Computational Exome and Genome Analysis

Peter N. Robinson, Rosario Michael Piro, Marten Jager

• Publisher: Chapman and Hall/CRC
• Year: 2017
• ISBN:
  9781351650816
• Edition: 1
• Topic: Informatique
• Subtopic: Programmation de jeux

Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.

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Contents

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Preface

Contributors

PART I Introduction

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CHAPTER 1 ▪ Introduction: Whole Exome and Genome Sequencing

CHAPTER 2 ▪ NGS Technology

CHAPTER 3 ▪ Illumina Technology

CHAPTER 4 ▪ Obtaining WES/WGS Data for This Book

PART II Raw Data Processing

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CHAPTER 5 ▪ FASTQ Format

CHAPTER 6 ▪ Raw Data: Quality Control

CHAPTER 7 ▪ Trimming

PART III Alignment

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CHAPTER 8 ▪ Alignment: Mapping Reads to the Reference Genome

CHAPTER 9 ▪ SAM/BAM Format

CHAPTER 10 ▪ Postprocessing the Alignment

CHAPTER 11 ▪ Alignment Data: Quality Control

PART IV Variant Calling

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CHAPTER 12 ▪ Variant Calling and Quality-Based Filtering

CHAPTER 13 ▪ Variant Call Format (VCF)

CHAPTER 14 ▪ Jannovar

CHAPTER 15 ▪ Variant Annotation

CHAPTER 16 ▪ Variant Calling: Quality Control

CHAPTER 17 ▪ Integrative genomics viewer (IGV): Visualizing alignments and variants

CHAPTER 18 ▪ De Novo Variants

CHAPTER 19 ▪ Structural Variation

PART V Variant Filtering

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CHAPTER 20 ▪ Pedigree and Linkage Analysis

CHAPTER 21 ▪ Intersection analysis and rare variant association studies

CHAPTER 22 ▪ Variant frequency analysis

CHAPTER 23 ▪ Variant pathogenicity prediction

PART VI Prioritization

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CHAPTER 24 ▪ Variant prioritization

CHAPTER 25 ▪ Prioritization by random walk analysis

CHAPTER 26 ▪ Phenotype analysis

CHAPTER 27 ▪ Exomiser and Genomiser

CHAPTER 28 ▪ Medical Interpretation

PART VII Cancer

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CHAPTER 29 ▪ A (Very) Short Introduction to Cancer

CHAPTER 30 ▪ Somatic Variants in Cancer

CHAPTER 31 ▪ Tumor evolution and sample purity

CHAPTER 32 ▪ Driver mutations and mutational signatures

Appendix A ▪ Hints and Answers

References

Index

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Preface

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The knowledge that we convey in this book draws from complementary sources and experiences. The Robinson Lab at the Institute for Medical Genetics and Human Genetics of the Charit′e University Hospital in Berlin, Germany, established computational pipelines for the analysis of whole-exome sequencing (WES) and (later) whole-genome sequencing (WGS) data starting in the early days of WES. Rosario Piro spent important years of his scientific career at the German Cancer Research Center (DKFZ), Heidelberg, which has a longstanding record of achievements in cancer genomics.

The Robinson lab published one of the first exome-based gene discovery papers in Nature Genetics in 2010, the identification of disease- causing mutations in PIGV by identity-by-descent filtering of exome sequence data [226]. We subsequently contributed to the characterization as disease genes of PIGO [224], PGAP2 [225], IL21R [219], TTC8 [140], and PGAP3 [169]. We have additionally used WES analysis to address questions in hereditary cardiomyopathy [377], Vici syndrome [112], and other diseases [215].

The lab has developed the Human Phenotype Ontology [146, 209, 372] (HPO), which is being used by many of the ma jor WES/WGS translational research pro jects, such as Genomics England’s 100,000 Genomes Project, the Wellcome Trust Sanger Institute’s DECIPHER and Deciphering Developmental Disorders (DDD) pro jects, the National Institutes of Health (NIH) Undiagnosed Diseases Network, and many others [214]. We have developed software that exploits HPO- based phenotypic similarity algorithms to prioritize genes and variants in WES and WGS analysis [373, 399, 403], as we shall examine in several chapters of this book. We have additionally developed the annotation tool Jannovar [184], and contributed to a range of other algorithms designed for the analysis of quality control of WES/WGS data [161, 162], as well as algorithms for ChIP-seq [159], ChIP-nexus [158], NGS-based T-cell receptor profiling [230], and RNA- seq [182].

Rosario Piro’s research at the DKFZ contributed to the remarkable finding that all secretory meningioma harbor the same single nucleotide change in the pluripotency-related transcription factor KLF4 [363], a finding that was independently and simultaneously confirmed by another research group [80]. A fusion gene (NAB2-STAT6) was identified in meningeal hemangiopericytoma and solitary fibrous tumors that allows these tumors to be distinguished from anaplastic meningiomas by simple STAT6 immunohistochemistry [391], having an immediate impact on cancer diagnostics [70, 391]. The integration of DNA sequencing and epigenomic data led to the finding that poor-prognosis hindbrain ependymomas harbor an extremely low mutation rate and are instead characterized by a CpG island methylator phenotype (CIMP), leading to a transcriptional silencing of differentiation genes [271].

Marten J¨ager established and implemented the exome and genome pipelines in the Robinson lab at the Institute for Medical Genetics and Human Genetics of the Charit′e University Hospital. His research has concentrated on methods for exome and genome bioinformatics including approaches to VCF annotation and variant calling with non-linear genome assemblies, a method for composite transcriptome assembly of RNA-seq data, and contributions to many other research projects.

All three authors held courses on exome analysis in the Bioinformatics Department of the Free University of Berlin, out of which this book grew.

This book, therefore, reflects the experiences made over multiple years in establishing first an Illumina Genome Analyzer (GAIIX) and later a HiSeq 1500 and other devices in Berlin as well as working with HiSeq 2000 and MiSeq sequencing data in Heidelberg. It reflects the experiences of developing software for phenotype analysis that is being used internationally by groups involved in translational genomics. A major rule both in Heidelberg and Berlin was “eyeball the data”, and we place an emphasis in this book on understanding how data is represented in the various file formats common in WES/WGS analysis, and how to recognize errors and artifacts (or indeed, how to recognize high- quality data). With several exceptions, this book does not attempt to explain in detail the algorithms or statistics involved in WES/WGS analysis, but instead presents a practical guide to the many steps of WES/WGS analysis with intuitive algorithmic explanations and pointers to the literature.

A NOTE ON HOW TO USE THIS BOOK

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