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Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Genetics (non-medical) > DNA
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Primer to Analysis of Genomic Data Using R (Paperback)
Loot Price: R2,443
Discovery Miles 24 430
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Primer to Analysis of Genomic Data Using R (Paperback)
Series: Use R!
Expected to ship within 18 - 22 working days
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Through this book, researchers and students will learn to use R for
analysis of large-scale genomic data and how to create routines to
automate analytical steps. The philosophy behind the book is to
start with real world raw datasets and perform all the analytical
steps needed to reach final results. Though theory plays an
important role, this is a practical book for graduate and
undergraduate courses in bioinformatics and genomic analysis or for
use in lab sessions. How to handle and manage high-throughput
genomic data, create automated workflows and speed up analyses in R
is also taught. A wide range of R packages useful for working with
genomic data are illustrated with practical examples. The key
topics covered are association studies, genomic prediction,
estimation of population genetic parameters and diversity, gene
expression analysis, functional annotation of results using
publically available databases and how to work efficiently in R
with large genomic datasets. Important principles are demonstrated
and illustrated through engaging examples which invite the reader
to work with the provided datasets. Some methods that are discussed
in this volume include: signatures of selection, population
parameters (LD, FST, FIS, etc); use of a genomic relationship
matrix for population diversity studies; use of SNP data for
parentage testing; snpBLUP and gBLUP for genomic prediction.
Step-by-step, all the R code required for a genome-wide association
study is shown: starting from raw SNP data, how to build databases
to handle and manage the data, quality control and filtering
measures, association testing and evaluation of results, through to
identification and functional annotation of candidate genes.
Similarly, gene expression analyses are shown using microarray and
RNAseq data. At a time when genomic data is decidedly big, the
skills from this book are critical. In recent years R has become
the de facto< tool for analysis of gene expression data, in
addition to its prominent role in analysis of genomic data.
Benefits to using R include the integrated development environment
for analysis, flexibility and control of the analytic workflow.
Included topics are core components of advanced undergraduate and
graduate classes in bioinformatics, genomics and statistical
genetics. This book is also designed to be used by students in
computer science and statistics who want to learn the practical
aspects of genomic analysis without delving into algorithmic
details. The datasets used throughout the book may be downloaded
from the publisher's website.
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