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)

To get the most out of this book

The book assumes that you have a basic working knowledge of R in the console and that you have a background in biology or life sciences. The book won’t try and teach you any biology; instead, we concentrate on applications of R within science. You will learn lots of new things about R that will enhance your bioinformatics research.

Software/hardware covered in the book

Operating system requirements

R 4.2 or greater

Windows, macOS, or Linux

Python 3.8 or greater

Windows, macOS, or Linux

Many individual R packages and some external tools are needed. All these will be discussed in the relevant chapters and recipes so make sure to read the Getting ready… sections.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

The sample data for this book is provided in an R package that you can install from within R. Look out for advice on how to do that in the relevant chapters and recipes. The code won’t work without the sample data, so look out for that.