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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Length 396 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse FREE CHAPTER 3. Chapter 3: ggplot2 and Extensions for Publication Quality Plots 4. Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites 5. Chapter 5: Easily Performing Statistical Tests Using Linear Models 6. Chapter 6: Performing Quantitative RNA-seq 7. Chapter 7: Finding Genetic Variants with HTS Data 8. Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs 9. Chapter 9: Phylogenetic Analysis and Visualization 10. Chapter 10: Analyzing Gene Annotations 11. Chapter 11: Machine Learning with mlr3 12. Chapter 12: Functional Programming with purrr and base R 13. Chapter 13: Turbo-Charging Development in R with ChatGPT 14. Index 15. Other Books You May Enjoy

Finding SNPs and INDELs from sequence data using VariantTools

A key bioinformatics task is to take an alignment of high-throughput sequence reads, typically stored in a BAM file, and compute a list of variant positions. Of course, this is ably handled by many external command-line programs and tools and usually results in a VCF file of variants, but some really powerful packages in Bioconductor can do the whole thing, quickly and efficiently, by taking advantage of BiocParallel’s facilities for parallel evaluation, a set of tools designed to speed up work with large datasets in Bioconductor objects. Using Bioconductor tools allows us to keep all of our processing steps within R, and in this recipe, we’ll go through a whole pipeline – from reads to lists of genes carrying variants – using purely R code and several Bioconductor packages.

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