000			03486nam a2200397 i 4500
999			_c392302 _d392302
001			392302
003			ES-MaUEC
005			20231027164621.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			180305s2018 xxua o |||| 0|eng d
020			_a9781493977109
024	7		_a10.1007/978-1-4939-7710-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQH450.2 _b2018 EB
245	0	0	_aTranscriptome Data Analysis : _bMethods and Protocols _cedited by Yejun Wang, Ming-an Sun
250			_a1st edition 2018
264		1	_aNew York, NY _bSpringer International Publishing _c2018
300			_a1 recurso en línea (X, 238 páginas) _b55 ilustraciones, 50 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 _v1751
505	0		_aComparison of Gene Expression Profiles in Non-Model Eukaryotic Organisms with RNA-Seq -- Microarray Data Analysis for Transcriptome Profiling -- Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes -- QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization -- Tracking Alternatively Spliced Isoforms from Long Reads by SpliceHunter -- RNA-Seq-Based Transcript Structure Analysis with TrBorderExt -- Analysis of RNA Editing Sites from RNA-Seq Data Using GIREMI -- Bioinformatic Analysis of MicroRNA Sequencing Data -- Microarray-Based MicroRNA Expression Data Analysis with Bioconductor -- Identification and Expression Analysis of Long Intergenic Non-Coding RNAs -- Analysis of RNA-Seq Data Using TEtranscripts -- Computational Analysis of RNA-Protein Interactions via Deep Sequencing -- Predicting Gene Expression Noise from Gene Expression Variations -- A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data -- Single-Cell Transcriptome Analysis Using SINCERA Pipeline -- Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues.
520			_aThis detailed volume provides comprehensive practical guidance on transcriptome data analysis for a variety of scientific purposes. Beginning with general protocols, the collection moves on to explore protocols for gene characterization analysis with RNA-seq data as well as protocols on several new applications of transcriptome studies.  Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.  Authoritative and useful, Transcriptome Data Analysis: Methods and Protocols serves as an ideal guide to the expanding purposes of this field of study.
988			_aSpringer_Protocols_2018
650		7	_2embne _9147235 _aTranscripción genética _vManuales de laboratorio
776	0	8	_iPrinted edition: _z9781493977093
776	0	8	_iPrinted edition: _z9781493977116
776	0	8	_iPrinted edition: _z9781493992645
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-4939-7710-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2023 _dz _eb _zSI