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)

Reconstructing trees from alignments using phangorn

So far in this chapter, we’ve assumed that trees are already available and ready to use. Of course, there are many ways to make a phylogenetic tree and, in this recipe, we’ll take a look at some of the different methods available.

Getting ready

For this chapter, we’ll use the abc.fa file of yeast ABC transporter sequences, the Bioconductor Biostrings package, and the CRAN msa and phangorn packages.

How to do it…

Constructing trees using phangorn can be done like this:

  1. Load in the libraries and sequences and make an alignment:
    library(Biostrings)library(msa)library(phangorn)seqfile <- fs::path_package(  "extdata",  "abc.fa",  package="rbioinfcookbook")seqs <- readAAStringSet(seqfile)aln <- msa::msa(seqs, method=c("ClustalOmega"))
  2. Convert the alignment:
    aln <- as.phyDat(aln, type = "AA")
  3. Make...