Primer to Analysis of Genomic Data Using R

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and create routines to automate analytical steps. It covers everything from principles of genomic prediction to which R packages to use and how to speed up the high-throughput analysis. Other topics include the principles of microarray management, effective building of databases, instruction for working with public databases, and topics from all stages of genomic data analysis all while using R. Not only are important principles demonstrated, but they are illustrated through engaging examples which invite the reader to work with the provided datasets. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or use in lab sessions. Some methods that are unique to this volume include: signatures of selection, population parameters (FST, FIS, etc); analysis of haplotypes and recombination; identification of candidate genes from GWAS; use of genomic relationship matrix for population diversity studies; genomic blup (gBLUP) for prediction, and analyses of 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 immediately applicable to 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.§