Building phylogenetic trees](../02-isolates/03-phylogeny.md#tree-inference-iq-tree)" section of the materials to see what command to use. - Once you have both of these commands working, make sure to save them in a new shell script (as a record of your analysis). Save the script as `scripts/05-phylogeny.sh`. - Visualise the tree using FigTree. @@ -631,7 +633,7 @@ An example of how to do this is detailed in the [_civet_ documentation](https:// We have already downloaded the data from GISAID, so we can go straight to running our _civet_ analysis. -:::exercise +:::{.callout-exercise} Create a new script in `scripts/06-civet.sh`, with the following command, adjusted to fit your files: @@ -684,10 +686,10 @@ At this point in our analysis, we have several tables with different pieces of i Each of these tables stores different pieces of information, and it would be great if we could _integrate_ them together, to facilitate their interpration and generate some visualisations. We will demonstrate how this analysis can be done using the _R_ software, which is a popular programming language used for data analysis and visualisation. -Check the [Quick R Intro](107-quick_r.html) section of the materials for the basics of how to work with R and RStudio. +Check the [Quick R Intro](../appendices/quick_r.Rmd) appendix for the basics of how to work with R and RStudio. -:::exercise +:::{.callout-exercise} **Data Integration** From RStudio, open the script in `scripts/07-data_integration.R`. @@ -698,7 +700,7 @@ Once you reach the end of the script, you should have two tab-delimited files na Open both files in _Excel_ to check their content and confirm they contain information from across the multiple tables. ::: -:::exercise +:::{.callout-exercise} **Data Visualisation** After integrating the data, it's time to produce some visualisations. @@ -709,12 +711,12 @@ As you run the code, several plots will be created. You can export these plots from within RStudio using the "Export" button on the plotting panel -- these will be useful when you write your report later. ::: -:::exercise +:::{.callout-exercise} **Annotating Phylogenetic Tree** Use the file `report/consensus_metrics.tsv` (created in the Data Integration exercise) to annotate your phylogenetic tree in FigTree and display the lineages assigned to each sample as the tip labels. -If you need a reminder of how to load annotations in FigTree, check the [Building Phylogenies > Visualising Trees](06-phylogeny.html#Visualising_Trees) section of the materials. +If you need a reminder of how to load annotations in FigTree, check the "[Building phylogenetic trees](../02-isolates/03-phylogeny.md#visualising-trees)" section of the materials. ::: @@ -744,7 +746,7 @@ $F_{score} = \frac{2 \times Precision \times Sensitivity}{Precision + Sensitivit A high F-score is indicative of both high precision and sensitivity, whereas a lower score indicates that at least one of those metrics are low. -:::exercise +:::{.callout-exercise} From RStudio, open the script in `scripts/09-eqa_validation.R`. Run the code in the script, going line by line. @@ -755,7 +757,7 @@ Are you happy with the results? ::: -:::exercise +:::{.callout-exercise} **(Optional)** While you run the R script, you will have an object called `all_mutations` in your environment (check the top-right panel). @@ -765,7 +767,7 @@ Look at mutations that were not detected in your samples, but were present in th Open the BAM file for one of those samples in _IGV_ and navigate to the position where that mutation should occur. Investigate what the reason may have been for missing that mutation. -If you need a reminder of where to find the BAM file, consult the [Consensus > Output Files](04-consensus.html#Output_Files) section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.5/output). +If you need a reminder of where to find the BAM file, consult the [Consensus > Output Files](04-consensus.html#Output_Files) section of the materials, or the [Viralrecon output documentation](https://nf-co.re/viralrecon/2.6.0/output). ::: @@ -777,7 +779,7 @@ So, all that is left is to report our results in the most informative way to our You may already have established reporting templates in your institution, and if that's the case, you should use those. Alternatively, we will look at a suggested template, based on the reports done by the UK Health Security Agency (UKHSA). -:::exercise +:::{.callout-exercise} Open our shared [report template](https://docs.google.com/document/d/10StS8Sd7DzKpB6yjjOhb14R2-jkpNAF4UQfPhTuVuVU/edit?usp=sharing) document and download it as a _Word_ document (File → Download → Microsoft Word (.docx)). Save the file in `report/YYYY-MM-DD_analysis_report.docx` (replace `YYYY-MM-DD` by today's date). diff --git a/materials/03-case_studies/images b/materials/03-case_studies/images new file mode 120000 index 0000000..5e67573 --- /dev/null +++ b/materials/03-case_studies/images @@ -0,0 +1 @@ +../images \ No newline at end of file diff --git a/materials/04-wastewater/01-wastewater_surveillance.md b/materials/04-wastewater/01-wastewater_surveillance.md new file mode 100644 index 0000000..0f7484c --- /dev/null +++ b/materials/04-wastewater/01-wastewater_surveillance.md @@ -0,0 +1,11 @@ +--- +pagetitle: "SARS Genomic Surveillance" +--- + +# Wastewater surveillance + +:::{.callout-caution} + +Under development + +::: \ No newline at end of file diff --git a/materials/04-wastewater/images b/materials/04-wastewater/images new file mode 120000 index 0000000..5e67573 --- /dev/null +++ b/materials/04-wastewater/images @@ -0,0 +1 @@ +../images \ No newline at end of file diff --git a/101-managing_software_installation.md b/materials/05-software/01-managing_software.md similarity index 93% rename from 101-managing_software_installation.md rename to materials/05-software/01-managing_software.md index e025597..3074b76 100644 --- a/101-managing_software_installation.md +++ b/materials/05-software/01-managing_software.md @@ -1,14 +1,9 @@ --- -pagetitle: "SARS-CoV-2 Genomics" -editor_options: - markdown: - wrap: sentence +pagetitle: "SARS Genomic Surveillance" --- # Managing