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)

Analyzing splice variants with SGSeq

Alternative splicing is a process by which different variants of a gene are produced from a single primary transcript. This process allows you to generate multiple different proteins from a single gene, increasing the functional diversity of the genome. Alternative splicing can be regulated by different mechanisms, including cis-acting elements in the primary transcript and trans-acting factors that bind to these elements.

Analyzing alternative splicing in genomics can be beneficial in several ways, including allowing you to understand the genetic basis of disease. Many diseases are caused by mutations in genes that lead to changes in protein function. Alternative splicing can create different variants of a protein with different functions, and understanding how these variants are regulated can provide insights into disease mechanisms. The SGSeq R Bioconductor package can be used to help us analyze alternatively spliced transcripts. This package...