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 SNPs and INDELs from sequence data using VariantTools

A key bioinformatics task is to take an alignment of high-throughput sequence reads, typically stored in a BAM file, and compute a list of variant positions. Of course, this is ably handled by many external command-line programs and tools and usually results in a VCF file of variants, but some really powerful packages in Bioconductor can do the whole thing, quickly and efficiently, by taking advantage of BiocParallel’s facilities for parallel evaluation, a set of tools designed to speed up work with large datasets in Bioconductor objects. Using Bioconductor tools allows us to keep all of our processing steps within R, and in this recipe, we’ll go through a whole pipeline – from reads to lists of genes carrying variants – using purely R code and several Bioconductor packages.