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)

Predicting open reading frames in long reference sequences

A draft genome assembly of a previously unsequenced genome can be a rich source of biological knowledge, but when genomics resources such as gene annotations aren’t available, it can be tricky to proceed. In this recipe, we’ll look at a first-stage pipeline for finding potential genes and genomic loci of interest absolutely de novo and without information beyond the sequence. We’ll use a very simple set of rules to find open reading frames (ORFs) – sequences that begin with a start codon and end with a stop codon. The tools for doing this are encapsulated within a single function in the systemPipeR Bioconductor package. We’ll end up with yet another GRanges object that we can integrate into processes downstream that allow us to cross-reference other data, such as RNA-Seq. As a final step, we’ll look at how we can use a genome simulation to assess which of the open reading frames are...