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)

Quantifying and estimating the differences between trees with treespace

Comparing trees to differentiate or group them can help researchers to see patterns of evolution. Multiple trees of a single gene tracked across species or strains can reveal differences in how that gene is changing across species. At the core of these approaches are metrics of distances between trees. In this recipe, we’ll calculate one such metric to find pairwise differences between 20 different genes in 15 different species, hence 15 different tips with identical names in each tree. Such similarity in trees is needed to compare and get distances, and we can’t do an analysis like this unless these conditions are met.

Getting ready

For this recipe, we’ll use the treespace package to compute distances and clusters. We’ll use ape and adegraphics for accessory loading and visualization functions. The input data will be 20 files of Newick format trees, each of which represents a...