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R Bioinformatics Cookbook
R Bioinformatics Cookbook

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

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R Bioinformatics Cookbook

Finding Genetic Variants with HTS Data

High-Throughput Sequencing (HTS) has made it possible to discover genetic variants and carry out genome-wide genotyping and haplotyping in many samples in a short space of time. The deluge of data that this technology has released has created some unique opportunities for bioinformaticians and computer scientists, and some really innovative new data storage and data analysis pipelines have been created. The fundamental pipeline in variant calling starts with the quality control of HTS reads and the alignment of those reads to a reference genome. These steps invariably take place before analysis in R and typically result in a BAM file of read alignments or a VCF file of variant positions (see the Appendix of this book for a brief discussion of these file formats) that we'll want to process in our R code.

As variant calling and analysis...

Technical requirements

Here are the R packages you'll need. Some will install with install.packages(). The packages listed under Bioconductor need to be installed with the dedicated installer. That's described here. If you need to do anything further, installation will be described in the recipes in which the packages are used:

  • Bioconductor: Following are the packages:
    • Biostrings
    • GenomicRanges
    • gmapR
    • karyoploteR
    • rtracklayer
    • systemPipeR
    • SummarizedExperiment
    • VariantAnnotation
    • VariantTools
  • rrBLUP

Bioconductor is huge and has its own installation manager. You can install these packages with the following code (further information is available at https://www.bioconductor.org/install/):

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

Normally, in R, a user will load a library and use the functions directly...

Finding SNPs and indels from sequence data using VariantTools

A key bioinformatics task is to take an alignment of high-throughput sequence reads, typically stored in a BAM file, and compute a list of variant positions. Of course, this is ably handled by many external command-line programs and tools and usually results in a VCF file of variants, but there are some really powerful packages in Bioconductor that can do the whole thing, and in a fast and efficient manner, by taking advantage of BiocParallel's facilities for parallel evaluation—a set of tools designed to speed up work with large datasets in Bioconductor objects. Using Bioconductor tools allows us to keep all of our processing steps within R, and in this section, we'll go through a whole pipeline—from reads to lists of genes carrying variants—using purely R code and a number of Bioconductor...

Predicting open reading frames in long reference sequences

A draft genome assembly of a previously unsequenced genome can be a rich source of biological knowledge, but when genomics resources such as gene annotations aren't available, it can be tricky to proceed. Here, we'll look at a first stage pipeline for finding potential genes and genomic loci of interest absolutely de novo and without information beyond the sequence. We'll use a very simple set of rules to find open reading frames—sequences that begin with a start codon and end with a stop codon. The tools for doing this are encapsulated within a single function in the Bioconductor package, systemPipeR. We'll end up with yet another GRanges object that we can integrate into processes downstream that allow us to cross-reference other data, such as RNAseq, as we saw in the Finding unannotated transcribed...

Plotting features on genetic maps with karyoploteR

One of the most rewarding and insightful things we can do is visualize data. Very often, we want to see on a chromosome or genetic map where some features of interest lie in relation to others. These are sometimes called chromosome plots, and sometimes ideograms, and in this section, we'll see how to create one of these using the karyoploteR package. The package takes as input the familiar GRanges objects and creates detailed plots from configuration. We'll take a quick look at some different plot styles and some configuration options for ironing out the bumps in your plots when labels spill off the page or overlap each other.

Getting ready

For this recipe, you&apos...

Selecting and classifying variants with VariantAnnotation

In pipelines where we've called variants, we'll often want to do subsequent analysis steps that need further filtering or classification based on features of the individual variants, such as the depth of coverage in the alternative allele. This is best done from a VCF file, and a common protocol is to save a VCF of all variants from the actual calling step and then experiment with filtering that. In this section, we'll look at taking an input VCF and filtering it to retain variants in which the alternative allele is the major allele in the sample.

Getting ready

We'll need a tabix index VCF file; I provide one in the datasets/ch2/sample.vcf.gz file...

Extracting information in genomic regions of interest

Very often, you'll want to look in more detail at data that falls in a particular genomic region of interest, whether that be the SNPs and variants in a gene or the genes in a particular locus. This extremely common task is handled very well by the extremely powerful GRanges and SummarizedExperiment objects, which are a little fiddly to set up but have very flexible subsetting operations that make the effort well worth it. We'll look at a few ways to set up these objects and a few ways we can manipulate them to get interesting information.

Getting ready

In this recipe, we need the GenomicRanges, SummarizedExperiment, and rtracklayer Bioconductor packages. We&apos...

