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)

Finding phenotype and genotype associations with GWAS

A powerful application of a variant calling many thousands of SNPs with high-throughput sequencing is genome-wide association studies (GWAS) of genotype and phenotype. GWAS is a genomic analysis of variants in different individuals or genetic lines to see whether any particular variant is associated with a trait. There are numerous techniques for doing this, but all of them rely on gathering data on variants in particular samples and working out each sample’s genotype before cross-referencing with the phenotype in some way. In this recipe, we’ll look at the sophisticated mixed linear model described by Yu et al. in 2006 (Nature Genetics, 38:203-208 ). Describing the workings of the unified mixed linear model is beyond the scope of this recipe, but it is a suitable model for use in data with large samples and broad allelic diversity and can be used on plant and animal data.

Getting ready

In this recipe, we’...