and Running Bioinformatics Software -**Note:** This page is under revision. - If you are attending one of our workshops, we will provide a virtual training environment with all of the required software and data pre-installed. In this page you will learn to manage bioinformatics software installation and setup. You will also learn why most bioinformatic tools run on Linux. @@ -16,9 +11,11 @@ With this regard you will learn typical environment in which you can setup and r If you run Windows and you want to setup your own computer to run the analysis demonstrated on this course, you can follow the instructions below. However, if you have already Linux in your PC/Server or you want to install fresh Ubuntu Linux you can follow the instruction from th [Install Linux](##Install%20Linux) section. -::: note -The `viralrecon` pipeline that we cover in this workshop can run on a regular desktop (e.g. with 4 CPUs and 16GB RAM). -However, if you are processing hundreds of samples, it may take several hours or even days. +:::{.callout-note} +#### Hardware specifications + +The `viralrecon` pipeline that we cover in this workshop can run on a regular desktop or laptop computer (e.g. with 4 CPUs and 16GB RAM). +However, if you are processing hundreds of samples, it may take several hours or even days to complete. For general bioinformatic applications, we recommend investing in a high-performance desktop workstation. The exact specifications depend on the application, but as a minimum at least 32 threads and 64GB RAM. @@ -42,10 +39,6 @@ There could be a lot of reasons for this, but we outline the following ones. - Linux has flexible built-in tools to manipulate large data - `cat`, `grep`, `sed`, `awk`, and the `|` pipe for chaining commands -::: warning -Try to open a large FASTA file (human assembly contig file) in Windows Notepad → the max size it can handle is 58Mb! -::: - Despite the above advantages of Linux OS over Windows from bioinformatics perspective, many PCs users appear to be Windows savy mainly due to inherent and historical reasons particularly the fact that Windows was the earliest Graphical User Interface (GUI) OS, so many applications were written in GUI and therefore gained popularity among users who had average knowledge of computer. The first step for running bioinformatics software in your Windows machine is to ensure that Linux OS runs on your machine. @@ -64,12 +57,11 @@ There are different ways to run bioinformatics tools on your Windows machine. WSL2 enables you to run Linux software on Windows natively. This section describes step by step on how to install WSL on Windows 11. -For Windows 10 instructions are [here](https://cambiotraining.github.io/sars-cov-2-genomics/104-wsl_windows.html "WSL2"). We advise that you update Windows 10 to 11 as it seems that WSL2 can be smooth installed on the Windows 11. To run WSL use the following command: -```{bash} +```bash wsl --install ``` @@ -77,7 +69,7 @@ The above command will download the Linux kernel, set WSL2 as default, and insta If you don't want Ubuntu you can use the following command: -```{bash} +```bash wsl --install -d ``` @@ -99,7 +91,7 @@ The most popular VM software are: - Oracle VirtualBox () -The full instructions and commands to run Oracle VirtualBox on Windows or Mac OS are explained in the [managing software installations instructions page](101-setup_installation_instructions.html). +The full instructions and commands to run Oracle VirtualBox on Windows or Mac OS are explained in the [managing software installations instructions page](01-managing_software.md). #### Dual Booting @@ -152,7 +144,7 @@ Otherwise you need to install it using apt package manager. To check the pip version i your system you can type this command: -```{bash} +```bash pip3 --version ``` @@ -160,5 +152,5 @@ More instructions on installing pip on Ubuntu can be found in the installation i ### Installing from source -In some cases bioinformatics software may need to be installed from the source to run your analysis pipeline. It could be that trying installing using the above options did not work for some unknown or unfamiliar reasons. For the users who are little bit advanced they can use bash commands based on the package instructions to install the package from the source, i.e. need compilation of the package binaries. A good example is samtools and its associated package dependencies bcftools and htslibs which you can find instruction commands to install these packages [here](https://cambiotraining.github.io/sars-cov-2-genomics/101-setup_installation_instructions.html) +In some cases bioinformatics software may need to be installed from the source to run your analysis pipeline. It could be that trying installing using the above options did not work for some unknown or unfamiliar reasons. For the users who are little bit advanced they can use bash commands based on the package instructions to install the package from the source, i.e. need compilation of the package binaries. A good example is samtools and its associated package dependencies bcftools and htslibs which you can find instruction commands to install these packages [here](03-software_setup.md) diff --git a/101-setup_installation_instructions.md b/materials/05-software/02-virtualbox.md similarity index 93% rename from 101-setup_installation_instructions.md rename to materials/05-software/02-virtualbox.md index f1226ec..16de758 100644 --- a/101-setup_installation_instructions.md +++ b/materials/05-software/02-virtualbox.md @@ -1,8 +1,5 @@ --- title: "Setup and Installation Instructions" -editor_options: - markdown: - wrap: sentence --- ## Running Ubuntu on Oracle VirtualBox (OVB) @@ -23,13 +20,10 @@ To install and run Ubuntu on Oracle VirtualBox please follow below instructions: ![](images/python_core_win32api_ovb_installation.PNG) -- While installing Oracle VM, seprately download Ubuntu Linux distribution which is in ISO image [here](https://ubuntu.com/download/desktop/thank-you?version=22.04.2&architecture=amd64) (preferred the latest Ubuntu 22.04). - - ::: note +- While installing Oracle VM, seprately download