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

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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...

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

What do you get with Print?

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sreechakilam Nov 16, 2020

like, very useful

Amazon Verified review

Cliente Amazon Jul 26, 2023

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

RUser Jan 04, 2021

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...

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

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

Getting ready

Selecting and classifying variants with VariantAnnotation

Getting ready

Extracting information in genomic regions of interest

Getting ready

Finding phenotype and genotype associations with GWAS

Estimating the copy number at a locus of interest

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

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Key benefits

Description

Who is this book for?

What you will learn

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Table of Contents

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