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 multiple alignments of proteins or genes

Aligning sequences as a task before building phylogenetic trees or as an end in itself to determine conserved and divergent regions is a mainstay in bioinformatics analysis and is amply covered in R with ape and in Bioconductor with the msa and DECIPHER packages. We’ll look at the extremely straightforward procedures for going from sequence to alignment in this recipe.

There are different techniques for different sequence length categories. In the first part of this recipe, we’ll look at sequences on the order of a couple of thousand residues or smaller, such as those that represent genes and proteins.

Getting ready

For this recipe, you’ll need the msa package. This is a pretty hefty package and includes external software: Clustal, Clustal Omega, and Muscle. The ape and seqinR packages are also needed. As a test dataset, we’ll use some hemoglobin protein sequences stored in the rbioinfcookbook...