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.
