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 regions showing high expression ab initio using bumphunter

The bumphunter package in R’s Bioconductor ecosystem is a tool for identifying genomic regions that exhibit bumps of enrichment in high-throughput sequencing data. These bumps may represent functional regions, such as enhancers or transcription factor binding sites, and the package can be used to identify both known and novel regions of interest.

The bumphunter package works by scanning a given genomic region to enrich a particular feature of interest, such as the presence of certain transcription factor binding sites, or the level of histone modifications. It does this by dividing the region into non-overlapping windows and comparing the mean signal within each window to the overall mean signal across the entire region. The package then employs a statistical model to determine whether any particular window is significantly enriched for the feature of interest.

bumphunter can be used to identify novel enhancer...