Ubuntu Linux distribution which is in ISO image [here](https://ubuntu.com/download/desktop/thank-you?version=22.04.2&architecture=amd64) (preferred the latest Ubuntu 22.04). Depending in the Internet speed it may take roughly 10 minutes or more to download the latest Ubuntu image ISO. In my case it took about 13 minutes to fully download the Ubuntu ISO image. - ::: - + - After Oracle VM successfully installed, click its desktop icon ![](images/VM_image.jpeg){width="18"} to start your Oracle VM - You will see a window similar like this one: @@ -102,14 +96,14 @@ We will test the Ubuntu shipped package manager which is apt and apt-get both ar Many bioinformatics tools require to first update the system package managers before installing the tool. In this kind of situation the apt or apt-get is used as shown in the example below. -```{bash} +```bash sudo apt-get update # Update database packages to the current state sudo apt upgrade # upgrade the old package ``` After those two commands you can then install the bioinformatics tools you want, for instance you can install bwa tool for doing mapping using the following commands. -```{bash} +```bash sudo apt -y install bwa ``` @@ -117,7 +111,7 @@ There is another built-in package manager called 'dpkg' which is required in som A good example is when installing RStudio package which is in .deb format. The command used to install RStudio is demonstrated below. -```{bash} +```bash sudo dpkg -i studio-2022.12.0-353-amd64.deb # install rstudio .deb package ``` @@ -127,7 +121,7 @@ The following are the steps to follow to install and use Conda package manager: - Update your Ubuntu packages database through this command: - ```{bash} + ```bash sudo apt-get update ``` @@ -143,19 +137,19 @@ The following are the steps to follow to install and use Conda package manager: - And then using the following command to download Miniconda in your machine: - ```{bash} + ```bash wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.1.0-1-Linux-x86_64.sh ``` - Type the following command to install the conda: - ```{bash} + ```bash bash Miniconda3-py310_23.1.0-1-Linux-x86_64.sh ``` - After installation remove the downloaded miniconda file to clear up the storage drive. - ```{bash} + ```bash rm Miniconda3-py310_23.1.0-1-Linux-x86_64.sh ``` @@ -164,7 +158,7 @@ The following are the steps to follow to install and use Conda package manager: ### Mamba as an alternative to conda Lastly, you can also install mamba which you can use to install other conda packages on your own PCs - ```{bash} + ```bash conda init conda config --add channels defaults; conda config --add channels bioconda; conda config --add channels conda-forge; conda config --set channel_priority strict @@ -174,13 +168,13 @@ The following are the steps to follow to install and use Conda package manager: - Now you can install samtools using conda by running the command: - ```{bash} + ```bash conda install -c bioconda samtools bcftools ``` - You can then use 'mamba' to install the following useful packages for the workshop in your own PCs. - ```{bash} + ```bash mamba create -n sars -y nextflow mafft iqtree treetime ``` @@ -188,7 +182,7 @@ The following are the steps to follow to install and use Conda package manager: To install pip in Ubuntu, run the following commands: -```{bash} +```bash which python && which python3 # Check the python version of your Ubuntu OS and its path. pip --version # Check which pip version is your installed Python @@ -201,14 +195,14 @@ sudo apt install python3-pip # installing pip for Python3 if it does not exist Now you can install and run any Python based tool. For instance, you install in your Ubuntu OS installed the bowtie popular mapper. -```{bash} +```bash pip install bowtie # example of installing bowtie using pip ``` ## Homework Exercise In this homework exercise, we are encouraging all participants to set up and install VM for Ubuntu 22.04 using Oracle VirtualBox on their own PCs (Windows or Mac OSX). -Follow the instructions in the [setup tutorial page](https://cambiotraining.github.io/sars-cov-2-genomics/101-setup_installation_instructions.html) and install the following: +Follow the instructions in the [setup tutorial page](01-managing_software.md) and install the following: - Conda - Singularity diff --git a/101-setup.md b/materials/05-software/03-software_setup.md similarity index 97% rename from 101-setup.md rename to materials/05-software/03-software_setup.md index d5ac52e..1700680 100644 --- a/101-setup.md +++ b/materials/05-software/03-software_setup.md @@ -1,13 +1,13 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Setup +# Software setup If you are attending one of our workshops, we will provide a virtual training environment with all of the required software and data pre-installed. If you want to setup your own computer to run the analysis demonstrated on this course, you can follow the instructions below. -:::note +:::{.callout-note} The `viralrecon` pipeline that we cover in this workshop can run on a regular desktop (e.g. with 4 CPUs and 16GB RAM). However, if you are processing hundreds of samples, it may take several hours or even days. @@ -16,8 +16,9 @@ The exact specifications depend on the application, but as a minimum at least 32 ::: -## Install Linux {.tabset} +## Install Linux +:::{.panel-tabset} ### Fresh Installation The recommendation for bioinformatic analysis is to have a dedicated computer running a Linux distribution. @@ -25,7 +26,7 @@ The kind of distribution you choose is not critical, but we recommend **Ubuntu** You can follow the [installation tutorial on the Ubuntu webpage](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview). -:::warning +:::{.callout-warning} Installing Ubuntu on the computer will remove any other operating system you had previously installed, and can lead to data loss. ::: @@ -64,9 +65,9 @@ Also see the section below "Docker for Windows" for further setup. Another way to run Linux within Windows (or macOS) is to install a Virtual Machine. However, this is mostly suitable for practicing and **not suitable for real data analysis**. -We give instructions