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)

Getting ready

In this recipe, we’ll use a set of synthetic reads on the first 83 KB or so of the human genome chromosome 17. The reads were generated using the wgsim tool in samtools, an external command-line program. They have 64 single nucleotide polymorphisms (SNPs) introduced by wgsim, which can be seen in the snp_positions DataFrame that comes in rbioinfcookbook. We’ll use BAM and reference genome files that are stored in that package too, so we’ll need to install that along with the GenomicRanges, gmapR, rtracklayer, VariantAnnotation, and VariantTools Bioconductor packages, as well as the fs CRAN package.

How to do it…

Finding SNPs and insertions/deletions (INDELs) from sequence data using VariantTools can be done by performing the following steps:

  1. Import the required libraries:
    library(GenomicRanges)library(gmapR)library(rtracklayer)library(VariantAnnotation)library(VariantTools)
  2. Then, load the datasets:
    bam_file <- fs::path_package...