From 09afdc84aff7cb0a53b923648ac2e44e9eee0a17 Mon Sep 17 00:00:00 2001 From: Hugo Tavares Date: Tue, 12 Mar 2024 17:10:58 +0000 Subject: [PATCH] update ww materials --- materials/01-intro/02-ngs.md | 2 +- materials/02-isolates/02-qc.md | 6 +- materials/04-wastewater/01-ww_surveillance.md | 5 + materials/04-wastewater/02-ww_abundance.md | 387 ++- .../libs/bootstrap/bootstrap-icons.css | 2078 ++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 176200 bytes .../libs/bootstrap/bootstrap.min.css | 12 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 203 ++ .../libs/quarto-html/quarto.js | 897 ++++++ .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + .../04-wastewater/03-ww_visualisation.qmd | 188 ++ materials/04-wastewater/04-ww_mutations.md | 52 + materials/04-wastewater/eda_variants.r | 38 + materials/04-wastewater/sampling_variation.r | 34 +- materials/04-wastewater/ww_notes.md | 202 ++ materials/images/mosdepth.png | Bin 0 -> 325798 bytes .../viralrecon_workflow_ww_illumina.svg | 1250 ++++++++ materials/images/workflow_ww_overview.svg | 2522 +++++++++-------- materials/images/ww_depth_and_coverage.png | Bin 0 -> 81671 bytes utils/wastewater/tidy_freyja_viralrecon.py | 2 +- 25 files changed, 6641 insertions(+), 1269 deletions(-) create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap-icons.css create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap.min.css create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap.min.js create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/clipboard/clipboard.min.js create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/anchor.min.js create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/popper.min.js create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/quarto.js create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/tippy.css create mode 100644 materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/tippy.umd.min.js create mode 100644 materials/04-wastewater/03-ww_visualisation.qmd create mode 100644 materials/04-wastewater/04-ww_mutations.md create mode 100644 materials/04-wastewater/eda_variants.r create mode 100644 materials/04-wastewater/ww_notes.md create mode 100644 materials/images/mosdepth.png create mode 100644 materials/images/viralrecon_workflow_ww_illumina.svg create mode 100644 materials/images/ww_depth_and_coverage.png diff --git a/materials/01-intro/02-ngs.md b/materials/01-intro/02-ngs.md index 4652668..570677c 100644 --- a/materials/01-intro/02-ngs.md +++ b/materials/01-intro/02-ngs.md @@ -129,7 +129,7 @@ zcat sequences.fq.gz | wc -l If we want to know how many sequences there are in the file, we can divide the result by 4 (since each sequence is always represented across four lines). -### FASTQ Quality Control +### FASTQ Quality Control {#sec-fastqc} One of the most basic tasks in Illumina sequence analysis is to run a quality control step on the FASTQ files we obtained from the sequencing machine. diff --git a/materials/02-isolates/02-qc.md b/materials/02-isolates/02-qc.md index 8380334..e1fcb81 100644 --- a/materials/02-isolates/02-qc.md +++ b/materials/02-isolates/02-qc.md @@ -55,13 +55,13 @@ This table also contains information about the lineage/clade assigned to each sa 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](03-lineages.md). -:::{.callout-warning} + ![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) @@ -78,7 +78,7 @@ It can mean two things: ::: -### Amplicon Depth of Coverage +### Amplicon Depth of Coverage {#sec-amplicon-coverage} The next section of the report - **Amplicon Coverage Heatmap** - contains a graphical representation of the average depth of coverage for each PCR amplicon in the ARTIC protocol (i.e. the average number of reads mapped to each amplicon interval). This plot is extremely useful to identify common PCR dropouts occurring across many samples. diff --git a/materials/04-wastewater/01-ww_surveillance.md b/materials/04-wastewater/01-ww_surveillance.md index 6e53d6f..967f891 100644 --- a/materials/04-wastewater/01-ww_surveillance.md +++ b/materials/04-wastewater/01-ww_surveillance.md @@ -55,6 +55,11 @@ Different methods may work better or worse for different samples and situations. Also depends on resources available (equipment and reagents). +## Metadata + +See https://github.com/pha4ge/Wastewater_Contextual_Data_Specification?tab=readme-ov-file + + ## Glossary - WBE - wastewater-based epidemiology diff --git a/materials/04-wastewater/02-ww_abundance.md b/materials/04-wastewater/02-ww_abundance.md index 2096d6f..ca9e9d7 100644 --- a/materials/04-wastewater/02-ww_abundance.md +++ b/materials/04-wastewater/02-ww_abundance.md @@ -2,59 +2,155 @@ pagetitle: "SARS Genomic Surveillance" --- -# Wastewater surveillance +# Lineage Abundance -:::{.callout-caution} - -Under development +:::{.callout-tip} +#### Learning Objectives +- Summarise the bioinformatic steps involved in estimating lineage abundance from sequencing data. +- Apply the `nf-core/viralrecon` pipeline for wastewater analysis. +- Evaluate sample quality with a particular focus on abundance estimation. +- Interpret the results from the Freyja software and its limitations. +- Prepare the abundance estimates for downstream analysis. ::: ## Bioinformatic analysis -![](images/workflow_wastewater_overview.svg) +The bioinformatic analysis of wastewater sequencing data broadly involves the following steps: + +- Filter the raw sequencing data to remove low-quality reads and trim Illumina adaptors. +- Map the reads to the _Wuhan-Hu-1_ reference genome. +- Trim the primers from the aligned reads based on the primer location BED file. +- Identify changes relative to the reference sequence. +- Estimate the abundance of known SARS-CoV-2 lineages. + +![Overview of the lineage abundance pipeline. The diagram illustrates Illumina sequencing, where each PCR amplicon is covered by 2 reads (read 1 and read 2). The PCR amplicons typically overlap, therefore the reads may also overlap in their alignment positions.](images/workflow_ww_overview.svg) + +Most of these steps are in common with the [processing of clinical samples](../02-isolates/01-consensus.md). +The main difference is the last step, where we use a specialised software to estimate the abundance of viral lineages based on the mutations found. +There are different software packages designed to estimate lineage abundance from sequencing data and new ones are likely to be developed as the field evolves. +See [Kayikcioglu et al. 2023](http://dx.doi.org/10.7717/peerj.14596) and [Sutcliffe et al. 2023](https://doi.org/10.1101/2023.12.20.572426) for an overview of the software packages available and their performance on synthetic data. -The command to run our analysis is very similar to what was explained in the [consensus assembly of clinical isolates](../02-isolates/01-consensus.md): +The software that we will use, implemented in the `nf-core/viralrecon` pipeline is called **Freyja**. +This tool takes advantage of the large number of known lineages of SARS-CoV-2, which it pulls from the [UShER global phylogeny](https://academic.oup.com/mbe/article/38/12/5819/6361626). +This global phylogeny is updated daily, and so is the Freyja database. +Freyja combines the known mutations for each lineage with the frequency of observed mutations estimated from our sequencing data to estimate the frequency of those lineages. + +## Wastewater pipeline {#sec-ww-viralrecon} + +We will use the same workflow covered in the [consensus assembly of clinical isolates](../02-isolates/01-consensus.md): `nf-core/viralrecon`. +The current version of this pipeline includes the Freyja tool for inference of lineage abundance. + +The command