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)

Finding allele-specific expression with AllelicImbalance

Allele-specific expression (ASE) refers to the differential expression of alleles (different versions of a gene) in a diploid organism. It occurs when one allele is expressed more or less than the other allele, resulting in an imbalance of expression. ASE can occur in both coding and non-coding regions of the genome and can have a significant impact on the phenotype of an organism. Analyzing ASE can help with identifying genetic variations that contribute to the development of diseases such as cancer and inherited disorders.

The R Bioconductor AllelicImbalance package can be used to analyze ASE data. This package provides a set of tools for identifying, quantifying, and visualizing ASE in high-throughput sequencing data. The package can be used to quantify ASE at both the gene and transcript level and to identify differentially expressed alleles. Additionally, AllelicImbalance can help identify the genomic regions that are...