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)

Installing and managing different versions of Bioconductor packages in environments

Bioconductor is an open source project that provides a collection of R packages for the analysis and comprehension of genomic data. It is focused on the needs of the bioinformatics and computational biology communities. The packages in Bioconductor cover a wide range of topics, including data representation and management, preprocessing and normalization, statistical analysis, and visualization of high-throughput genomic data. Bioconductor exists as a project distinct from R because it addresses the specific needs of the bioinformatics community, and requires different data structures, analysis methods, and visualization techniques to other fields. A user might choose Bioconductor tools because they are specifically designed for the analysis of genomic data and are often more specialized than general-purpose R packages. Bioconductor usually comes with its own installer, but there are other ways to work...