000			03839nam a22003735i 4500
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007			cr nn 008mamaa
008			160323s2016 xxu| s |||| 0|eng d
020			_a9781493935789
024	7		_a10.1007/978-1-4939-3578-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aStatistical Genomics _bMethods and Protocols _cedited by Ewy Mathé, Sean Davis.
250			_a1st edition 2016
264		1	_aNew York, NY _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XI, 418 páginas) _b113 ilustraciones, 85 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v1418
505	0		_aOverview of Sequence Data Formats -- Integrative Exploratory Analysis of Two or More Genomic Datasets -- Study Design for Sequencing Studies -- Genomic Annotation Resources in R/Bioconductor -- The Gene Expression Omnibus Database -- A Practical Guide to the Cancer Genome Atlas (TCGA) -- Working with Oligonucleotide Arrays -- Meta-Analysis in Gene Expression Studies -- Practical Analysis of Genome Contact Interaction Experiments -- Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data -- Variant Calling From Next Generation Sequence Data -- Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes -- NGS-QC Generator: A Quality Control System for ChIP-seq and Related Deep Sequencing-Generated Datasets -- Operating on Genomic Ranges Using BEDOPS -- GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality -- Visualizing Genomic Data using Gviz and Bioconductor -- Introducing Machine Learning Concepts with WEKA -- Experimental Design and Power Calculation for RNA-Seq Experiments -- It's DE-licious: A Recipe for Differential Expression Analyses of RNA-Seq Experiments Using Quasi-Likelihood Methods in EdgeR.
520			_aThis volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data.
700	1		_aMathé, Ewy _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDavis, Sean _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9781493935765
776	0	8	_iPrinted edition: _z9781493935772
776	0	8	_iPrinted edition: _z9781493980833
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-4939-3578-9 _z(usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Protocols_2016
999			_c234400 _d234400