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)

Differential peak analysis

Identifying differentially expressed peaks in genomics data is a key task in bioinformatics and has many uses. One of the most common applications is the analysis of ChIP-Seq data, where the technique is used to identify binding sites of transcription factors and other DNA-binding proteins. By comparing ChIP-Seq data between different samples or conditions, researchers can identify peaks of enrichment that are differentially expressed and gain insight into how the protein in question regulates the expression of different genes. Another example is RNA-Seq data – by comparing RNA-Seq data between different samples or conditions, researchers can identify peaks of expression that are differentially expressed and gain insight into how different samples or conditions affect the expression of different genes.

Other use cases include Histone modification and ATAC-Seq data analysis to study the regulation of gene expression through chromatin accessibility...