to process our samples is very similar to what we've seen before: ```bash nextflow run nf-core/viralrecon \ -r dev -profile singularity \ --platform illumina \ - --input SAMPLESHEET_CSV \ + --input YOUR_SAMPLESHEET_CSV \ --outdir OUTPUT_DIRECTORY \ --protocol amplicon \ --genome 'MN908947.3' \ - --primer_set artic \ - --primer_set_version PRIMER_VERSION \ + --fasta PATH_TO_FASTA_FILE \ + --gff PATH_TO_GFF_FILE \ + --primer_bed PATH_TO_BED_FILE \ --skip_assembly --skip_asciigenome \ --skip_consensus ``` -The key differences are: +The key difference is: - `-r dev`: we are using the **development version** of the pipeline, which is not yet released as an official version. - The reason is that Freyja is currently (Feb 2024) only available in the development version. - Once a new version of the pipleine is released (>= 2.7), it should no longer be necessary to use the development version. + This is because Freyja is currently (Feb 2024) only available in the development version. + Once a new version of the pipeline is released (>= 2.7), it should no longer be necessary to use the development version. - `--skip_consensus`: in this case we skip the generation of a consensus FASTA file. Since wastewater samples are mixed, it doesn't make sense to generate a consensus FASTA file, as it would potentially include mutations from different lineages in the final consensus, creating artificial recombinant genomes. +In addition, there are [other options](https://nf-co.re/viralrecon/dev/parameters#skip_freyja) that can be specified with `viralrecon` to modify Freyja's default behaviour. +We highlight two important ones below: `--freyja_depthcutoff` and `--freyja_repeats`. + + +:::{.callout-tip} +#### Samplesheet + +To automatically create a samplesheet CSV file from the FASTQ files in a directory, we can use [this script](https://github.com/nf-core/rnaseq/blob/master/bin/fastq_dir_to_samplesheet.py) developed by the nf-core team. +We have downloaded the script to a directory called `utilities` and produced the samplesheet using the following command: + +```bash +python utilities/fastq_dir_to_samplesheet.py -r1 '_1.fastq.gz' -r2 '_2.fastq.gz' data/reads/ samplesheet.csv +``` + +- `-r1` defines the suffix of read 1 files +- `-r2` defines the suffix of read 2 files +- Then we include the input directory with FASTQ files +- At the end the name of the output samplesheet file + +This will generate a CSV file where the `sample` names are taken from the FASTQ filenames. +If you want to use your own custom names, you can open this file in Excel and edit it the sample names to suit your needs. +::: + +:::{.callout-note collapse=true} +#### Pipeline details + +The processing of raw sequencing reads goes through several steps already covered in @sec-viralrecon. +The main new step is the **lineage abundance** estimation done by Freyja. +This software consists of 4 main commands: + +- `freyja variants` identifies mutations from the sequencing reads to obtain their frequencies and sequencing depth. +- `freyja update` is used to update the lineage database, pulled from the UShER global phylogeny. +- `freyja demix` does the actual estimation of lineage abundances. +- `freyja boot` calculates confidence intervals for the abundance estimates using a bootstrap method (see section below). + +![Diagram of the Illumina workflow for wastewater samples. Some steps have been omitted for simplification.](./images/viralrecon_workflow_ww_illumina.svg) +::: + + ### Depth threshold -There are [other options](https://nf-co.re/viralrecon/dev/parameters#skip_freyja) that can be specified with `viralrecon` to modify the default behaviour of Freyja. -A couple that are worth highlighting are: +The option `--freyja_depthcutoff` determines the minimum depth of sequencing for a site to be used in the Freyja analysis. +The default is '0', meaning Freyja uses all the sites in its analysis, as long as at least 1 read is mapped to the reference. +This may seem too low, however recall that Freyja will use all the mutations across the genome, so as long as the average depth of sequencing is high enough (e.g. >100x) and the genome is well covered (e.g. >80%), then even if some of the sites have low depth, the accuracy of the final estimates might still be good. + +Also, Freyja takes the depth of each site into account when it does it's calculation, giving more weight to sites with more reads than those with very few reads. + +The default threshold (`--freyja_depthcutoff 0`) usually leads to more lineages being reported in the output, although some of the low-frequency ones may be innacurate. +We may increasing this threshold to be more strict (e.g. `--freyja_depthcutoff 5`), however this will lower our resolution and some minor lineages may not be captured. + +As a first step we recommend keeping the default threshold and only increase it if you notice many false positive lineages systematically being reported. -- `--freyja_depthcutoff` determines the minimum depth of sequencing for a site to be used in the Freyja analysis. The default '0', i.e. Freyja uses all the sites in its analysis, as long as at least 1 read is mapped to the reference. - This may seem insufficient, however recall that Freyja will use all the mutations across the genome, so as long as the average depth of sequencing is high enough (e.g. >100x), then even if some of the sites have low depth the accuracy of the final estimates might still be good. ### Confidence intervals -- `--freyja_repeats` determines the number of bootstrap repeats used to estimate confidence intervals for the abundance estimates. The default is 100 bootstrap replicates, which should be fine to get an idea of the uncertainty of those estimates. However, this step is very computationally demanding, substantially increasing the time to run the analysis (it is, essentially, running the Freyja step 100 times for each sample). Therefore, it can sometimes be advantageous to "turn off" this option by setting `--freyja_repeats 1`. In that case, make sure to ignore the confidence intervals that are output by the pipeline. +The option `--freyja_repeats` is used to configure the calculation of confidence intervals for the abundance estimations. +Freyja can quantify the uncertainty of its lineage abundance estimates, related to the sequencing depth. +Our sequencing reads can be thought of as a sample from all the viral molecules present in our sequencing library. +Therefore, a site with 10x reads will provide lower accuracy than a site with 100x reads. + +The way Freyja quantify the uncertainty in lineage abundance due to sequencing depth is to use a method known as **bootstrap resampling**. +This method works by taking a sample of reads with replacement and re-calculating the lineage abundance. +This process is repeated several times (e.g. 100 times) to generate a distribution of abundances, representing our uncertainty in the estimates due to the random sampling of reads in each position. +Freyja then uses this distribution to calculate a 95% confidence for each lineage abundance. +Watch [this video](https://youtu.be/Xz0x-8-cgaQ?feature=shared) if you want to learn more about bootstrap resampling in general. + +The `viralrecon` option `--freyja_repeats` determines the number of bootstrap repeats used to estimate confidence intervals for the abundance estimates. +The default is 100 bootstrap replicates, which should be enough to get robust confidence intervals. + +However, this step is **very computationally demanding**, substantially increasing the time to run the analysis (it is, essentially, running the Freyja step 100 times for each sample). +Therefore, it can sometimes be