Book Image

R Bioinformatics Cookbook

By : Dan MacLean
Book Image

R Bioinformatics Cookbook

By: Dan MacLean

Overview of this book

Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Table of Contents (13 chapters)

Finding Genetic Variants with HTS Data

High-Throughput Sequencing (HTS) has made it possible to discover genetic variants and carry out genome-wide genotyping and haplotyping in many samples in a short space of time. The deluge of data that this technology has released has created some unique opportunities for bioinformaticians and computer scientists, and some really innovative new data storage and data analysis pipelines have been created. The fundamental pipeline in variant calling starts with the quality control of HTS reads and the alignment of those reads to a reference genome. These steps invariably take place before analysis in R and typically result in a BAM file of read alignments or a VCF file of variant positions (see the Appendix of this book for a brief discussion of these file formats) that we'll want to process in our R code.

As variant calling and analysis...