Computational Exome And Genome Analysis
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Computational Exome and Genome Analysis
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.
Computational Exome and Genome Analysis
Author: Peter Nicholas Robinson
language: en
Publisher: Chapman & Hall/CRC Mathematical and Computational Biology
Release Date: 2017
Cover -- Half Title -- Series Editor -- Published Titles -- Title -- Copyright -- Dedication -- Contents -- Who is this book for? -- Preface -- Contributors -- Part I Introduction -- Chapter 1 Introduction: Whole Exome and Genome Sequencing -- Chapter 2 NGS Technology -- Chapter 3 Illumina Technology -- Chapter 4 Data -- Part II Raw Data Processing -- Chapter 5 FASTQ Format -- Chapter 6 Raw Data: Quality Control -- Chapter 7 Trimming -- Part III Alignment -- 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 -- 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 -- 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 -- 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 -- 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
Computational Genomics with R
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.