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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

Installing and managing different versions of Bioconductor packages in environments

Bioconductor is an open source project that provides a collection of R packages for the analysis and comprehension of genomic data. It is focused on the needs of the bioinformatics and computational biology communities. The packages in Bioconductor cover a wide range of topics, including data representation and management, preprocessing and normalization, statistical analysis, and visualization of high-throughput genomic data. Bioconductor exists as a project distinct from R because it addresses the specific needs of the bioinformatics community, and requires different data structures, analysis methods, and visualization techniques to other fields. A user might choose Bioconductor tools because they are specifically designed for the analysis of genomic data and are often more specialized than general-purpose R packages. Bioconductor usually comes with its own installer, but there are other ways to work...

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