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)

Preface

In biology, genetics and genomics data is the driver of discovery. To harness the power of data, bioinformaticians rely on computational tools and none is more powerful in the statistical and visualization world than R. R Bioinformatics Cookbook, Second Edition, is designed to help you take control of all manner of bioinformatics analyses and to help you navigate the intricate world of bioinformatics with R.

The R Bioinformatics Cookbook, Second Edition book, is a resource for getting good work done quickly. It will help researchers to sharpen their skills and broaden their knowledge of important packages such as the tidyverse for data management, ggplot for visualization, and mlr for machine learning and delves deeply into the comprehensive Bioconductor framework of tools tailored specifically toward the most taxing and common analyses in bioinformatics.