advantageous to "turn off" this option by setting `--freyja_repeats 1`. +In that case, make sure to _ignore the confidence intervals that are output by the pipeline_. + +:::{.callout-important} +#### Freyja confidence intervals + +Note that the bootstrap confidence intervals generated by Freyja only account for the uncertainty related to sequencing depth. +They do not take into account variation due to sampling at the wastewater treatment plant, and variation due to sample manipulation in the lab protocols. +::: ## Output Files -After running the pipeline, we get several output files. +After running the pipeline, we get several output files (see `viralrecon`'s [documentation](https://nf-co.re/viralrecon/)). These are very similar to what has been described for [clinical isolates](../02-isolates/02-qc.md). As a reminder, here are some files of interest: @@ -69,9 +165,11 @@ The new directories of particular interest for wastewater analysis are: - `variants/freyja/variants`: directory containing the output files from the `freyja variants` command, which includes all the variants (mutations) considered by Freyja in its lineage abundance calculations. These are somewhat redundant with the files in `variants/ivar/variants_long_table.csv`. -## Freyja Output +## Freyja `demix` output {#sec-demix} -Freyja outputs files in a non-standard format. +The main step of the Freyja analysis is the command `freyja demix` (which `viralrecon` runs for us), where the mutations present in the sequencing reads are used to infer the abundance of individual lineages. + +These files are text-based, but in a non-standard format. Here is an example from one of our samples: ``` @@ -90,26 +188,249 @@ Here is the meaning of each line from this file: 3. The name of each individual lineage detected in the sample. 4. The corresponding frequencies of each lineage from the previous line, in descending order. 5. The "residual" variation left from the statistical model used to estimate the frequencies; this value does not have an easy interpretation. -6. The percentage of the genome covered at 10x depth. We can also obtain this information from the regular MultiQC report. +6. The percentage of the genome covered at 10x depth. We can also obtain this information from the regular MultiQC report (see more about this in the QC section below). + +We will later use a custom script to "tidy" these results into a more standard CSV format, aggregating the results from several samples and combining it with our metadata. + +:::{.callout-important} +#### Ambiguous lineage abundance + +For similar lineages (i.e. with few distinguishing mutations) it can sometimes happen that Freyja cannot distinguish their abundance. +This usually affects lower-frequency lineages, as there are fewer reads to cover them to start with. + +In those situations, Freyja will assign equal frequency to all of them. +This is important to know, as it means that the frequency of those lineages is likely to be inaccurate. + +Generally, low-abundance lineages (< 1%) should be interpreted with caution, in particular for samples with low median depth of sequencing. +::: + + +## Quality Control + +As usual, the first thing to do after running our pipeline is to check the quality of our results. +For wastewater samples, there are a few main diagnostics to pay attention to: + +- The average depth of sequencing of our samples, i.e. the average number of reads mapped to each position of the genome. +- Whether there was any amplicon dropout. +- The percentage of the genome covered at a certain depth of sequencing. + +In addition, general quality metrics should also be considered, which we also cover below. + +These metrics are all available from the MultiQC report generated by the pipeline saved in `multiqc/multiqc_report.html`. + +### General metrics + +One of the first sections of the MultiQC report is "**Variant calling metrics**", which contains a table with several metrics of interest, for example: + +``` +Sample # Input reads % Non-host reads # Trimmed reads (fastp) % Mapped reads # Mapped reads # Trimmed reads (ivar) Coverage median % Coverage > 1x % Coverage > 10x +Sample1 751204 NA 697880 697880 100.00 697867 1246.00 100.00 100.00 +Sample2 377092 NA 331424 331424 100.00 331404 377.00 99.00 95.00 +Sample3 174642 NA 134338 134338 100.00 134282 65.00 99.00 90.00 +Sample4 14378 NA 12808 12808 100.00 12800 24.00 97.00 77.00 +``` + +These columns give us: + +- The total number of reads that we started with. +- The percentage of those reads that matched the human genome. +- The number of reads that passed the quality filtering step, which includes adapter removal and quality trimming (using the `fastp` software). +- The number and percentage of mapped reads. This is a good indicator of whether the sample contains non-human contamination, as that would result in lower mapping rates to the SARS-CoV-2 reference genome. +- The number of reads that had the amplicon primer site trimmed (using `ivar` software). It is expected that most reads will start at the primer site, so this number should be similar to the number of mapped reads. However, depending on the type of library used, this number might be smaller. + +These initial metrics are essential to analyse, as it informs us of whether our samples are free of contamination and generally aligned well to the SARS-CoV-2 reference genome. + +We should also assess the quality of our raw reads, by looking at the section "**FastQC (raw reads)**", as detailed in @sec-fastqc. + + +### Sequencing depth and coverage + +The "Variant calling metrics" table has a few other metrics of particular interest for wastewater samples, namely: + +- **Coverage median:** the number of reads that maps to each position of the genome _on average_. +- **% Coverage > 10x:** the fraction of the genome that is covered with at least 10 reads. + +These two metrics together give us an idea of how much of the genome we have effectively sequenced and how deeply we have sequenced it. +As mentioned earlier, the sequencing depth is critical for the estimation of lineage abundance. +The more we sequence a sample, the more accurate are _Freyja_'s abundance estimates, as well as our ability to detect low-abundance lineages. +The two metrics on this table are useful summaries, however we can get an even more detailed insights from looking at the plot show in the section "**Mosdepth** > **Cumulative coverage distribution**" (see figure below). -## Mutation co-occurrence +![Snapshot of the coverage distribution plot from the `mosdepth` software. This plot allows us to evaluate what fraction of the genome is covered at a certain depth of sequencing. There are two ways to interpret this plot: "what fraction of the genome is covered at a certain minimum depth?" (vertical line, left); or "what is the maximum depth covering at least X% of the genome?" (horizontal line, right).](images/mosdepth.png) -As mentioned, low-frequency lineages pose a challenge in the analysis of mixed samples, as it becomes difficult to distinguish their presence from technical background noise (e.g. due to sequencing errors). -An approach to help distinguish errors from the true presence of a rare lineage is to look for mutations that co-occur in the same read or read pair. -As it would be very unlikely that two (or more) errors would occur in exactly the same read pair, the presence of co-occurring mutations provide a high-confidence signal that the respective lineage is present in the sample. -This approach requires that the lineages of interest have enough distinguishing mutations within close proximity to each other (i.e. in the same PCR amplicon). +There is no defined