Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
Table of Contents (16 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 aligning 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 variant call file (VCF) of variant positions that we’ll want to process in our R code.

As variant calling and analysis is such a fundamental technique in bioinformatics, Bioconductor is well-equipped with the tools we need to construct our software and perform our analysis. The key questions...