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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

Searching Gene and Protein Sequences for Domains and Motifs

The sequences of genes, proteins, and entire genomes hold clues to their function. Repeated subsequences or sequences with strong similarities to each other can be clues to things such as evolutionary conservation or functional relatedness. Sequence analysis for motifs and domains is a core technique in bioinformatics. Bioconductor contains many useful packages for analyzing genes, proteins, and genomes. In this chapter, you will learn how to use Bioconductor to analyze sequences for features of functional interest, such as de novo DNA motifs and known domains from widely used databases. You’ll learn about some packages for kernel-based machine learning to find protein sequence features. You will also learn about some large-scale alignment techniques for many sequences or very long sequences. You will use Bioconductor and other statistical learning packages.

In this chapter, we will cover the following recipes:

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