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)

Finding protein domains with PFAM and bio3d

Discovering the function of a protein sequence is a key task. We can do this in many ways, including by conducting whole sequence similarity searches against databases of known proteins using tools such as BLAST. If we want more informative and granular information, we can instead look for individual functional domains within a sequence. Databases such as PFAM and tools such as hmmer make this possible. PFAM encodes protein domains as Hidden Markov Models, which hmmer uses to scan sequences and report any likely occurrences of the domains. Often, genome annotation projects will carry out the searches for us, meaning that finding the PFAM domains in our sequence is a question of searching a database. Bioconductor does a great job of packaging up the data in these databases in particular packages, usually with names ending in .db. In this recipe, we’ll look at how to work out whether a package contains PFAM domain information, how to...