to install a VM using Oracle's Virtual Box on [our tutorial](101-setup_installation_instructions.html). +We give instructions to install a VM using Oracle's Virtual Box on [our tutorial](01-managing_software.md). -## {.unlisted .unnumbered} +::: After making a fresh install of Ubuntu, open a terminal and run the following commands to update your system and install some essential packages: @@ -215,8 +216,9 @@ If you have a different Linux distribution, you can find more detailed instructi If you have issues running _Nextflow_ pipelines with _Singularity_, then you can follow the instructions below for _Docker_ instead. -### Docker {.tabset} +### Docker +:::{.panel-tabset} #### Windows WSL When using WSL2 on Windows, running _Nextflow_ pipelines with `-profile singularity` sometimes doesn't work. @@ -249,4 +251,4 @@ sudo usermod -aG docker $USER After the last step, you will need to **restart your computer**. From now on, you can use `-profile docker` when you run _Nextflow_ -## {.unlisted .unnumbered} \ No newline at end of file +::: \ No newline at end of file diff --git a/materials/05-software/images b/materials/05-software/images new file mode 120000 index 0000000..5e67573 --- /dev/null +++ b/materials/05-software/images @@ -0,0 +1 @@ +../images \ No newline at end of file diff --git a/materials/_chapters.yml b/materials/_chapters.yml new file mode 100644 index 0000000..b6d3c4a --- /dev/null +++ b/materials/_chapters.yml @@ -0,0 +1,29 @@ +book: + chapters: + - part: "Introduction" + chapters: + - materials/01-intro/01-surveillance.md + - materials/01-intro/02-ngs.md + - materials/01-intro/03-workflows.md + - part: "Clinical Isolates" + chapters: + - materials/02-isolates/01-consensus.md + - materials/02-isolates/02-lineages.md + - materials/02-isolates/03-phylogeny.md + - part: "Case Studies" + chapters: + - materials/03-case_studies/01-switzerland.md + - materials/03-case_studies/02-southafrica.md + - materials/03-case_studies/03-eqa.md + - part: "Wastewater Samples" + chapters: + - materials/04-wastewater/01-wastewater_surveillance.md + - part: "Software" + chapters: + - materials/05-software/01-managing_software.md + - materials/05-software/02-virtualbox.md + - materials/05-software/03-software_setup.md + appendices: + - materials/appendices/file_formats.md + - materials/appendices/unix_cheatsheet.md + - materials/appendices/quick_r.Rmd diff --git a/106-file_formats.md b/materials/appendices/file_formats.md similarity index 98% rename from 106-file_formats.md rename to materials/appendices/file_formats.md index 50a9795..acede3e 100644 --- a/106-file_formats.md +++ b/materials/appendices/file_formats.md @@ -1,8 +1,8 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Common File Formats +# Common file formats {.unnumbered} This page lists some common file formats used in Bioinformatics (listed alphabetically). The heading of each file links to a page with more details about each format. diff --git a/materials/appendices/images b/materials/appendices/images new file mode 120000 index 0000000..5e67573 --- /dev/null +++ b/materials/appendices/images @@ -0,0 +1 @@ +../images \ No newline at end of file diff --git a/107-quick_r.Rmd b/materials/appendices/quick_r.Rmd similarity index 97% rename from 107-quick_r.Rmd rename to materials/appendices/quick_r.Rmd index c2ea545..ee4f4d8 100644 --- a/107-quick_r.Rmd +++ b/materials/appendices/quick_r.Rmd @@ -1,14 +1,14 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" output: html_document --- ```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE, root.dir = "course_files/r_demo/") -knitr::opts_knit$set(root.dir = "course_files/r_demo/") +knitr::opts_chunk$set(echo = TRUE, root.dir = "../../course_files/r_demo/") +knitr::opts_knit$set(root.dir = "../../course_files/r_demo/") ``` -# R Fundamentals +# R fundamentals {.unnumbered} This section gives a (very) quick and brief introduction to R and RStudio. It will not teach you the details of how to program in R, but it should serve as a basis to start you on that journey. @@ -236,7 +236,7 @@ Tables in R are called **data.frame**. The `tidyverse` package has its own version of a data.frame called a `tibble`. For the most part they are basically equivalent, but the `tibble` object has a nicer printing function to display our data on the console. -As an example for working with tables in R, let's read a TSV (tab-delimited) file that contains intervals of missing information in 5 SARS-CoV-2 consensus sequences (this data comes from the [Switzerland case study](08-case_study_switzerland.html#Missing_Intervals)). +As an example for working with tables in R, let's read a TSV (tab-delimited) file that contains intervals of missing information in 5 SARS-CoV-2 consensus sequences (this data comes from the [Switzerland case study](../03-case_studies/01-switzerland.md)). To import a TSV file into R as a data.frame we can use the function `read_tsv()` (for a CSV file we would use `read_csv()`): ```{r} @@ -350,7 +350,7 @@ missing_intervals |> count(seqID) ``` -:::exercise +:::{.callout-exercise} Modify the following code: @@ -364,7 +364,7 @@ To also: - rename the column `seqID` to be named `sample` instead. - save the output in an object called `intervals_count`. -
+:::{.callout-answer} To rename the column, we could use the `rename()` function: ```{r} @@ -381,7 +381,7 @@ intervals_count <- missing_intervals |> rename(sample = seqID) ``` -
+::: ::: ### Joining Tables @@ -414,7 +414,7 @@ inner_join(band_members, band_instruments, by = "name") In each case, if there was no match between the two tables, the cells are filled with missing values `NA`. -:::exercise +:::{.callout-exercise} In this exercise we will join the `intervals_summary` table we created earlier, with the metadata table that contains information about our samples. @@ -424,7 +424,8 @@ In this exercise we will join the `intervals_summary` table we created earlier, Save the output to the `intervals_summary` table (this will update the table).
You can use the `left_join()` function to achieve this, using the "sample" column as the identifier column used to join the two tables.
