Book Image

R Bioinformatics Cookbook

By : Dan MacLean
Book Image

R Bioinformatics Cookbook

By: Dan MacLean

Overview of this book

Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Table of Contents (13 chapters)

Working with Databases and Remote Data Sources

Large-scale model organism sequencing projects, such as the Human Genome Project (HGP), or the 1,001 plant genomes sequencing projects have made a huge amount of genomics data publicly available. Likewise, open access data sharing by individual laboratories has made the raw sequencing data of genomes and transcriptomes widely available, too. Working with this data programmatically can mean having to parse or bring locally some seriously large or complicated files. As such, much effort has gone into making these resources as accessible as possible through APIs and other queryable interfaces, such as BioMart. In this chapter, we'll look at some recipes that will allow us to search for annotations without having to download whole genome files and find relevant information across databases. We'll look at how to pull raw reads...