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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
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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

Extracting information in genomic regions of interest

Often, you’ll want to look in more detail at data that falls into a particular genomic region of interest, whether that be the SNPs and variants in a gene or the genes at a particular locus. This common task is handled extremely well by the powerful GRanges and SummarizedExperiment objects. They are a little fiddly to set up but have very flexible subsetting operations that make the effort well worth it. In this recipe, we’ll look at a few ways to set up these objects and a few ways we can manipulate them to get interesting information.

Getting ready

For this recipe, we’ll need the GenomicRanges, SummarizedExperiment, and rtracklayer Bioconductor packages. We will also use a GFF file of features of the Arabidopsis chromosome 4 and a smaller text version of gene features only. These are both in the rbioinfcookbook package, so we’ll extract them from that.

How to do it…

Extracting information...

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