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.

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Spis treści

R Bioinformatics Cookbook. Utilize R packages for bioinformatics, genomics, data science, and machine learning - Second Edition eBook -- spis treści

• 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