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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
Languages
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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

Estimating differential expression with Kallisto and Sleuth

Kallisto is an RNA-Seq read aligner that uses a pseudoalignment algorithm, which allows it to map reads to a reference transcriptome without using traditional alignment methods such as Smith-Waterman or Needleman-Wunsch. Instead, it uses a k-mer index of the reference transcriptome to quickly and accurately quantify expression levels of transcripts. This allows Kallisto to run much faster than traditional aligners, making it a popular choice for large-scale RNA-Seq experiments.

The companion R package called Sleuth is a tool for analyzing the output from Kallisto. It allows users to perform differential expression analysis and identify transcripts that are differentially expressed between different samples or conditions. Sleuth uses a Bayesian framework to model the expression levels of transcripts and take into account technical variability in the data, such as sequencing depth and batch effects. The package also provides...

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