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)

Getting Gene Ontology information for functional analysis from appropriate databases

The Gene Ontology (GO) is a very useful restricted vocabulary of annotation terms for genes and gene products that describe the biological process, molecular function, or cellular component of an annotated entity. As such, the terms are helpful as data in gene-set enrichment analysis and other functional -omic approaches. In this recipe, we’ll look at how we can prepare a list of gene IDs in a genomic region and get the GO IDs and descriptions for them.

Getting ready

We will just need the biomaRt package from Bioconductor and an internet connection.

How to do it…

Getting GO information can be done using the following steps:

  1. Make connections to Ensembl BioMart and find attributes:
    library(biomaRt)ensembl_connection <- useMart(biomart = "ENSEMBL_MART_ENSEMBL")listDatasets(ensembl_connection)data_set_connection <- useMart("hsapiens_gene_ensembl&quot...