rule for which depth and/or coverage thresholds to use to keep or remove a sample from downstream analysis. +Take "Sample4" in our example above: it only has a median depth of 24x with 77% of the genome covered at 10x, and a very sharp curve in the cumulative coverage plot. +Should we remove this sample from further analysis? +On the one hand, this sample may give less accurate information compared to other samples. +On the other hand, if a high-frequency variant is present, we may still be able to detect it and it may therefore be a useful data point for our analysis. -Another application of co-occurring mutations is to provide a stronger evidence for new emerging lineages. -If the same pattern of co-occurring mutations is observed across multiple samples and at increasing frequencies over time, it may be cause for concern and require close monitoring. -See, for example, the study of [Jahn et al 2022](https://doi.org/10.1038/s41564-022-01185-x), where they demonstrate that co-occurring mutations characteristic of the Alpha variant were detected in wastewater samples from two Swiss cities around 2 weeks before its first report from clinical samples. +### Amplicon dropout +Another consideration to take is whether there was any PCR amplicon dropout (detailed in @sec-amplicon-coverage). +In particular, if you sistematically failed to amplify the _S_ (_Spike_) gene, where many lineage-distinguishing mutations occur, this may lead to poorer lineage abundance estimation, as we are missing data in those regions of the genome. +Again, there is no rule about whether or not to exclude a sample from analysis based on amplicon dropout, but it is important to look at the **Amplicon Coverage Heatmap** of the _MultiQC_ report and make a note of any samples that may be particularly problematic. -## Glossary +A sistematic amplicon dropout may indicate that improvements are needed during the lab protocols or that new primer panels are required. -- WBE - wastewater-based epidemiology -- WWTP - wastewater treatment plants -- WGS - whole-genome sequencing \ No newline at end of file +In some cases, amplicon dropout is lineage-specific, for example due to mutations in the primer sites. +These cases will be very difficult to detect in wastewater samples, since they usually contain mixed lineages. +If a lineage is characterised by several mutations across different amplicons, this may not cause an issue, as _Freyja_ will still be able to calculate its frequency from the other mutations. +However, in some situations it may lead to a bias that we cannot easily control for. + +The figure below illustrates this issue, as well as the issue of low depth of sequencing discussed earlier. + +![Diagram illustrating the consequence of having low depth of sequencing, complete amplicon dropout or lineage-specific amplicon dropout. Yellow stars illustrate mutations and red boxes regions of the genome with no coverage.](images/ww_depth_and_coverage.png) + +In summary, although it is not always easy to decide whether to keep or remove a sample from analysis, evaluating the sequencing depth and genome coverage is a good indicator for which samples may be more or less accurate. +We may decide to keep them in our analysis, knowing that we need to interpret their results more carefully. +Finally, for surveillance purposes, you may want to discuss as a team which thresholds you want to use to exclude samples from reporting and consistently apply those across the teams performing the data analysis. + + + + + +## Exercises + +:::{.callout-exercise} +#### Run `viralrecon` for wastewater samples + +TODO - add exercise preamble + +1. Use the script `utilites/fastq_dir_to_samplesheet.py` to generate a samplesheet from the reads in `data/reads` directory. See the tip box in @sec-ww-viralrecon. +2. Open the _shell script_ called `scripts/01-run_viralrecon.sh`, which contains part of the command to run your samples through the viralrecon pipeline. Fix the script where the word "FIXME" appears: + - Use as input the samplesheet generated in the previous step. + - Output the results to the directory `results/viralrecon`. + +You will notice the `viralrecon` command includes direct path to the reference genome, gene annotation and primer file. +This is because these samples were processed with the SWIFT primers, which are proprietary. +Furthermore, we use the option `--ivar_trim_offset 5`, which is recommended in the [viralrecon documentation](https://nf-co.re/viralrecon/2.6.0/docs/usage#swift-primer-sets) for these primers. +Note that the primer BED we are using here is not the official file from the company, they are approximate coordinates used only for training purposes. + +:::{.callout-answer} + +TODO - finish the answer + +```bash +nextflow run nf-core/viralrecon \ + -r dev -profile singularity \ + --platform illumina \ + --input samplesheet.csv \ + --outdir results/viralrecon \ + --protocol amplicon \ + --fasta 'resources/reference/sarscov2_genome.fasta' \ + --gff 'resources/reference/sarscov2_annotation.gff' \ + --primer_bed 'resources/primers/swift_v2.bed' \ + --ivar_trim_offset 5 \ + --freyja_repeats 100 \ + --skip_assembly --skip_asciigenome \ + --skip_consensus +``` + +::: +::: + +:::{.callout-exercise} +#### Cleaning Freyja Output + +To make our downstream analysis easier, we provide a script that can be used to compile all the Freyja outputs into "tidy" CSV files. + +The script works by taking as input the directory with the `viralrecon` results, and it automatically reads all the _Freyja_ files to generate two CSV files: `lineage_abundances.csv` and `vocs_abundances.csv`. +These files contain, respectively, the abundances of individual lineages and the abundances aggregated by variants of concern (equivalent to the output from `freyja demix` detailed in @sec-demix). +Additionally, it contains the confidence intervals for each estimate calculated by `freyja boot`. +Finally, you can optionally provide a metadata CSV file, which will be joined with the abundances for easier downstream analysis and interpretation. + +Here is an example of the `vocs_abundance.csv` file from the script: + +``` +sample name abundance boot_lo boot_hi date country city latitude longitude +SRR18541074 Delta 0.9640087907444956 0.9596984293917038 0.9682214940819226 2021-12-01 United States San Diego 32.719875 -117.170082 +SRR18541074 Omicron 0.019682409750709555 0.013233836060878133 0.02276906228234253 2021-12-01 United States San Diego 32.719875 -117.170082 +SRR18541074 Other 0.0012202492036713667 0.0 0.008722550206162935 2021-12-01 United States San Diego 32.719875 -117.170082 +SRR18541043 Omicron 0.8762933605967606 0.8732252984148029 0.8812933627570921 2021-12-26 United States San Diego 32.719875 -117.170082 +``` + +The first 5 columns come from _Freyja_: + +- `sample` is the sample name +- `name` is the lineage or variant name +- `abundance` is the abundance estimate +- `boot_lo` is the lower bound of the bootstrap 95% confidence interval +- `boot_hi` is the higher bound of the confidence interval + +The remaining columns are part of our metadata, which in this case includes date of collection and location information. + +Your task for this exercise is to run the script on the preprocessed results in `preprocessed/viralrecon`. +To see how you should run the script, look at its help: + +```bash +python utilities/tidy_freyja_viralrecon.py --help +``` + +Use 70% as the minimum coverage to keep a sample in the output, and save the files in a directory called `results/tidyfreyja`. + +Once you run the script, open the resulting files in _Excel_ and answer the following questions: + +- Are there any lineages that are present