lecture07

Lecture 7: Introduction to Sequencing Data Analysis

Now that we have a basic grasp of concepts surrounding data management, manipulation, and visualization, we're ready to start focusing on some of the more specialized data encountered in computational biology research. Sequencing of nucleic acids is almost ubiquitous in biological research. In this lecture, we will introduce some common resources for depositing and retrieving sequence data generated by consortium efforts and independent laboratories. We will introduce concepts and practical steps of querying, inspecting, and visualizing sequence data. Then, we will cover the types of genomic variation and common tools used to predict these from sequencing data.

This lecture focuses on concepts surrounding genome sequence data and their associated workflows. Examples of code provided in this lecture include Unix command line tools. These are included for your future reference and will not be included in the homework for this section. We'll cover the Unix command line later in the semester, at which point you may find it useful to refer back to some of the material presented here.

Learning objectives

• Identify common databases and file formats used for sequence data
• Describe the steps involved in processing and analyzing sequence data to predict different types of genomic variants
• Recognize common tools (databases and software) used to assess variation in genomic data

Class materials

Outline of content from the slides:

1. Sequence data

• Databases and online resources for sequence data
• Learn the common sequence data file formats

2. Tools for sequencing data

• Tools to query, inspect, visualize an aligned sequence file
• Learn the contents of sequence data files
• Learn to generate sequencing metrics and to process sequence data
• Learn about Python and R libraries/packages to read sequence data

3. Genome variant analysis

• Types of genomic variation
• Tools to predict genomic variations
• Learn the common file formats for variation data
• Databases and online resources for human variation data

For your reference, data and examples shown in this lecture are available here, and visualization uses the Integrative Genomics Viewer (IGV). It is not necessary to download these for this lecture, although we'll use them in the next class.

Reminders

• Homework 2 (data manipulation and visualization in R/tidyverse) is currently available and is due Tuesday, October 22 at noon. You should have received an email containing an invitation to create your repository using GitHub Classroom. Contact Kate (khertwec at fredhutch.org) with any questions or concerns.
• The next class session (lecture 8) will include analysis of genomic data in R. To prepare for this session, please download all data files in this Dropbox folder and follow the instructions in this script to install required packages. You may also find it useful to install the Integrative Genomics Viewer (IGV) for visualization of genomic data.