Informatics on High-throughput Sequencing Data (2017)

Course Objectives

A poster announcing this workshop can be found here

With the introduction of high-throughput sequencing platforms, it is becoming feasible to consider sequencing approaches to address many research projects. However, knowing how to manage and interpret the large volume of sequence data resulting from such technologies is less clear. The CBW has developed a popular 2-day course covering the bioinformatics tools available for managing and interpreting high-throughput sequencing data, where the focus is on Illumina reads although the information is applicable to all sequencer reads.

Beginning with an understanding of the workflow involved to move from platform images to sequence generation, participants will gain practical experience and skills to be able to:

  • Assess sequence quality
  • Map sequence data onto a reference genome (required)
  • Perform de novo assembly tasks
  • Quantify sequence data
  • Integrate biological context with sequence information

Target Audience

This workshop is intended for graduate students, post-doctoral fellows, clinical fellows and investigators involved in analyzing data from HT sequencing platforms.

Prerequisite: UNIX familiarity is required. Familiarity can be gained through online activities. You should be familiar with these UNIX concepts (tutorial 1-3).

You will also require your own laptop computer. Minimum requirements: 1024x768 screen resolution, 1.5GHz CPU, 1GB RAM, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements). If you do not have access to your own computer, you may loan one from the CBW. Please contact course_info@bioinformatics.ca for more information.

Course Outline

Day 1

Module 1 - Introduction to High-Throughput Sequencing (2017) (Instructor: Jared Simpson)

  • Overview of high-throughput sequencing technologies: major players and their strengths and weaknesses

Module 2 - Data Visualization (2017) (Instructor: Florence Cavalli)

  • Data file formats used in genome visualization (FASTA, BED, WIG, GFF, etc)
  • Introduction to genomic data visualization tools and how they can be used to visualize sequencing read data: UCSC, IGV, Savant, GBrowse
  • Integrating other data sets into a browser

Lab Practical: Variant detection and visualization within the genome using IGV

Module 3 - Genome Alignment (2017) (Instructor: Mathieu Bourgey)

  • What is involved in mapping reads to a reference genome
  • What are the FASTQ and SAM/BAM file formats
  • Some common terminology used to describe alignments

Lab Practical:

  • Connecting to the Cloud
  • Genome alignment exercise

Integrated Assignment
Consolidate the skills you learned by performing an alignment.

Day 2

Module 6 - De Novo Assembly (2017) (Instructor: Jared Simpson)

  • Fundamentals of de novo assembly
  • Data structures used by assemblers (de Bruijn graphs and overlap graphs)
  • Common steps that assemblers perform
  • Overview of commonly used software

Lab Practical: Perform a de novo assembly task.

Module 4 - Small-Variant Calling and Annotation (2017) (Instructor: Mathieu Bourgey)

  • SNPs, SNVs, and short-INDELs and why to look for them
  • BQ recalibration, duplicate removal, aligner choice
  • Detecting variants and factors taken into account by the SNP callers
  • Different types of SNP calling: haploid/diploid, trio, somatic mutations, pooled
  • Determining which SNPS are good from the millions detected
  • INDEL cleaning
  • Standard file formats for SNPs
  • Introduction to SNP calling tools and how they compare with each other

Lab Practical: SNP detection exercise

Module 5 - Structural Variant Calling (2017) (Instructor: Mathieu Bourgey)

  • Structural variants (SVs), different types, mechanisms that give rise to SVs, and how SVs and CNVs differ
  • Differences between human and model organism genomes
  • Detecting SVs via sequencing (read pair, read depth, combined approach, local de novo assembly) and which SV types are detectable by which strategies
  • Introduction to SV detection tools
  • File formats used to describe SVs

Lab Practical:

  • SV discovery in a single human genome
  • Brief intro to SV visualization and interpretation

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