Finding phenotype and genotype associations with GWAS

A powerful application of being able to find many thousands of genetic variants in many samples using high-throughput sequencing is genome-wide association studies (GWAS) of genotype and phenotypes. GWAS is a genomic analysis set of genetic variants in different individuals or genetic lines to see whether any particular variant is associated with a trait. There are numerous techniques for doing this, but all rely on gathering data on variants in particular samples and working out each sample's genotype before cross-referencing with the phenotype in some way or other. In this recipe, we'll look at the sophisticated mixed linear model described by Yu et al in 2006 (Nature Genetics, 38:203-208). Describing the workings of the unified mixed linear model is beyond the scope of the recipe, but it is a suitable model for...

Estimating the copy number at a locus of interest

It is often of interest to know how often a sequence occurs in a sample of interest—that is, to estimate whether, in your particular sample, a locus has been duplicated or its copy number has increased. The locus could be anything from a gene at Kbp scale or a large section of DNA at Mbp scale. Our approach in this recipe will be to use HTS read coverage after alignment to estimate a background level of coverage and then inspect the coverage of our region of interest. The ratio of the coverage in our region of interest to the background level will give us an estimate of the copy number in the region. The recipe here is the first step. The background model we use is very simple—we calculate only a global mean, but we'll discuss some alternatives later. Also, this recipe does not cover ploidy—the number...

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Key benefits

  • Apply modern R packages to handle biological data using real-world examples
  • Represent biological data with advanced visualizations suitable for research and publications
  • Handle real-world problems in bioinformatics such as next-generation sequencing, metagenomics, and automating analyses

Description

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.

Who is this book for?

This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. Working knowledge of R programming language and basic knowledge of bioinformatics are prerequisites.

What you will learn

  • 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
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Table of Contents

12 Chapters
Performing Quantitative RNAseq Chevron down icon Chevron up icon
Finding Genetic Variants with HTS Data Chevron down icon Chevron up icon
Searching Genes and Proteins for Domains and Motifs Chevron down icon Chevron up icon
Phylogenetic Analysis and Visualization Chevron down icon Chevron up icon
Metagenomics Chevron down icon Chevron up icon
Proteomics from Spectrum to Annotation Chevron down icon Chevron up icon
Producing Publication and Web-Ready Visualizations Chevron down icon Chevron up icon
Working with Databases and Remote Data Sources Chevron down icon Chevron up icon
Useful Statistical and Machine Learning Methods Chevron down icon Chevron up icon
Programming with Tidyverse and Bioconductor Chevron down icon Chevron up icon
Building Objects and Packages for Code Reuse Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 33.3%
1 star 33.3%
sreechakilam Nov 16, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
like, very useful
Amazon Verified review Amazon
Cliente Amazon Jul 26, 2023
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Even for begginers it is pretty poor. Any tutorial in the internet may be more useful for the topics included in the book. It may be useful to get a general idea, but not to be introduced in practical bioinformatics
Amazon Verified review Amazon
RUser Jan 04, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Hallo, dieses Buch leidet unter der Verwendung von R-Libraries, die zumindes zu Anfang des Jahres 2021 nicht funktionieren. Das ist immer der Tod von Cook Books! Hier handelt es sich um die für viele "Rezepte" essentielle Library gmapR, die beispielsweise für das Betriebssystem Windows nicht verfügbar ist (Stand Anfang 2021). Unter den neuesten Versionen von Ubuntu und R hat es aber auch nicht funktioniert.Schade, aber das ist natürlich immer eine Gefahr bei kostenloser Software, die - wie in diesem Falle R mit Bioconductor - irgendwann zu komplex wird, um zuverlässig zu funktionieren.Da nutzt dann auch das schönste Kochbuch nichts, wenn man kein Feuer im Herd hat.Ein Update: Ich gab dem Buch eine zweite Chance. Vergebens, der Code der (Stand Januar 2021) verfügbar war, enthält viele kleine Fehler (fehlerhafte Verweise auf Dateiverzeichnisse), die nur lästig sind, und schwere inhaltliche Fehler. Man kann den Code eventuell selbst korrigieren, dann braucht man aber das Buch nicht mehr... Das Recipe3.R des dritten Kapitels ist eine Zumutung. Wieder wird eine Library verwendet, die noch in der Entwicklung ist, und deren Herunterladen aufgrund vieler Abhängigkeiten reine Glückssache ist. Ich habe es nach mehreren Stunden aufgegeben. Und der Code, der das Herunterladen vereinfachen soll - ja: Code, um das komplexe Herunterladen überhaupt organisieren zu können - den kann man abschreiben. Er ist nicht in den zum Buch verfügbaren Codes enthalten. Viel Spaß dabei.Das Buch hätte eine Lücke füllen können, so füllt es eine Lücke im Altpapier. Der Verlag bedauert...
Amazon Verified review Amazon
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