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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

Finding unannotated transcribed regions

Finding unannotated transcribed regions can be useful in several different genomics applications. One of the main use cases is identifying novel genes. Unannotated transcribed regions may represent novel genes that have not been previously identified.

By identifying these regions, researchers can gain a better understanding of the genome and potentially discover new biological pathways or proteins. Another use case is identifying alternative splicing events, where different exons are used to create different protein products. Identifying these events can provide insight into how the genome is regulated and how different proteins are produced from the same gene. Additionally, unannotated transcribed regions may include untranslated RNAs (UTRs), lncRNAs, miRNAs, and other types of non-coding RNAs, which can provide a more complete understanding of the functional elements in the genome and characterize the non-coding genome. Lastly, unannotated...

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