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Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
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R Bioinformatics Cookbook - Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis (Paperback)
Loot Price: R1,246
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R Bioinformatics Cookbook - Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis (Paperback)
Expected to ship within 10 - 15 working days
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Over 60 recipes to model and handle real-life biological data using
modern libraries from the R ecosystem Key Features 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 Book DescriptionHandling 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.
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
Who this book is forThis 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.
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