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)

Novel feature detection in proteins

Sometimes, we’ll have a list of protein sequences that have come from some analysis or experiment that are in some way biologically related. We might wish to determine the parts of those proteins that are responsible for the action. Domain and motif finding, as we’ve done in the preceding recipes, can only be helpful if we’ve seen the domains before or the sequence is well conserved or statistically over-represented. A different approach is to try machine learning, in which we build a model that can classify our proteins accurately and use the properties of that mode to show us which parts of the proteins result in the classification. We’ll take that approach in this recipe by training and analyzing a support vector machine (SVM).

Getting ready

For this recipe, we’ll need the kebabs and Biostrings Bioconductor packages, as well as the e1071 and readr packages. We’ll also need two input data files that...