R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning, 2nd Edition

Free Download R Bioinformatics Cookbook
by Dan MacLean
English | 2023 | ISBN: 1837634270 | 396 pages | True PDF EPUB | 24.47 MB

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, ggDescription2, 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 Descriptions, reports, and presentations using ggDescription2 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
Setting Up Your R Bioinformatics Working Environment
Loading, Tidying, and Cleaning Data in the tidyverse
ggDescription2 and Extensions for Publication Quality Descriptions
Using Quarto to Make Data-Rich Reports, Presentations, and Websites
Easily Performing Statistical Tests Using Linear Models
Performing Quantitative RNA-seq
Finding Genetic Variants with HTS Data
Searching Gene and Protein Sequences for Domains and Motifs
Phylogenetic Analysis and Visualization
Analyzing Gene Annotations
Machine Learning with mlr3
Functional Programming in puRRR and base R
Turbo-Charging Development in R with ChatGPT

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