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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

Selecting and classifying variants with VariantAnnotation

In variant calling pipelines, we’ll often want to do subsequent analysis steps that need further filtering or classification based on the features of the individual variants, such as the depth of coverage in the alternative allele. This is best done from a VCF file, and a common protocol is to save a VCF of all variants found and experiment with filtering that. In this recipe, we’ll look at taking an input VCF and filtering it to retain variants in which the alternative allele is the major allele in the sample.

Getting ready

We’ll need a tabix index VCF file; one is provided in the rbioinfcookbook package. To extract it, we’ll use the fs package. For analysis, we shall use the VariantAnnotation Bioconductor package.

How to do it…

Selecting and classifying variants with VariantAnnotation can be done as follows:

  1. Create a prefilter function:
    library(VariantAnnotation)is_not_microsat...
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