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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 transmembrane domains with tmhmm and pureseqTM

Protein transmembrane domains are the parts of a protein that pass through the lipid bilayer of a cell membrane. These domains are typically composed of hydrophobic amino acids that allow the protein to interact with the nonpolar interior of the membrane. Transmembrane domains play an important role in many cellular processes, including cell signaling, transporting molecules, and cell adhesion. One important application of bioinformatics is identifying protein transmembrane domains from amino acid sequences. Several methods are used to identify transmembrane domains bioinformatically, including hydrophobicity analysis, in which we identify regions of a protein sequence that have a high degree of hydrophobicity and are likely to be transmembrane domains. There are also hidden Markov models that are trained to identify transmembrane domains based on a set of known transmembrane proteins. We can also use machine learning algorithms...

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