-
Answer +:::{.callout-answer} + **Answer 1** To read the metadata CSV file, we use the `read_csv()` function, and use `<-` to save the output in an object called `sample_info`: @@ -451,8 +452,7 @@ In that case, we would use `inner_join()`: intervals_summary <- inner_join(intervals_summary, sample_info, by = "sample") ``` -
- +::: ::: @@ -519,12 +519,12 @@ ggplot(data = qc_metrics, colour = "Nextclade QC Status") ``` -:::exercise +:::{.callout-exercise} Modify the plot we just did, to show the relationship between median depth of sequencing (x-axis) and percentage of missing bases (y-axis). Colour the points according to the lineage column. -
Answer +:::{.callout-answer} Here is the modified code: @@ -539,5 +539,5 @@ ggplot(data = qc_metrics, The things we have changed were the aesthetics and labels. -
-::: \ No newline at end of file +::: +::: diff --git a/102-unix_cheatsheet.md b/materials/appendices/unix_cheatsheet.md similarity index 96% rename from 102-unix_cheatsheet.md rename to materials/appendices/unix_cheatsheet.md index 43acfce..6dbabe8 100644 --- a/102-unix_cheatsheet.md +++ b/materials/appendices/unix_cheatsheet.md @@ -1,14 +1,14 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- -# Unix Cheat Sheet +# Unix cheat sheet {.unnumbered} This document gives a brief summary of useful Unix commands. Anything within `{}` indicates a user-provided input. -:::note -If you are on Windows, you can install the _Windows Subsystem for Linux_ following [our instructions](104-wsl_windows.html). +:::{.callout-note} +If you are on Windows, you can install the _Windows Subsystem for Linux_ following [our instructions](../05-software/03-software_setup.md). ::: diff --git a/images/Ct_coverage_relationship.png b/materials/images/Ct_coverage_relationship.png similarity index 100% rename from images/Ct_coverage_relationship.png rename to materials/images/Ct_coverage_relationship.png diff --git a/images/MemoryCPUSize.PNG b/materials/images/MemoryCPUSize.PNG similarity index 100% rename from images/MemoryCPUSize.PNG rename to materials/images/MemoryCPUSize.PNG diff --git a/images/SummaryInstallationParameters.PNG b/materials/images/SummaryInstallationParameters.PNG similarity index 100% rename from images/SummaryInstallationParameters.PNG rename to materials/images/SummaryInstallationParameters.PNG 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a/images/workflow_medaka.svg b/materials/images/workflow_medaka.svg similarity index 100% rename from images/workflow_medaka.svg rename to materials/images/workflow_medaka.svg diff --git a/images/workflow_overview.svg b/materials/images/workflow_overview.svg similarity index 100% rename from images/workflow_overview.svg rename to materials/images/workflow_overview.svg diff --git a/07-nextflow_conda_setup.md b/materials/original/07-nextflow_conda_setup.md similarity index 97% rename from 07-nextflow_conda_setup.md rename to materials/original/07-nextflow_conda_setup.md index fb6b204..47a40c1 100644 --- a/07-nextflow_conda_setup.md +++ b/materials/original/07-nextflow_conda_setup.md @@ -1,17 +1,11 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- # Managing Bioinformatics Software -::: highlight - -## Questions - -- How can I install and manage bioinformatics software? -- How can I download and configure _Nextflow_ pipelines? - -## Learning Objectives +:::{.callout-tip} +#### Learning Objectives - Use the Conda package manager to manage and install software. - `conda create`, `conda env create -f env.yml`, `conda install`, `conda activate` @@ -46,7 +40,7 @@ In this course we will install the _Miniconda_ bundle, as it's lighter and faste 1. run `conda config --add channels conda-forge; conda config --add channels bioconda`. This adds two *channels* (sources of software) useful for bioinformatics and data science applications. -:::note +:::{.callout-note} _Anaconda_ and _Miniconda_ are also available for Windows and Mac OS. See the [Conda Installation Documents](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#regular-installation) for instructions. @@ -78,7 +72,7 @@ $ conda install --name ENV PROGRAM Where "PROGRAM" is the name of the software we want to install. -:::note +:::{.callout-note} One way to organise your software environments is to create an environment for each kind of analysis that you might be doing regularly. For example, you could have an environment named `imaging` with software that you use for image processing (e.g. Python's scikit-image or the ImageMagick package) and another called `deeplearn` with software you use for deep learning applications (e.g. Python's Keras). @@ -121,14 +115,14 @@ $ which python ~/miniconda3/envs/scipy/bin/python ``` -:::note +:::{.callout-note} **Tip** If you forget which environments you have created, you can use `conda env list` to get a list of them. ::: -:::exercise +:::{.callout-exercise} **Q1** @@ -174,7 +168,7 @@ multiqc --outdir FIXME results/fastqc
-
Answer +:::{.callout-answer} Create the environment with: @@ -207,13 +201,10 @@ $ multiqc --version Each of these commands should result in the version of the software being printed on the console. - - -
- +::: ::: -:::note +:::{.callout-note} **Quality Control for ONT Data** FastQC is designed for Illumina data. @@ -258,7 +249,7 @@ Commands: view View project script file(s) ``` -:::note +:::{.callout-note} **This was a note to self - can probably be removed from here** Making a local copy of the nextflow repository can be done in a few ways: @@ -395,9 +386,9 @@ The most informative are: ## Summary -:::highlight +:::{.callout-tip} -**Key Points** +#### Key Points - one - two diff --git a/101-setup_in_country.md b/materials/original/101-setup_in_country.md similarity index 100% rename from 101-setup_in_country.md rename to materials/original/101-setup_in_country.md diff --git a/103-tools_and_resources.md b/materials/original/103-tools_and_resources.md similarity index 97% rename from 103-tools_and_resources.md rename to materials/original/103-tools_and_resources.md index f0d634e..b891782 100644 --- a/103-tools_and_resources.md +++ b/materials/original/103-tools_and_resources.md @@ -1,5 +1,5 @@ --- -pagetitle: "SARS-CoV-2 Genomics" +pagetitle: "SARS Genomic Surveillance" --- # Tools and Resources diff --git a/104-wsl_windows.md b/materials/original/104-wsl_windows.md similarity index 97% rename from 104-wsl_windows.md rename to materials/original/104-wsl_windows.md index