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020 _a9780429803369
_q(electronic bk.)
020 _a0429803362
_q(electronic bk.)
020 _a9780429440557
_q(electronic bk.)
020 _a0429440553
_q(electronic bk.)
020 _a9780429803376
_q(electronic bk. : PDF)
020 _a0429803370
_q(electronic bk. : PDF)
020 _a9780429803352
_q(electronic bk. : Mobipocket)
020 _a0429803354
_q(electronic bk. : Mobipocket)
020 _z9781138340565
020 _z1138340561
035 _a(OCoLC)1104841012
035 _a(OCoLC-P)1104841012
050 4 _aQH442
_b.G6475 2019eb
072 7 _aSCI
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_2bisacsh
072 7 _aMAT
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_2bisacsh
072 7 _aSCI
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072 7 _aPS
_2bicssc
082 0 4 _a572/.33
_223
100 1 _aGonzález, Juan R.
_c(Bioinformatics researcher),
_eauthor.
245 1 0 _aOmic association studies with R and Bioconductor /
_cJuan R. González, Alejandro Cáceres.
264 1 _aBoca Raton, Florida :
_bCRC Press,
_c[2019]
264 4 _c©2019
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _aAfter the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions
505 0 _aCover; Half Title; Title Page; Copyright Page; Dedication; Contents; Preface; 1 Introduction; 1.1 Book overview; 1.2 Overview of omic data; 1.2.1 Genomic data; 1.2.1.1 Genomic SNP data; 1.2.1.2 SNP arrays; 1.2.1.3 Sequencing methods; 1.2.2 Genomic data for other structural variants; 1.2.3 Transcriptomic data; 1.2.3.1 Microarrays; 1.2.3.2 RNA-seq; 1.2.4 Epigenomic data; 1.2.5 Exposomic data; 1.3 Association studies; 1.3.1 Genome-wide association studies; 1.3.2 Whole transcriptome pro ling; 1.3.3 Epigenome-wide association studies; 1.3.4 Exposome-wide association studies
505 8 _a1.4 Publicly available resources1.4.1 dbGaP; 1.4.2 EGA; 1.4.3 GEO; 1.4.4 1000 Genomes; 1.4.5 GTEx; 1.4.6 TCGA; 1.4.7 Others; 1.5 Bioconductor; 1.5.1 R; 1.5.2 Omic data in Bioconductor; 1.6 Book's outline; 2 Case examples; 2.1 Chapter overview; 2.2 Reproducibility: The case for public data repositories; 2.3 Case 1: dbGaP; 2.4 Case 2: GEO; 2.5 Case 3: GTEx; 2.6 Case 4: TCGA; 2.7 Case 5: NHANES; 3 Dealing with omic data in Bioconductor; 3.1 Chapter overview; 3.2 snpMatrix; 3.3 ExpressionSet; 3.4 SummarizedExperiment; 3.5 GRanges; 3.6 RangedSummarizedExperiment; 3.7 ExposomeSet
505 8 _a3.8 MultiAssayExperiment3.9 MultiDataSet; 4 Genetic association studies; 4.1 Chapter overview; 4.2 Genetic association studies; 4.2.1 Analysis packages; 4.2.2 Association tests; 4.2.3 Single SNP analysis; 4.2.4 Hardy{Weinberg equilibrium; 4.2.5 SNP association analysis; 4.2.6 Gene environment and gene gene interactions; 4.3 Haplotype analysis; 4.3.1 Linkage disequilibrium heatmap plots; 4.3.2 Haplotype estimation; 4.3.3 Haplotype association; 4.3.4 Sliding window approach; 4.4 Genetic score; 4.5 Genome-wide association studies; 4.5.1 Quality control of SNPs
505 8 _a4.5.2 Quality control of individuals4.5.3 Population ancestry; 4.5.4 Genome-wide association analysis; 4.5.5 Adjusting for population strati cation; 4.6 Post-GWAS visualization and interpretation; 4.6.1 Genome-wide associations for imputed data; 5 Genomic variant studies; 5.1 Chapter overview; 5.2 Copy number variants; 5.2.1 CNV calling; 5.3 Single CNV association; 5.3.1 Inferring copy number status from signal data; 5.3.2 Measuring uncertainty of CNV calling; 5.3.3 Assessing the association between CNVs and traits; 5.3.3.1 Modeling association; 5.3.3.2 Global test of associations
505 8 _a5.3.4 Whole genome CNV analysis5.4 Genetic mosaicisms; 5.4.1 Calling genetic mosaicisms; 5.4.2 Calling the loss of chromosome Y; 5.5 Polymorphic inversions; 5.5.1 Inversion detection; 5.5.2 Inversion calling; 5.5.3 Inversion association; 6 Addressing batch e ects; 6.1 Chapter overview; 6.2 SVA; 6.3 ComBat; 7 Transcriptomic studies; 7.1 Chapter overview; 7.2 Microarray data; 7.2.1 Normalization; 7.2.2 Filter; 7.2.3 Di erential expression; 7.3 Next generation sequencing data; 7.3.1 Normalization; 7.3.2 Gene ltering; 7.3.3 Di erential expression; 8 Epigenomic studies; 8.1 Chapter overview
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aMolecular genetics.
650 0 _aMolecular genetics
_xData processing.
650 0 _aPhenotype.
650 0 _aGene expression.
650 0 _aDNA.
650 0 _aR (Computer program language)
650 7 _aSCIENCE / Life Sciences / Biochemistry
_2bisacsh
650 7 _aMATHEMATICS / Probability & Statistics / General
_2bisacsh
650 7 _aSCIENCE / Life Sciences / Biology / General
_2bisacsh
700 1 _aCáceres, Alejandro
_c(Bioinformatics researcher),
_eauthor.
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780429440557
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c130516
_d130516