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)

3D structure protein alignment in bio3d

Three-dimensional structural alignments between two molecular models can reveal structural properties that are common or unique to either of the proteins. These can suggest evolutionary or functional commonalities. In this recipe, we’ll look at how to get the alignment of two protein sequences in three dimensions and view them in 3D.

Getting ready

For this recipe, we need to install a sequence aligner. We’ll take advantage of the fact that the msa package comes with a version of MUSCLE and install that. You can also use conda to install it separately.

We will also need to install a viewer. We will use PyMOL, which can be installed into a conda environment as follows:

conda install -c conda-forge -c schrodinger pymol-bundle

We’ll also use the bio3d package and some data files in the rbioinfcookbook package.

How to do it…

We can predict structure by performing the following steps:

  1. Load the...