LEADER 05252nam 22007095 450 001 9910299769803321 005 20220627155838.0 010 $a3-319-14475-8 024 7 $a10.1007/978-3-319-14475-7 035 $a(CKB)3710000000414305 035 $a(EBL)2097291 035 $a(SSID)ssj0001501675 035 $a(PQKBManifestationID)11840183 035 $a(PQKBTitleCode)TC0001501675 035 $a(PQKBWorkID)11446596 035 $a(PQKB)11284965 035 $a(DE-He213)978-3-319-14475-7 035 $a(MiAaPQ)EBC2097291 035 $a(PPN)186030819 035 $a(EXLCZ)993710000000414305 100 $a20150516d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPrimer to Analysis of Genomic Data Using R$b[electronic resource] /$fby Cedric Gondro 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (283 p.) 225 1 $aUse R!,$x2197-5736 300 $aDescription based upon print version of record. 311 $a3-319-14474-X 320 $aIncludes bibliographical references. 327 $aR basics -- Simple marker association tests -- Genome wide association studies -- Population and genetic architecture -- Gene expression analysis -- Databases and functional information -- Extending R -- Final comments -- Index -- References. 330 $aThrough 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 advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics or for use in lab sessions. 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. Chapters show how to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R. A wide range of R packages useful for working with genomic data are illustrated with practical examples. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in the analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. At a time when genomic data is decidedly big, the skills from this book are critical. 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. . 410 0$aUse R!,$x2197-5736 606 $aStatistics  606 $aGene expression 606 $aMicroarrays 606 $aR (Computer program language) 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aGene Expression$3https://scigraph.springernature.com/ontologies/product-market-codes/B12010 606 $aMicroarrays$3https://scigraph.springernature.com/ontologies/product-market-codes/B12050 615 0$aStatistics . 615 0$aGene expression. 615 0$aMicroarrays. 615 0$aR (Computer program language). 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aGene Expression. 615 24$aMicroarrays. 676 $a572.838 700 $aGondro$b Cedric$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755555 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299769803321 996 $aPrimer to analysis of genomic data using R$91522556 997 $aUNINA