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)

Performing Quantitative RNA-seq

RNA-Seq has revolutionized the study of gene expression by providing highly accurate estimates of transcript abundances through high-sensitivity detection and high-throughput analysis. Bioinformatic analysis pipelines that use RNA-Seq data typically start with a read quality control step, followed by either alignment to a reference or assembling sequence reads into longer transcripts afresh. After that, transcript abundances are estimated with sequence read counting and statistical models, and differential expression between samples is assessed. There are many technologies available for all steps of this pipeline. Quality control and read alignment will usually take place outside of R, so analysis in R will begin with a file containing transcript or gene annotations (such as GFF and BED files) and a file of aligned reads (such as BAM files).

The tools in R for performing analysis are powerful and flexible. Many of them are part of the Bioconductor...