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)

Estimating the copy number at a locus of interest

We will often want to know how often a sequence occurs in a sample of interest – that is, to estimate whether in a given sample, a locus has been duplicated or its copy number has increased. The locus could be anything from a gene at a Kbp scale or a large section of DNA at a Mbp scale. Our approach in this recipe will be to use HTS read coverage after alignment to estimate a background level of coverage and then compare it to the coverage in a region of interest. The ratio will give us an estimate of the copy number of our region of interest. The recipe here is the first step. The background model we’ll use is very simple – we’ll only calculate a global mean, but we’ll discuss some alternatives later. This recipe does not cover ploidy – the number of genomes present in the whole cell. It is possible to estimate ploidy from similar data but it is a more involved pipeline. Take a look at the...