Book Image

R Bioinformatics Cookbook

By : Dan MacLean
Book Image

R Bioinformatics Cookbook

By: Dan MacLean

Overview of this book

Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Table of Contents (13 chapters)

Technical requirements

The sample data you'll need is available from this book's GitHub repository: https://github.com/danmaclean/R_Bioinformatics_Cookbook. If you want to use the code examples as they are written, then you will need to make sure that this data is in a sub-directory of whatever your working directory is.

The following are the R packages that you'll need. Most of these will install with install.packages(); others are a little more complicated:

  • ape
  • Bioconductor:
    • Biostrings
    • biomaRt
    • DECIPHER
    • EnsDb.Rnorvegicus.v79
    • kebabs
    • msa
    • org.At.tair.db
    • org.Eck12.db
    • org.Hs.eg.db
    • PFAM.db
    • universalmotif
  • bio3d
  • dplyr
  • e1071
  • seqinr

Bioconductor is huge and has its own installation manager. You can install it with the following code:

if (!requireNamespace("BiocManager"))
    install.packages("BiocManager")
BiocManager::install()
Further information...