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)

Analyzing Gene Annotations

Large-scale model organism sequencing projects, such as the Human Genome Project or the 1001 plant genomes sequencing projects, have made a huge amount of genomics data publicly available. Likewise, open-access data sharing by individual laboratories has made the raw sequencing data of genomes and transcriptomes widely available too. Working with this data programmatically can mean having to parse or bring locally some seriously large or complicated files. Much effort has gone into making these resources as accessible as possible through APIs and other queryable interfaces, such as BioMart. In this chapter, we will look at some recipes that will allow us to search annotations without having to download whole genome files and find relevant information across databases. We’ll look at how to analyze those annotations for biologically meaningful patterns in gene or protein sets derived from things such as differential expression and proteomics analysis...