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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

Retrieving and working with SNPs

SNPs and other polymorphisms are important genomic features and we of the want to retrieve know SNPs in particular genomic regions. In this recipe, we will look at doing that in two different BioMarts that hold different types of data. In the first part, we’ll use gramene to look a retrieving plant SNPs. In the second part, we’ll look at how to find information on human SNPs in the main Ensembl database.

Getting ready

As before, we’ll need only the biomaRt package from Bioconductor and a working internet connection.

How to do it…

Retrieving and working with SNPs can be done using the following steps:

  1. Get the list of datasets, attributes, and filters from gramene:
    library(biomaRt)listMarts(host = "https://ensembl.gramene.org")gramene_connection <- useMart(biomart = "ENSEMBL_MART_PLANT_SNP",                ...
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