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)

Reading and writing varied tree formats with ape and treeio

Phylogenetic analysis is a cornerstone of biology and bioinformatics. The programs are diverse and complex, the computations are long-running, and the datasets are often large. Many programs are standalone and many have proprietary input and output formats. This has created a very complex ecosystem that we must navigate when dealing with phylogenetic data, meaning that often the simplest strategy is to use combinations of tools to load, convert, and save the results of analyses in order to be able to use them in different packages. In this recipe, we’ll look at dealing with phylogenetic tree data in R. To date, R support for the wide range of tree formats is restricted, but a few key packages have sufficient standardized objects such that workflows can focus on a few types and conversion to those types is streamlined. We’ll look at using the ape and treeio packages to get tree data into and out of R.

Getting...