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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

Using AnnoDB packages for genome annotation

Bioconductor AnnoDB packages provide a set of software tools and resources for the annotation and functional analysis of genomic data, including DNA sequences, microarray data, and high throughput sequencing data. These packages are designed to help researchers in the field of genomics to better understand the biological significance of their data by providing them with access to a comprehensive database of biological annotations and functional annotations, such as GO pathway analysis, and functional enrichment analysis.

The Bioconductor AnnoDB packages are designed to work with a wide range of genomic data types from various organisms and are regularly updated to include the latest annotations and functional information. Some of the key features of these packages include tools for mapping genomic data to annotated genomic features, tools for retrieving functional annotations for specific genomic regions, and tools for performing various...

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