c9a410e..096ea6f 100644 --- a/104-wsl_windows.md +++ b/materials/original/104-wsl_windows.md @@ -9,7 +9,7 @@ The WSL is a tool that allows developers/users to run a GNU/Linux environment di ## Resources There are two main versions of WSL, WSL 1 and WSL 2. Microsoft recommends using WSL 2 as it offers improved performance (over WSL 1) and 100% system call compatibility. WSL 2 is available in Windows 10, Version 1903, Build 18362 or higher. -:::note +:::{.callout-note} To check your Windows version press **Windows Logo Key** + **R**, type **winver**, select OK. @@ -39,6 +39,6 @@ So if you want to navigate to `C:` drive, you have to do: cd /mnt/c ``` -:::note +:::{.callout-note} You may have noticed that the drive letter is in a small case. If you try to use an uppercase letter then you will get an error, "No such file or directory". ::: diff --git a/105-vs_code.md b/materials/original/105-vs_code.md similarity index 99% rename from 105-vs_code.md rename to materials/original/105-vs_code.md index 227f489..b5ab2bc 100644 --- a/105-vs_code.md +++ b/materials/original/105-vs_code.md @@ -80,7 +80,7 @@ If a folder/directory is open then it will look like the following: ![Folder/directory open in VS Code](images/vscode-folder.png) -:::note +:::{.callout-note} If only a file is open on VS Code, you will see that the colour of the bottom bar is purple. If a folder/directory is, then the colour will be blue. ::: diff --git a/materials/original/README.md b/materials/original/README.md new file mode 100644 index 0000000..bd5c9c0 --- /dev/null +++ b/materials/original/README.md @@ -0,0 +1,28 @@ +# Introduction to SARS-CoV-2 Sequencing Data Analysis + +:construction: **These materials are under active development and not ready to be used for teaching.** :construction: + +## Build Website Locally +### Requirements +* R - [https://www.r-project.org/](https://www.r-project.org/) +* pandoc - [https://pandoc.org/installing.html](https://pandoc.org/installing.html) + +### Step 1 +First you need to install `rmarkdown` package. To install run: +```bash +Rscript -e 'install.packages("rmarkdown")' +``` +> Note: On Windows make sure that R is added to Windows PATH. + +### Step 2 +To build the website run: +```bash +Rscript -e 'rmarkdown::render_site()' +``` +If there is no error then you will see a `_site` directory, under which you will find all the html files. + +## License + +These materials have been developed under a contract between the [Bioinformatics Training Facility (University of Cambridge)](https://bioinfotraining.bio.cam.ac.uk/) and the [New Variant Assessment Platform (NVAP)](https://www.gov.uk/guidance/new-variant-assessment-platform) from Public Health England. + +If you want to use these materials please get in touch with us at `bioinfotraining @ bio.cam.ac.uk`. diff --git a/materials/original/index.md b/materials/original/index.md new file mode 100644 index 0000000..682828d --- /dev/null +++ b/materials/original/index.md @@ -0,0 +1,100 @@ +--- +pagetitle: "SARS Genomic Surveillance" +--- + +# Introduction to SARS-CoV-2 Genomics + +:::{.callout-tip} + +This course will teach you how to analyse sequencing data from SARS-CoV-2 amplicon samples to generate consensus sequences ready to be uploaded to public databases such as GISAID and to be used in other downstream analysis such as variant annotation and phylogeny. +We will teach the use of a standardised analysis pipeline, which can work with both Illumina and Nanopore data. +We will also cover how to assign sequences to lineages, identify variants of interest/concern and produce visualisations to communicate your findings. +Along the way, you will gain foundational bioinformatic skills, including the use of the Unix command line and learn to write simple scripts to ensure your analysis is reproducible. + +::: + +**Learning Goals:** + +- Recognise the uses of genomic surveillance to inform public health actions during a pandemic. +- Assemble high-quality SARS-CoV-2 genome sequences from raw sequencing data. +- Assign consensus sequences to lineages and identify variants of interest/concern. +- Capture high-quality metadata, recognising its impact on downstream analyses. +- Construct phylogenetic trees to contextualise new samples in a set of background samples. +- Produce visualisations to communicate these findings and help inform public health action. + +**Learning Objectives:** + +By the end of this course, learners should be able to: + +- Enumerate examples of how the genomic surveillance of SARS-CoV-2 has impacted public health decisions during the ongoing pandemic. +- Contrast the different technologies and protocols commonly used for SARS-CoV-2 sequencing, including the pros and cons of each. +- Understand the importance and uses of metadata such as geolocation, date of collection and protocols used. +- Use the Unix command line to navigate a filesystem, manipulate files, launch programs and write scripts for reproducible analysis. +- Recognise the structure of common sequence file formats in bioinformatics and run basic quality control tools on them. +- Summarise the steps in the bioinformatic pipeline used for assembling SARS-CoV-2 genomes from high-throughput amplicon sequencing. +- Apply the `nf-core/viralrecon` _Nextflow_ pipeline to generate a consensus sequence from Illumina and Nanopore data. +- Assess the quality of the consensus sequences and identify high-quality sequences suitable for downstream analyses and submission to public databases. +- Assign sequences to lineages and variants using `pangolin` and `nextclade`. +- Interactively explore data and produce visualisations using _Nextstrain_ tools. +- Manage and install bioinformatics software using _Conda_ and set up _Nextflow_ workflows. + + +## Target Audience + +This course is aimed at life scientists interested in the bioinformatic analysis of SARS-CoV-2 genomic data. +In particular it will benefit those working in SARS-CoV-2 sequencing facilities, such as public health authority labs. +Those with prior experience in bioinformatics may also benefit from the later sessions of the course, which cover specific tools for SARS-CoV-2 (earlier sessions of the course cover basic skills such as the Unix command line and could be skipped by experienced bioinformaticians). + + +## Prerequisites + +We assume no prior bioinformatics experience or experience with the tools introduced in this course. +An elementary