Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
Table of Contents (16 chapters)

Searching Gene and Protein Sequences for Domains and Motifs

The sequences of genes, proteins, and entire genomes hold clues to their function. Repeated subsequences or sequences with strong similarities to each other can be clues to things such as evolutionary conservation or functional relatedness. Sequence analysis for motifs and domains is a core technique in bioinformatics. Bioconductor contains many useful packages for analyzing genes, proteins, and genomes. In this chapter, you will learn how to use Bioconductor to analyze sequences for features of functional interest, such as de novo DNA motifs and known domains from widely used databases. You’ll learn about some packages for kernel-based machine learning to find protein sequence features. You will also learn about some large-scale alignment techniques for many sequences or very long sequences. You will use Bioconductor and other statistical learning packages.

In this chapter, we will cover the following recipes:

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