Topic outline

  • General

    Collapse all Expand all

  • Feedback

  • Course Description

    This workshop will focus on the core steps involved in calling variants with the Broad’s Genome Analysis Toolkit, using the “Best Practices” developed by the GATK team. You will learn why each step is essential to the variant discovery process, what are the operations performed on the data at each step, and how to use the GATK tools to get the most accurate and reliable results out of your dataset. In the course of this workshop, we highlight key functionalities such as the GVCF workflow for joint variant discovery in cohorts, RNAseq-specific processing, and somatic variant discovery using MuTect2. We also preview capabilities of the upcoming GATK version 4, including a new workflow for CNV discovery.

    The lecture-based component of the workshop is aimed at a mixed audience of life scientists who are new to the topic of variant discovery or to GATK, seeking an introductory course into the tools, or who are already GATK users seeking to improve their understanding of and proficiency with the tools. The hands-on component is aimed at novice to intermediate users who are seeking detailed guidance with GATK and related tools.

    During the lecture day, we explain the rationale, theory and application of our Best Practices for Variant Discovery in high-throughput sequencing data. In the practical tutorial session, we walk attendees through hands-on exercises that teach how to manipulate the standard data formats involved in variant discovery and how to apply GATK tools appropriately to various use cases and data types. In the course of these exercises, we demonstrate useful tips and tricks for interacting with GATK, dealing with problems, and using third-party tools such as Samtools, IGV and RStudio.

    Please note that this workshop is focused on human data analysis. The majority of the materials presented does apply equally to non-human data, and we will address some questions regarding adaptations that are needed for analysis of non-human data, but we will not go into much detail on those points.