knowledge of molecular and viral biology is assumed (concepts such as: DNA, RNA, PCR, primers, SNPs). + + +## Acknowledgements + +These materials have been developed as a collaboration between the [Bioinformatics Training Facility](https://bioinfotraining.bio.cam.ac.uk/) at the University of Cambridge and the [New Variant Assessment Platform (NVAP)](https://www.gov.uk/guidance/new-variant-assessment-platform) program from the _UK Health Security Agency_. + +Our partners also include [COG Train](https://www.cogconsortium.uk/cog-train/about-cog-train/). +We also thank the wider community for publicly sharing training resources, including: + +- The [workshop video series from _CLIMB BIG DATA_](https://www.youtube.com/channel/UCdiGIIyryQL3x-Og5uiY1rw). +- The [Carpentries](https://carpentries.org/) project, in particular for their [Unix Shell](https://swcarpentry.github.io/shell-novice/) lesson, which we adapted for this workshop. + + +## Citation & Authors + +You can cite these materials as: + +Tavares H, Kumar A, Castle M & UKHSA New Variant Assessment Platform Team (2022) "cambiotraining/sars-cov-2-genomics: Introduction to Sars-CoV-2 Genomics", https://cambiotraining.github.io/sars-cov-2-genomics + +Or, in BibTeX format: + +``` +@Misc{, + author = {Tavares, Hugo and Kumar, Ankit and Castle, Matt and UKHSA New Variant Assessment Platform Team}, + title = {cambiotraining/sars-cov-2-genomics: Introduction to Sars-CoV-2 Genomics}, + month = {March}, + year = {2022}, + url = {https://cambiotraining.github.io/sars-cov-2-genomics}, +} +``` + +**Please make sure to include a link to the materials in the citation.** (we will add a DOI in due time) + +The contributing members from University of Cambridge Bioinformatics Training Facility team are: + +- Matt Castle, Bioinformatics Training Manager +- Hugo Tavares, Senior Teaching Associate +- Ankit Kumar, Teaching Assistant + +The UKHSA's NVAP Team members that supported these materials are: + +- Dr Leena Inamdar, NVAP Programme Lead and Global Health Lead +- Dr Babak Afrough, Senior Project Manager +- Aude Wilhelm, Senior Epidemiology Scientist +- Richard Myers, Data Analytics Surveillance Head Bioinformatician +- Sam Sims, Bioinformatician +- Kate Edington, Bioinformatician +- Constantina Laou, Specialist Lab Advisor diff --git a/cov19-artic-pipeline.Rproj b/sars-quarto.Rproj similarity index 72% rename from cov19-artic-pipeline.Rproj rename to sars-quarto.Rproj index 91d3d74..dac98af 100644 --- a/cov19-artic-pipeline.Rproj +++ b/sars-quarto.Rproj @@ -12,7 +12,5 @@ Encoding: UTF-8 RnwWeave: Sweave LaTeX: pdfLaTeX -AutoAppendNewline: Yes -StripTrailingWhitespace: Yes - -BuildType: Website +QuitChildProcessesOnExit: Yes +DisableExecuteRprofile: Yes diff --git a/sars-quarto.code-workspace b/sars-quarto.code-workspace new file mode 100644 index 0000000..585c1ce --- /dev/null +++ b/sars-quarto.code-workspace @@ -0,0 +1,11 @@ +{ + "folders": [ + { + "path": "." + } + ], + "settings": { + "editor.insertSpaces": true, + "editor.tabSize": 2 + } +} \ No newline at end of file diff --git a/101-data.md b/setup.md similarity index 84% rename from 101-data.md rename to setup.md index b230e9b..24c4690 100644 --- a/101-data.md +++ b/setup.md @@ -1,8 +1,17 @@ ---- -pagetitle: "SARS-CoV-2 Genomics" ---- +# Data & Software {.unnumbered} -# Data +:::{.callout-tip level=2} +#### Workshop Attendees + +If you are attending one of our workshops, we will provide a training environment with all of the required software and data. You do not need to download or install anything ahead of the workshop. +If you want to setup your own computer to run the analysis demonstrated on this course, you can follow the instructions below. +::: + +## Software + +The software installation for this course is quite complex and detailed in a [separate section](TODO). + +## Data Different datasets are used throughout these materials. This page provides links to download each dataset, with a brief description for each of them. @@ -12,8 +21,8 @@ We have split the data between sequencing platforms. ### UK Sequences -These samples were downloaded from SRA and include samples collected between X and X in the UK. -These data are used in the following sections of the materials: [Consensus Assembly](04-consensus.html), [Lineages and Variants](05-lineage_analysis.html) and [Building Phylogenies](06-phylogeny.html). +These samples were downloaded from SRA and include samples collected in the UK. +These data are used in the following sections of the materials: [Consensus Assembly](materials/02-isolates/01-consensus.md), [Lineages and Variants](materials/02-isolates/02-lineages.md) and [Building Phylogenies](materials/02-isolates/03-phylogeny.md). - [ Download (zip file)]() @@ -30,8 +39,8 @@ fastq-dump --split-3 --gzip ERR5728910 ERR5728911 ERR5728913 ERR5742126 ERR57422 ### South Africa -These samples were downloaded from SRA and include samples collected between X and X in South Africa. -These data are used in the worked example [South Africa (Illumina)](08-case_study_southafrica.html). +These samples were downloaded from SRA and include samples collected between Nov and Dec 2021 in South Africa. +These data are used in the worked example [South Africa (Illumina)](materials/03-case_studies/02-southafrica.md). We provide two versions of the data: the full data includes 24 samples (more realistic sample size), whereas the small version includes only 8 samples (quicker processing time for practising). @@ -51,7 +60,7 @@ fastq-dump --split-3 --gzip SRR17051908 SRR17051923 SRR17051916 SRR17051953 SRR1 ### EQA panels The standard panels for _External Quality Assessment_ provided by NEQAS were sequenced on an Illumina platform by UKHSA and used to generate four sets of data for training purposes. -These data can be used for the extended exercise: [EQA (Exercise)](08-case_study_eqa.html). +These data can be used for the extended exercise: [EQA (Exercise)](materials/03-case_studies/03-eqa.md). We provide a link to each of the four datasets below (the datasets are very similar to each other): @@ -65,8 +74,8 @@ We provide a link to each of the four datasets below (the datasets are very simi ### India -These samples were downloaded from SRA and include samples collected between X and X in India and sequenced by the [National Institute of Mental Health and Neurosciences](https://nimhans.ac.in/). -These data are used for the exercises in the