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)

Estimating differential expression with Kallisto and Sleuth

Kallisto is an RNA-Seq read aligner that uses a pseudoalignment algorithm, which allows it to map reads to a reference transcriptome without using traditional alignment methods such as Smith-Waterman or Needleman-Wunsch. Instead, it uses a k-mer index of the reference transcriptome to quickly and accurately quantify expression levels of transcripts. This allows Kallisto to run much faster than traditional aligners, making it a popular choice for large-scale RNA-Seq experiments.

The companion R package called Sleuth is a tool for analyzing the output from Kallisto. It allows users to perform differential expression analysis and identify transcripts that are differentially expressed between different samples or conditions. Sleuth uses a Bayesian framework to model the expression levels of transcripts and take into account technical variability in the data, such as sequencing depth and batch effects. The package also provides...