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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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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

Analyzing Gene Annotations

Large-scale model organism sequencing projects, such as the Human Genome Project or the 1001 plant genomes sequencing projects, have made a huge amount of genomics data publicly available. Likewise, open-access data sharing by individual laboratories has made the raw sequencing data of genomes and transcriptomes widely available too. Working with this data programmatically can mean having to parse or bring locally some seriously large or complicated files. Much effort has gone into making these resources as accessible as possible through APIs and other queryable interfaces, such as BioMart. In this chapter, we will look at some recipes that will allow us to search annotations without having to download whole genome files and find relevant information across databases. We’ll look at how to analyze those annotations for biologically meaningful patterns in gene or protein sets derived from things such as differential expression and proteomics analysis...

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