following sections of the materials: [Consensus Assembly](04-consensus.html), [Lineages and Variants](05-lineage_analysis.html) and [Building Phylogenies](06-phylogeny.html). +These samples were downloaded from SRA and include samples collected in India and sequenced by the [National Institute of Mental Health and Neurosciences](https://nimhans.ac.in/). +These data are used for the exercises in the following sections of the materials: [Consensus Assembly](materials/02-isolates/01-consensus.md), [Lineages and Variants](materials/02-isolates/02-lineages.md) and [Building Phylogenies](materials/02-isolates/03-phylogeny.md). - [ Download (zip file)]() - Platform: MinION @@ -90,8 +99,8 @@ fastq-dump --split-3 --gzip SRR14494107 SRR14493634 SRR14493632 SRR14493631 SRR1 ### Switzerland -These samples were downloaded from SRA and include samples collected between X and X in Switzerland and sequenced by the Institute for Infectious Diseases, University of Bern. -These data are used in the worked example [Switzerland (Nanopore)](08-case_study_switzerland.html). +These samples were downloaded from SRA and include samples collected between Nov 2021 and Jan 2022 in Switzerland and sequenced by the Institute for Infectious Diseases, University of Bern. +These data are used in the worked example [Switzerland (Nanopore)](materials/03-case_studies/01-switzerland.md). We provide two versions of the data: the full data includes 65 samples (more realistic sample size), whereas the small version includes only 10 samples (quicker processing time for practising). @@ -120,7 +129,7 @@ fastq-dump --split-3 --gzip ERR8971298 ERR8961150 ERR8961147 ERR8961133 ERR89611 ### EQA panels The standard panels for _External Quality Assessment_ provided by NEQAS were sequenced on an ONT platform by TODO. -These data can be used for the extended exercise: [EQA (Exercise)](08-case_study_eqa.html). +These data can be used for the extended exercise: [EQA (Exercise)](materials/03-case_studies/03-eqa.md). We provide a link to datasets corresponding to two different runs: diff --git a/setup_environment.sh b/setup_environment.sh deleted file mode 100644 index 10ae8d1..0000000 --- a/setup_environment.sh +++ /dev/null @@ -1,141 +0,0 @@ -#!/bin/bash -set -e - -if [ "${EUID:-$(id -u)}" -eq 0 ]; then - echo "Please do not run this script as root - run it from the participant's user account." - exit 0 -fi - -cd $HOME - -# Update user's path -echo " -export PATH=$HOME/bin/:\$PATH -" >> $HOME/.bashrc - -# system updates and install git -echo "Updating apt packages..." -sudo apt update && sudo apt upgrade -y && sudo apt autoremove -y -sudo apt install -y git - - -#### Conda #### -echo "Installing conda..." - -# remove any previous installation -rm -rf miniconda3 - -wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -bash Miniconda3-latest-Linux-x86_64.sh -b -rm Miniconda3-latest-Linux-x86_64.sh -export PATH="$HOME/miniconda3/bin/:$PATH" -conda init # adds conda to .bashrc - -# setup conda channels -conda config --add channels defaults; conda config --add channels bioconda; conda config --add channels conda-forge - - -#### Bioinformatic Packages #### -echo "Installing bioinfo packages..." - -# mamba for fast installation -conda install -y mamba - -# conda packages -# mamba install -y bcftools=1.15.1 mafft=7.505 iqtree=2.2.0.3 treetime=0.9.1 fastqc=0.11.9 multiqc=1.13a bowtie2=2.4.5 tbb=2020.2 igv=2.13.2 figtree=1.4.4 seqkit=2.3.0 -mamba install -y datrie -pip install biopython -pip install snakemake==7.16.0 -mamba install -y mafft iqtree treetime igv figtree seqkit minimap2 -mamba install -y cov-ert::jclusterfunk -mamba install -y gofasta=0.0.5 ucsc-fatovcf -mamba install -y usher -mamba install -y git-lfs -mamba install -y nextclade - -pip install git+https://github.com/cov-lineages/scorpio.git -pip install git+https://github.com/cov-lineages/constellations.git -pip install git+https://github.com/cov-lineages/pangolin-data.git -pip install git+https://github.com/artic-network/civet.git -pip install git+https://github.com/cov-lineages/pangolin.git - - - -# # CIVET -# git clone https://github.com/artic-network/civet.git -# cd civet -# mamba env create -f environment.yml -# mamba install -y -n civet snakemake-minimal=7.16.0 -# source activate civet -# pip install . -# civet --version -# conda deactivate -# cd ../ -# rm -rf civet - -# AliView -wget https://ormbunkar.se/aliview/downloads/linux/linux-version-1.28/aliview.tgz -tar -xzvf aliview.tgz -rm aliview.tgz -echo "alias aliview='java -jar $HOME/aliview/aliview.jar'" >> $HOME/.bashrc - - -#### Singularity #### -echo "Installing singularity (requires sudo password)..." - -sudo apt install -y runc cryptsetup-bin -CODENAME=$(lsb_release -cs) -wget -O singularity.deb https://github.com/sylabs/singularity/releases/download/v3.10.2/singularity-ce_3.10.2-${CODENAME}_amd64.deb -sudo dpkg -i singularity.deb -rm singularity.deb - - -#### Nextflow #### -echo "Installating Nextflow..." -# Java -sudo apt install -y curl -curl -s https://get.sdkman.io | bash && source "$HOME/.sdkman/bin/sdkman-init.sh" && sdk install java - -# Nextflow -wget -qO- https://get.nextflow.io | bash -chmod +x nextflow -mkdir bin && mv nextflow bin/ - -# setup nextflow -echo " -conda { - useMamba = true - createTimeout = '1 h' - cacheDir = \"$HOME/.nextflow-conda-cache/\" -} -singularity { - enabled = true - pullTimeout = '1 h' - cacheDir = \"$HOME/.nextflow-singularity-cache/\" -} -" >> $HOME/.nextflow/config - - -#### R + RStudio #### -echo "Installing R and RStudio..." - -# Install R: https://cloud.r-project.org/bin/linux/ubuntu -sudo apt install -y --no-install-recommends software-properties-common dirmngr -wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc -sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" -sudo apt install -y r-base r-base-dev libcurl4-openssl-dev libssl-dev libxml2-dev -sudo Rscript -e "install.packages(c('tidyverse', 'janitor'))" - -# Download and install RStudio -sudo apt install -y libclang-dev libpq5 libssl-dev # dependencies - -if [ $CODENAME = "jammy" ] -then - wget -O rstudio.deb https://download1.rstudio.org/desktop/jammy/amd64/rstudio-2022.07.2-576-amd64.deb -else - wget -O rstudio.deb https://download1.rstudio.org/desktop/bionic/amd64/rstudio-2022.07.2-576-amd64.deb -fi -sudo dpkg -i rstudio.deb -rm rstudio.deb - -echo "Successfully installed all dependencies"

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