at >75% frequency in any of the samples? +- How about variants of concern? +- If you sort the table by date, can you see a pattern in which variants of concern are present across time? + +:::{.callout-answer} + +We can use the following command to run the script (use use `\` to split the command across multiple lines for readability): + +```bash +python utilities/tidy_freyja_viralrecon.py \ + --viralrecon_results preprocessed/viralrecon/ \ + --outdir results/tidyfreyja \ + --metadata sample_info.csv \ + --mincov 70 +``` + +This generates two files in the folder `results/tidyfreyja`. + +We open both files in _Excel_ to answer the questions: + +- Are there any lineages that are present at >75% frequency in any of the samples? + - Only three samples have lineages with >75% frequency. Those lineages are AY.103, AY.100 and AY.116. +- How about variants of concern? + - There are several samples with variants of concern (VOCs) at >75% frequency. There are two VOCs: delta and omicron. +- If you sort the table by date, can you see a pattern in which variants of concern are present across time? + - There is a noticeable pattern of the Delta variant being replaced by Omicron around Dec 2021. +::: +::: + + +## Summary + +:::{.callout-tip} +#### Key Points + +- The main bioinformatic steps for lineage abundance estimation from sequencing data are: quality filtering of raw reads, mapping to the reference genome, trimming PCR primers, identifying mutations and their frequency and estimating lineage abundance. +- The development version of `viralrecon` can perform lineage abundance using the _Freyja_ software. + - For wastewater data, we usually skip the generation of a consensus genome, as it would not necessarily be reflective of the genomes present in the sample. +- _Freyja_ combines mutation frequencies and their sequencing depth with known lineage mutations, to infer their frequencies in the sample. + - Higher sequencing depth gives more accurate estimates. + - Low-frequency lineages are harder to detect and their frequency estimates less accurate. + - Very similar lineages may be artificially assigned the same frequency, if some of their distinctive mutations are missing from our data. +- Sequencing depth, genome coverage and amplicon dropout are important metrics to assess the quality of wastewater samples, as they may affect the accuracy of lineage abundance estimation. +- Lineage abundance estimation relies on previously known lineages, and therefore new lineages are not directly reported. +::: diff --git a/materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap-icons.css b/materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/materials/04-wastewater/02-ww_abundance_files/libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid 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var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.375rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #ffffff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.375rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 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var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #ffffff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #212529;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return 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b/materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/tippy.css b/materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/materials/04-wastewater/02-ww_abundance_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/materials/04-wastewater/03-ww_visualisation.qmd new file mode 100644 index 0000000..cf43e31 --- /dev/null +++ b/materials/04-wastewater/03-ww_visualisation.qmd @@ -0,0 +1,188 @@ +--- +pagetitle: "SARS Genomic Surveillance" +--- + +# Abundance Visualisation + +```{r setup, echo=False} +knitr::opts_chunk$set(echo = TRUE, root.dir = "../../course_files/r_demo/") +knitr::opts_knit$set(root.dir = "../../course_files/r_demo/") +``` + +:::{.callout-tip} +## Learning Objectives + +Under development + +::: + + +## Custom visualisation with R + +In the previous section we have generated a CSV file that aggregated all the lineage/variant abundances into a single table. +Although we have done some exploration of the data in _Excel_, we can perform more advanced and customised visualisations using the _R_ software (see the [R fundamentals](../appendices/quick_r.Rmd) appendix for a quick introduction to this software). + +We start by loading the `tidyverse` package, which contains several functions for data manipulation and visualisation: + +```{r} +library(tidyverse) +theme_set(theme_classic()) +``` + +We have also set a simpler theme for our `ggplot` plots, instead of the default greyed-background theme. + +The next step is to read our data in: + +```{r, eval=FALSE} +vocs <- read_csv("results/tidyfreyja/vocs_abundances.csv") +``` + +```{r, include=FALSE} +vocs <- read_csv("vocs_abundances.csv") +lineages <- read_csv("lineage_abundances.csv") +``` + +We can check the first few rows of our data, to check that is was imported correctly: + +```{r} +head(vocs) +``` + +We can also do some basic exploration of our data, answering some simple questions. +Some examples are given here. + +How many VOCs have >75% frequency? + +```{r} +vocs |> + # keep rows with >= 75% abundance + filter(abundance >= 0.75) |> + # sort them by date + arrange(date) +``` + +What was the count of each detected variant? + +```{r} +vocs |> + count(name) +``` + +We can also start doing some visualisations. +For example, the previous question can be better visualised with a barplot: + +```{r} +vocs |> + count(name) |> + ggplot(aes(x = n, y = name)) + + geom_col() +``` + +Even better, we can sort our variants by count rather than alphabetically: + +```{r} +vocs |> + count(name) |> + # reorder the names by their count + mutate(name = fct_reorder(name, n)) |> + ggplot(aes(x = n, y = name)) + + geom_col() +``` + +We may also want to break this down by city: + +```{r} +vocs |> + # count by city and name + count(city, name) |> + # reorder the names by their count + mutate(name = fct_reorder(name, n)) |> + ggplot(aes(x = n, y = name)) + + geom_col(aes(fill = city)) +``` + +It seems like Omicron was only detected in San Diego. +Could this be because of the dates when the samples were collected in each city? + +```{r} +vocs |> + # create a variable with the start of the week for each sample + mutate(week = floor_date(date, "week")) |> + # count how many samples per week and city + count(week, city) |> + # barplot + ggplot(aes(week, n)) + + geom_col() + + facet_grid(~ city) +``` + +Yes, it seems like San Diego has a wider coverage across time. +Let's create a new table for some downstream visualisations: + +- Retain samples from San Diego only. +- Add a new column with the start of the month that each sample was collected in. +- Add an ordering to the sample IDs by their date of colection. +- Add an ordering to the variants IDs by their average date of occurrence. + +```{r} +sandiego <- vocs |> + filter(city == "San Diego") |> + mutate(month = floor_date(date, "month"), + sample = fct_reorder(sample, date), + name = fct_reorder(name, date)) +``` + + +Using `ggplot`, we can create a barplot of VOCs over time: + +```{r} +sandiego |> + ggplot(aes(x = sample, y = abundance)) + + geom_col(aes(fill = name)) + + scale_x_discrete(guide = guide_axis(angle = 45)) +``` + +If you want, you can also visualise this as a heatmap-style plot: + +```{r} +sandiego |> + ggplot(aes(sample, name)) + + geom_tile(aes(fill = abundance)) + + scale_fill_viridis_c(limits = c(0, 1)) + + scale_x_discrete(guide = guide_axis(angle = 45)) + + facet_grid(~ month, scales = "free_x", space = "free_x") +``` + +We can clearly see the transition between Delta and Omicron. +Delta is clearly dominant in Sep and Nov, then samples start to appear more mixed in Dec, and finally replaced by Omicron by Jan. + + +```{r} +# distribution of lineage abundances +lineages |> + ggplot(aes(abundance)) + + geom_histogram(binwidth = 0.01) + +lineages |> + # order the samples by date + mutate(sample = fct_reorder(sample, date), + name = fct_reorder(name, date)) |> + ggplot(aes(sample, name)) + + geom_tile(aes(fill = abundance)) + + scale_fill_viridis_c(limits = c(0, 1)) + + theme_classic() + + scale_x_discrete(guide = guide_axis(angle = 45)) +``` + +## Freyja dashboard + +```bash +freyja dash +``` + +- Freyja requires a metadata file in a strange format - it might be better to simply use the tidy script and R + +Custom visualisation with R: + +- Note that abundances don't always add up to 1. This is a rounding issue from Freyja output. +- diff --git a/materials/04-wastewater/04-ww_mutations.md b/materials/04-wastewater/04-ww_mutations.md new file mode 100644 index 0000000..ed87763 --- /dev/null +++ b/materials/04-wastewater/04-ww_mutations.md @@ -0,0 +1,52 @@ +--- +pagetitle: "SARS Genomic Surveillance" +--- + +# Mutation Analysis + +:::{.callout-caution} + +Under development + +::: + + +## Mutation Frequency Visualisation + +TODO - using R to read the mutations long table and make some heatmap-style visualisations + + +## Mutation co-occurrence + +As mentioned, low-frequency lineages pose a challenge in the analysis of mixed samples, as it becomes difficult to distinguish their presence from technical background noise (e.g. due to sequencing errors). +An approach to help distinguish errors from the true presence of a rare lineage is to look for mutations that co-occur in the same read or read pair. +As it would be very unlikely that two (or more) errors would occur in exactly the same read pair, the presence of co-occurring mutations provide a high-confidence signal that the respective lineage is present in the sample. + +This approach requires that the lineages of interest have enough distinguishing mutations within close proximity to each other (i.e. in the same PCR amplicon). + +Another application of co-occurring mutations is to provide a stronger evidence for new emerging lineages. +If the same pattern of co-occurring mutations is observed across multiple samples and at increasing frequencies over time, it may be cause for concern and require close monitoring. +See, for example, the study of [Jahn et al 2022](https://doi.org/10.1038/s41564-022-01185-x), where they demonstrate that co-occurring mutations characteristic of the Alpha variant were detected in wastewater samples from two Swiss cities around 2 weeks before its first report from clinical samples. + +Can use some R code to visualise them (although a bit ugly due to parsing): + +```r +library(tidyverse) +theme_set(theme_classic()) + +covar <- read_tsv("results/covariants.tsv") %>% + janitor::clean_names() + +covar %>% + mutate(covarid = factor(1:n())) %>% + separate_longer_delim(covariants, " ") %>% + mutate(pos = str_remove(covariants, "^[C,A,T,G,\\(]") %>% str_remove("[C,A,T,G,\\)].*$") %>% as.numeric()) %>% + mutate(covariants = fct_reorder(covariants, pos)) %>% + group_by(covarid) %>% + filter(n() > 1) %>% + ungroup() %>% + ggplot(aes(covarid, covariants)) + + geom_tile(aes(fill = freq), colour = "grey") + + scale_fill_viridis_c(limits = c(0, 1)) +``` + diff --git a/materials/04-wastewater/eda_variants.r b/materials/04-wastewater/eda_variants.r new file mode 100644 index 0000000..67cf1b6 --- /dev/null +++ b/materials/04-wastewater/eda_variants.r @@ -0,0 +1,38 @@ +library(tidyverse) +theme_set(theme_classic()) + +vars <- read_csv("./preprocessed/viralrecon/variants//ivar/variants_long_table.csv") |> + janitor::clean_names() + +meta <- read_csv("sample_info.csv") + +vars <- vars |> + full_join(meta, by = "sample") |> + mutate(sample = fct_reorder(sample, date), + gene = fct_reorder(gene, pos)) + +p <- vars |> + filter(dp > 10 & effect == "missense_variant") |> + group_by(pos) |> + filter(any(af > 0.5) & n_distinct(sample) >= 3) |> + group_by(sample, date, pos, alt, gene) |> + summarise(freq = mean(af)) |> + ungroup() |> + ggplot(aes(factor(pos), sample)) + + geom_tile(aes(fill = freq)) + + facet_grid(~ gene, scale = "free_x", space = "free_x") + + scale_fill_continuous(limits = c(0, 1)) + + scale_x_discrete(guide = guide_axis(angle = 45)) + +p + +plotly::ggplotly(p) + + +# Tidy Freyja variants + +lineages <- read_csv("results/tidyfreyja/lineage_abundances.csv") + +lineages |> + group_by(sample, abundance) |> + filter(n() > 1) diff --git a/materials/04-wastewater/sampling_variation.r b/materials/04-wastewater/sampling_variation.r index 20e76ce..087539c 100644 --- a/materials/04-wastewater/sampling_variation.r +++ b/materials/04-wastewater/sampling_variation.r @@ -79,7 +79,7 @@ p4 <- example |> # uncertainty in the estimated frequencies # using Jeffreys interval estimation using the Beta -crossing(freq = c(0.01, 0.05, 0.1, 0.5, 0.9), depth = seq(10, 1000, 1)) |> +crossing(freq = c(0.01, 0.05, 0.1, 0.5, 0.9), depth = seq(10, 500, 1)) |> mutate(lo = qbeta(0.025, depth*freq+0.5, depth*(1-freq)+0.5), hi = qbeta(0.975, depth*freq+0.5, depth*(1-freq)+0.5)) |> ggplot(aes(depth, freq)) + @@ -87,36 +87,37 @@ crossing(freq = c(0.01, 0.05, 0.1, 0.5, 0.9), depth = seq(10, 1000, 1)) |> geom_ribbon(aes(ymin = lo, ymax = hi, fill = factor(freq)), alpha = 0.3) + theme_bw() + scale_fill_viridis_d() + scale_colour_viridis_d() + - scale_x_continuous(breaks = seq(0, 10000, 200)) + + scale_x_continuous(breaks = seq(0, 10000, 100)) + scale_y_continuous(breaks = seq(0, 1, 0.2)) + labs(x = "Sequencing depth", y = "Estimated frequency\n(95% confidence interval)", subtitle = "Decrease in uncertainty as sequencing depth increases", colour = "Starting\nfrequency", fill = "Starting\nfrequency") # uncertainty interval for a variant at 50% frequency -crossing(freq = 0.5, depth = seq(10, 2000, 1)) |> +crossing(freq = 0.5, depth = seq(10, 500, 1)) |> mutate(lo = qbeta(0.025, depth*freq+0.5, depth*(1-freq)+0.5), hi = qbeta(0.975, depth*freq+0.5, depth*(1-freq)+0.5)) |> #View() ggplot(aes(depth, hi - lo)) + geom_line(aes(group = factor(freq)), linewidth = 2) + theme_bw() + scale_colour_viridis_d() + - scale_x_continuous(breaks = seq(0, 10000, 200)) + + scale_x_continuous(breaks = seq(0, 10000, 100)) + + scale_y_continuous(limits = c(0, 0.6)) + labs(x = "Sequencing depth", y = "Uncertainty\n(95% interval range)", subtitle = "Non-linear decrease in uncertainty as sequencing depth increases") -# probability of detecting a variant with at least 5 reads +# probability of detecting a variant with at least 1 read # assuming different starting frequencies and total read depth -crossing(freq = c(0.01, 0.05, 0.1, 0.5, 0.9), depth = seq(10, 2000, 1)) |> - mutate(detect = 1 - pbinom(4, depth, freq)) |> +crossing(freq = c(0.01, 0.05, 0.1, 0.5, 0.9), depth = seq(10, 500, 1)) |> + mutate(detect = 1 - pbinom(0, depth, freq)) |> # filter(depth < 400) |> ggplot(aes(depth, detect)) + geom_line(aes(colour = factor(freq)), linewidth = 2) + theme_bw() + scale_colour_viridis_d() + - scale_x_continuous(breaks = seq(0, 2000, 200)) + + scale_x_continuous(breaks = seq(0, 2000, 100)) + scale_y_continuous(breaks = seq(0, 1, 0.2)) + - labs(x = "Sequencing depth", y = "Probability of detection\n(minimum 5 reads)", + labs(x = "Sequencing depth", y = "Probability of detection\n(minimum 1 read)", subtitle = "High sequencing depth is required to detect rare variants", colour = "Starting\nfrequency") @@ -186,3 +187,18 @@ sim |> position = "dodge") + geom_hline(yintercept = freqs, colour = "grey") + theme_classic() + + +# UShER + +muts <- read_csv("https://github.com/andersen-lab/Freyja/raw/main/freyja/data/usher_barcodes.csv") +colnames(muts)[1] <- "lineage" + +mut_mat <- as.matrix(muts[, -1]) +rownames(mut_mat) <- muts[[1]] + +dist(mut_mat[1:30, ]) + +# distinguishing mutations in some UK lineages at the moment +temp <- mut_mat[c("JN.1", "JN.1.1", "JN.2", "BA.2.86.1"), ] +temp[ , colSums(temp) != 0 & colSums(temp) != nrow(temp)] diff --git a/materials/04-wastewater/ww_notes.md b/materials/04-wastewater/ww_notes.md new file mode 100644 index 0000000..5502ea8 --- /dev/null +++ b/materials/04-wastewater/ww_notes.md @@ -0,0 +1,202 @@ +--- +pagetitle: "SARS Genomic Surveillance" +--- + +# Wastewater surveillance + +- Links + - https://doi.org/10.3201/eid2908.221634 + - https://en.ssi.dk/covid-19/national-surveillance-of-sars-cov-2-in-wastewater + - https://link.springer.com/article/10.1007/s10096-023-04590-0 + - https://wastewater-observatory.jrc.ec.europa.eu/ + + +- Cheaper than clinical testing +- Not everyone will come to a clinical setting to get tested, but everybody goes to the toilet +- A single sample captures many people +- Several pathogens can be found (potentially) +- Includes marginalised or isolated populations +- High population coverage +- Possible to compare results across geographical regions +- Cost effective, fast and flexible +- Supplement to other surveillance systems + - Wastewater surveils everyone in a population - but no distinction between those that have symptoms or not + - Clinical captures only those that cause disease symptoms (or severe hospitalisations) +- Gives distribution of variants circulating in population + - these variants might be different from the variants circulating in clinical cases (causing symptoms) +- Where to sample and how often? + - Depends on the goals + - E.g. to surveil local outbreaks need to do sampling in more locations (local wastewater treatment plants); for regional surveillance only a few key treatment plants in the country; for national surveillance even fewer. + - Need to consider the population density, e.g. how many people live in each catchment area of a wastewater plant. +- Challenges + - results cannot be translated to exact number of infected individuals + - not as intuitive to interpret + - sources of variation such as shedding, dilution, degradation + - sensitivity can be low +- In Denmark + - They have samplers setup next to the plant + - Usually take proportional samples, i.e. wastewater is collected in proportion to the inflow to the treatment plant (more water is sampled when there is more flow). + - Samples collected in two 24h periods per week. + - In the lab the main objective is to estimate concentration of SARS-CoV-2 in the sample + - Sample is purified and RNA extracted, then used for RT-qPCR +- The qPCR results are converted to RNA-copies equivalent. The trend is plotted over time. + - For reporting they classify the trend over 3 weeks into categories "strong decrease", "decrease", "stable", "increase", "strong increase" --> to facilitate communication with stakeholders +- Wastewater surveillance is a good indication of the underlying incidence of SARS-CoV-2 in the population + - They had very high testing rates at some point, which allowed them to compare the wastewater to the clinical + + +**Sample preparation in the lab** + +- Purification of samples +- Wastewater samples are highly variable +- Season and catchment area can also affect physiochemical properties of the samples + - Dilution (rainy season), salt, pH, organic matter, retention time in sewage pipes +- No one method is optimal for every sample +- Viral concentration is very low + - viral concentration is the most important step in wastewater sample prep +- Common methods of concentration: + - precipitation + - polyethylene glycol precipitation - shields molecules and viruses from water and allows precipitating them + - pros: cheap, no special equipment + - cons: long protocol, hard to reproduce + - ultracentrifugation + - uses a filter that catches larger molecules + - pros: simple protocol and easy to implement + - cons: filters can clog, organic material is also retained + - direct capture / lysis + - lyse the particles such as viruses, and then get RNA/DNA material using either a silica column membrane (to capture nucleic acids) or magnetic beads to bind DNA/RNA. + - Pros: captures also already lysed particles; it uses modified standard labs + - Cons: chemical properties of the sample can affect recovery; co-precipitation of PCR inhibitors + - adsorption-elution + - binding viral particles to a solid-phase - e.g. a membrane, beads or mesh + - pros: can be scaled well + - cons: more expensive and specialised equipment may be required +- Different concentration techniques are used in the field of wastewater surveillance - there is no "best" method really +- After concentration most extraction protocols can be used. The basic steps are: + - Lysis (detergent, protease) + - Bind RNA to either beads of membrane (column-based) + - Wash + - Elute +- Removal of PCR inhibitors is key + - This can be done using either a dilution series and seeing if the drop in cycles is as predicted + - or you can add a known standard to the sample and compare the Ct value for it compared to the SARS-CoV-2 to see if there is inhibition (something like that, I think?) +- Limit centrifugation before processing, because the virus binds to organic matter, so will loose some virus in the process +- Saturation of purification may lead to underestimation of viral concentration +- When setting up a surveillance lab, it's a good idea to test different protocols to pick the one that is most effective + - make sure to test across different treatment plants and different times of year - to account for variation + - just taking a method from a paper may not be the best approach for your setting +- Can also spike the samples with a known virus to assess the quality of the extraction. + - they don't do it because they have automated and optimised protocols quite a lot, so didn't find it necessary + + +**Analysis, QC and automation** + +- Fluorescent probe-based real-time PCR +- A probe with a fluorescent report is added to the mix +- The polymerase also has an exonuclease activity, which cleaves the reporter from the probe, releasing it from the quencher and thus releasing signal +- This method can be multiplex (using different fluorofores) +- Ct value is estimated + - A standard curve is used to convert the Ct values to RNA concentration (using dilution series from panels with known concentration) +- Multiple genes used for their quantification - multiplex 3-4 PCR targets +- Sampling (e.g. pipetting) will affect the final quantification + - see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5496743/ +- Technical replicates are highly recommended in this case, because of the limit of detection +- How can we convert viral concentration to the number of people that it came from? + - There are ways to normalise for this, but one method to do it is using molecular markers that you add to the reaction + - Nucleic acid markers for population: + - PMMoV - only humans consume pepper + - CrAssphage - common in the human gut + - bacteroids - enterobacteria + - RNase P or mitochondrial DNA - something that is excreted by humans in high quantity + - Note that using these markers are used for normalisation between samples, but you cannot back-calculate how many people contributed to the sample. So, it accounts for things like dilution due to rain, or