R Bioinformatics Cookbook

By : MacLean, Dr Dan Maclean

2.7 (3)

Buy this Book

R Bioinformatics Cookbook

2.7 (3)

By: MacLean, Dr Dan Maclean

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Free Chapter

Performing Quantitative RNAseq

Technical requirements

Estimating differential expression with edgeR

Estimating differential expression with DESeq2

Power analysis with powsimR

Finding unannotated transcribed regions

Finding regions showing high expression ab initio with bumphunter

Differential peak analysis

Estimating batch effects using SVA

Finding allele-specific expressions with AllelicImbalance

Plotting and presenting RNAseq data

Finding Genetic Variants with HTS Data

Technical requirements

Finding SNPs and indels from sequence data using VariantTools

Predicting open reading frames in long reference sequences

Plotting features on genetic maps with karyoploteR

Selecting and classifying variants with VariantAnnotation

Extracting information in genomic regions of interest

Finding phenotype and genotype associations with GWAS

Estimating the copy number at a locus of interest

Searching Genes and Proteins for Domains and Motifs

Technical requirements

Finding DNA motifs with universalmotif

Finding protein domains with PFAM and bio3d

Finding InterPro domains

Performing multiple alignments of genes or proteins

Aligning genomic length sequences with DECIPHER

Machine learning for novel feature detection in proteins

3D structure protein alignment with bio3d

Phylogenetic Analysis and Visualization

Technical requirements

Reading and writing varied tree formats with ape and treeio

Visualizing trees of many genes quickly with ggtree

Quantifying differences between trees with treespace

Extracting and working with subtrees using ape

Creating dot plots for alignment visualization

Reconstructing trees from alignments using phangorn

Metagenomics

Technical requirements

Loading in hierarchical taxonomic data using phyloseq

Rarefying counts and correcting for sample differences using metacoder

Reading amplicon data from raw reads with dada2

Visualizing taxonomic abundances with heat trees in metacoder

Computing sample diversity with vegan

Splitting sequence files into OTUs

Proteomics from Spectrum to Annotation

Technical requirements

Representing raw MS data visually

Viewing proteomics data in a genome browser

Visualizing distributions of peptide hit counts to find thresholds

Converting MS formats to move data between tools

Matching spectra to peptides for verification with protViz

Applying quality control filters to spectra

Identifying genomic loci that match peptides

Producing Publication and Web-Ready Visualizations

Technical requirements

Visualizing multiple distributions with ridgeplots

Creating colormaps for two-variable data

Representing relational data as networks

Creating interactive web graphics with plotly

Constructing three-dimensional plots with plotly

Constructing circular genome plots of polyomic data

Working with Databases and Remote Data Sources

Technical requirements

Retrieving gene and genome annotation from BioMart

Retrieving and working with SNPs

Getting gene ontology information

Finding experiments and reads from SRA/ENA

Performing quality control and filtering on high-throughput sequence reads

Completing read-to-reference alignment with external programs

Visualizing the quality control of read-to-reference alignments

Useful Statistical and Machine Learning Methods

Technical requirements

Correcting p-values to account for multiple hypotheses

Generating a simulated dataset to represent a background

Learning groupings within data and classifying with kNN

Predicting classes with random forests

Predicting classes with SVM

Learning groups in data without prior information

Identifying the most important variables in data with random forests

Identifying the most important variables in data with PCA

Programming with Tidyverse and Bioconductor

Technical requirements

Making base R objects tidy

Using nested dataframes

Writing functions for use in dplyr::mutate()

Working programmatically with Bioconductor classes

Developing reusable workflows and reports

Making use of the apply family of functions

Building Objects and Packages for Code Reuse

Technical requirements

Creating simple S3 objects to simplify code

Taking advantage of generic object functions with S3 classes

Creating structured and formal objects with the S4 system

Simple ways to package code for sharing and reuse

Using devtools to host code from GitHub

Building a unit test suite to ensure that functions work as you intend

Using continuous integration with Travis to keep code tested and up to date

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Taking advantage of generic object functions with S3 classes

Once we have an S3 object, we will need to create functions to work with it. These are really what makes working with the objects easy in the long run. It is in these functions that we can abstract away the processing of the data in the objects and reduce the work we are doing each time we use them. R's object system is based on generic functions. These are grouped functions with the same base name, but a class-specific name extension. Each group is called a method and R will decide which of the particular functions belonging to a method will be called based on the class of the object the method is called on. This means we can call plot() on objects of class A and get a completely different sort of figure than if we called it on an object of class B. In this recipe, we'll have a look at how that works.

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