Bioinformatics Analysis of Whole Exome Sequencing Data

Peter J Ulintz; Weisheng Wu; Chris M Gates

doi:10.1007/978-1-4939-8876-1_21

Bioinformatics Analysis of Whole Exome Sequencing Data

Methods Mol Biol. 2019:1881:277-318. doi: 10.1007/978-1-4939-8876-1_21.

Authors

Peter J Ulintz^{1

2}, Weisheng Wu³, Chris M Gates³

Affiliations

¹ BRCF Bioinformatics Core, University of Michigan, Ann Arbor, MI, USA. [email protected].
² Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. [email protected].
³ BRCF Bioinformatics Core, University of Michigan, Ann Arbor, MI, USA.

PMID: 30350213
DOI: 10.1007/978-1-4939-8876-1_21

Abstract

This chapter contains a step-by-step protocol for identifying somatic SNPs and small Indels from next-generation sequencing data of tumor samples and matching normal samples. The workflow presented here is largely based on the Broad Institute's "Best Practices" guidelines and makes use of their Genome Analysis Toolkit (GATK) platform. Variants are annotated with population allele frequencies and curated resources such as GnomAD and ClinVar and curated effect predictions from dbNSFP using VCFtools, SnpEff, and SnpSift.

Keywords: Cancer research; Clinical genomics; Exome sequencing; Genome sequencing; Next-generation sequencing; Somatic variant detection; Variant annotation.

MeSH terms

Computational Biology / methods
Exome Sequencing / methods*
Genetic Variation
Genome, Human
High-Throughput Nucleotide Sequencing / methods*
Humans
Molecular Sequence Annotation
Neoplasms / genetics
Sequence Analysis, DNA / methods*
Software