R Bioinformatics Cookbook

This is the code repository for R Bioinformatics Cookbook, published by Packt.

Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

What is this book about?

In R Bioinformatics Cookbook, you encounter common and not-so-common challenges in the bioinformatics domain and solve them using real-world examples. The book guides you through varied bioinformatics analysis, from raw data to clean results. It shows you how to import, explore and evaluate your data and how to report it.

This book covers the following exciting features:

• Employ Bioconductor to determine differential expressions in RNAseq data
• Run SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and Indels
• Use ggplot to create and annotate a range of visualizations
• Query external databases with Ensembl to find functional genomics information
• Execute large-scale multiple sequence alignment with DECIPHER to perform comparative genomics
• Use d3.js and Plotly to create dynamic and interactive web graphics
• Use k-nearest neighbors, support vector machines and random forests to find groups and classify data

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

if (!requireNamespace("BiocManager"))
    install.packages("BiocManager")
BiocManager::install() 

Following is what you need for this book: This book is for data scientists, bioinformatics analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the R programming language and some basic understanding of bioinformatics are mandatory.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

Software and Hardware List

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Errata

• Page 11 (Paragraph 1, line 4): eset_dge <- edgeR::estimateDisp(eset_dge, design) should be eset_dge <- edgeR::estimateDisp(count_dge, design)

Related products

• R Statistics Cookbook [Packt] [Amazon]

• Data Analysis with R - Second Edition [Packt] [Amazon]

Get to Know the Author

Professor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now a Honorary Professor in the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics, specializing in R and Bioconductor and developing analytical workflows in bioinformatics, genomics, genetics, image analysis, and proteomics at The Sainsbury Laboratory since 2006. Dan has developed and published software packages in R, Ruby, and Python with over 100,000 downloads combined.

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