R Bioinformatics Cookbook - Second Edition: Utilize R packages for bioinformatics, genomics, data science, and machine learning (Paperback or Softback)

Discover over 80 recipes for modeling and handling real-life biological data using modern libraries from the R ecosystem

Key Features

• Apply modern R packages to process biological data using real-world examples
• Represent biological data with advanced visualizations and workflows suitable for research and publications
• Solve real-world bioinformatics problems such as transcriptomics, genomics, and phylogenetics
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Set up a working environment for bioinformatics analysis with R
• Import, clean, and organize bioinformatics data using tidyr
• Create publication-quality plots, reports, and presentations using ggplot2 and Quarto
• Analyze RNA-seq, ChIP-seq, genomics, and next-generation genetics with Bioconductor
• Search for genes and proteins by performing phylogenetics and gene annotation
• Apply ML techniques to bioinformatics data using mlr3
• Streamline programmatic work using iterators and functional tools in the base R and purrr packages
• Use ChatGPT to create, annotate, and debug code and workflows

Who this book is for

This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning via a recipe-based approach. Working knowledge of the R programming language and basic knowledge of bioinformatics are prerequisites.

Table of Contents

1. Setting Up Your R Bioinformatics Working Environment
2. Loading, Tidying, and Cleaning Data in the tidyverse
3. ggplot2 and Extensions for Publication Quality Plots
4. Using Quarto to Make Data-Rich Reports, Presentations, and Websites
5. Easily Performing Statistical Tests Using Linear Models
6. Performing Quantitative RNA-seq
7. Finding Genetic Variants with HTS Data
8. Searching Gene and Protein Sequences for Domains and Motifs
9. Phylogenetic Analysis and Visualization
10. Analyzing Gene Annotations
11. Machine Learning with mlr3
12. Functional Programming in puRRR and base R
13. Turbo-Charging Development in R with ChatGPT

Professor Dan MacLean has a PhD in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now an honorary professor at the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics, specializing in R and Bioconductor, and has developed analytical workflows in bioinformatics, genomics, genetics, image analysis, and proteomics at the Sainsbury Laboratory since 2006. Dan has developed and published software packages in R, Ruby, and Python, with over 100,000 downloads combined.