variations in population density over time (e.g. in coastal areas, where density increases in holiday). +- Quality control + - having a standard is important - having samples with known numbers of viral copies is essential to build standard curves + - use stable reference samples - e.g. a circular plasmid that is stable; using a reference sample when building standard curves will allow seeing if something is odd, e.g. RNAse degradation that shifts the curve down (even though at first sight it might look like a nice straight line) +- Automation + - consistency between samples over time + - use robot liquid handler to do most work + - lots of investment up front, but saves time, avoids sample mixup, long-term consistency + + +**Processing of RT-qPCR data** + +- The data we start with is: + - date, location, sample ID, target gene, technical replicate name (they always do 3 techs), Cq or Ct value, RNA copies per well + - The RNA copies per well is calculated from standard curves - this is the value they use in analysis +- Analysis + - Take average of the 3 technical replicates + - Transform copies_per_well to copies_per_l: + - `copies_per_l = copies_per_well * 2000` + - log10-transform data + - normalise results - they have two SARS markers, N1 and N2, and the normaliser PMMoV. Their normalisation formula is: `log10_sars_faeces = (log10(N1) + log10(N2))/2 - log10(PMMoV)` + - basically the average of the two markers (which they call `log10_sars`) minus the PMMoV. Because we are on log-scale these are effectively a LFC + - For interpretation, they then back-transform the number: `10^log10_sars_faeces * 10^7`; the 10^7 is just to bring the number to a sensible scale +- Other variables are recorded such as flow into the plant, precipitation, temperature +- Population data is also used by pairing number of inhabitants within each catchment area +- The population data can be used to then calculate a weighted average + +**Normalisation** + +- Variation in the composition of wastewater needs to be considered when normalising data +- Several factors: + - number of people using the sewage system + - e.g. in Copenhagen 2022 they had tour de France, which massively increased the number of tourists in the region + - rain, precipitation, groundwater all influence outcome + - the amount of industry around the catchment area also has big influence +- Normalising both by the amount of flow that comes into the treatment plant and the population in the area, different treatment plants can be compared +- For flow normalisation we need flow measurements from the wastewater treatment plant. + - only accounts for dilution, but not for number of shredders + - flow often not available, however no additional analysis is required in the lab +- For faecal normalisation we need an extra PCR reaction in the lab (e.g. for the PPMoV, CrAssphage, etc.) + - only this normalisation takes into account variations in the population density + - this normalisation indirectly also accounts for dilution effects +- A good faecal indicator needs to be something that is consistent in the shedding, and that only comes from humans. +- Can do just faecal normalisation, although they usually calculate both normalised values to compare the two estimates. + +**Wastewater sequencing** + +- Why sequence wastewater? + - Depends on the pathogen + - e.g. monitoring poliovirus can assess whether we have vaccine-derived or wild-type poliovirus circulating + - for SARS-CoV-2 we can get idea of circulating variants, emerging variants +- Which technology? There's different pros and cons + - Illumina: more accurate, short reads + - ONT: less accurate, long reads + - Ion Torrent: accuracy in-between, short reads + - But also portability and yield also things to consider +- Denmark: + - ONT sequencing of 1049bp amplicon of the S gene + - Map to references of S genes of different variants + - However this was limiting as it didn't allow distinguishing fine-resolution distinction of variants + - Later setup Illumina WGS +- Sample prep: + - cDNA synthesis using random hexamer primers + - then two pools A and B, to use ARTIC protocol +- Wastewater samples is tricky + - fragmented genomes + - low concentration + - PCR inhibitors + - Mix of different variants + - Low mapping rates not uncommon +- Freyja demix output + - coverage should be greater than 50% (that seems too low to me...) +- Lineage abundance estimates can sometimes be limited by the depth of coverage + - If you have low coverage, freyja may assign an equal estimate to several lineages that share those variants. + - If we increase min depth used by freyja, then we are increasing our resolution for abundance estimate, but we may include less of the genome. So, it's a trade-off. + - Can also set a filter for minimum lineage abundance to be reported, but then we need to be aware that we've set that threshold, so we will not get some potentially rare variants reported. +- Challenges + - Level of resolution + - we want to capture the diversity, but not exagerate it + - How do we normalise across sites? + - E.g. if we have 10% abundance in one site and 70% in another site, what is the average? + - Catchment areas will have different numbers of inhabitants + - Number of mapped reads does not reflect that variation + - Should we take viral load of the sample or number of mapped reads in the sample into account? + - Sometimes there are discrepancies between what is detected with Freyja and what is seen with clinical samples, in particular for low-frequency lineages + - Deeper sequencing is needed to catch those less abundant lineages + + +Trainers: + +- Amanda Qvesel (bioinformatics) - amqv@ssi.dk +- \ No newline at end of file diff --git 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b/utils/wastewater/tidy_freyja_viralrecon.py index c37f9be..3ec618e 100644 --- a/utils/wastewater/tidy_freyja_viralrecon.py +++ b/utils/wastewater/tidy_freyja_viralrecon.py @@ -64,7 +64,7 @@ def parse_arguments(): parser.add_argument("--viralrecon_results", required=True, help="Path to the directory with the viralrecon results. The script will automatically read the files from their relevant sub-directories.") - parser.add_argument("--outdir", required=True, help="Output directory name, which will be created if it does not exist. Two files will be saved, named 'lineage_abundances.csv' and 'vocs_summaries.csv'") + parser.add_argument("--outdir", required=True, help="Output directory name, which will be created if it does not exist. Two files will be saved, named 'lineage_abundances.csv' and 'vocs_abundances.csv'") parser.add_argument("--prefix", default="", help="Optional prefix for output file names.")

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + reads.fq.gz + + Adapter Trimming + + fastp + + Read Mapping + + bowtie2 + + samtools + + Trim Primers + + ivar trim + + Call Variants + + ivar variants + + Lineage abundance + + freyja variants + + + Reference Files + + reference.fasta + + + + + + + + primers.bed + + + + + + Input Files + + software + + Workflow Step + + Output Files + + + Remove Human + + kraken2 + Variant Report + variants_long_table.csv + + Quality Report + + multiqc + + multiqc_report.html + + + + + + + + Annotate Variants + + snpsift + + snpeff + + + + + + + freyja update + + freyja demix + + + + freyja boot + + + + diff --git a/materials/images/workflow_ww_overview.svg b/materials/images/workflow_ww_overview.svg index 53b1983..650ecb4 100644 --- a/materials/images/workflow_ww_overview.svg +++ b/materials/images/workflow_ww_overview.svg @@ -5,7 +5,7 @@ viewBox="0 0 199.69296 313.87647" height="313.87646mm" 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