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)

Proteomics from Spectrum to Annotation

Mass spectrometry (MS) data usually comprises spectra that must be bioinformatically processed to identify candidate peptides. These peptides include assignments, and counts can then be analyzed using a wide range of techniques and packages. The wide range of graphical user interface-driven tools for proteomics means that there is a proliferation of file formats that can be tough to deal with initially. These recipes will explore how to take advantage of the excellent parsers and reformatters available in the new RforProteomics project and associated tools for analysis and verification of spectra, and even show you how to view your peptides in genome browsers alongside other genomic information such as gene models.

In this chapter, we will cover the following recipes:

  • Representing raw MS data visually
  • Viewing proteomics data in a genome...