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Transcriptome Data Analysis :Methods...

Sun, Ming-an,

Transcriptome Data Analysis :Methods and Protocols /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Transcriptome Data Analysis :edited by Yejun Wang, Ming-an Sun.
Reminder of title:	Methods and Protocols /
other author:	Wang, Yejun,
Description:	1 online resource (x, 238 pages 55 illustrations, 50 illustrations in color.) :online resource
Subject:	Medicine.
Online resource:	https://doi.org/10.1007/978-1-4939-7710-9
Online resource:	https://link.springer.com/book/10.1007/978-1-4939-7709-3
Online resource:	https://link.springer.com/book/10.1007/978-1-4939-7710-9
ISBN:	9781493992645

Transcriptome Data Analysis :Methods and Protocols /
Transcriptome Data Analysis :Methods and Protocols /edited by Yejun Wang, Ming-an Sun. - 1 online resource (x, 238 pages 55 illustrations, 50 illustrations in color.) :online resource - Methods in Molecular Biology,17511064-3745 ;. - Methods in molecular biology ;1751..

Includes bibliographical references and index.

Comparison 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.

This 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.

ISBN: 9781493992645EUR$84.99

Source: cam 2200493Mi 4500Subjects--Topical Terms:

193819
Medicine.
Index Terms--Genre/Form:

854751
Laboratory Manual.

LC Class. No.: QH431

Transcriptome Data Analysis :Methods and Protocols /
LDR:03077nam a2200277 a 4500 001 568775
003 OCoLC
005 20200707224615.0
008 200708s2019 nyu 000 0 eng d
020 $a 9781493992645 $c EUR$84.99
020 $a 1493992643
020 $a 9781493977109(e-book)
037 8 3 $a cam 2200493Mi 4500
050 # 4 $a QH431
245 0 0 $a Transcriptome Data Analysis : $b Methods and Protocols / $c edited by Yejun Wang, Ming-an Sun.
264 1 $a New York, NY : $b Springer New York : $b Imprint : $b Humana Press, $c 2018.
300 $a 1 online resource (x, 238 pages 55 illustrations, 50 illustrations in color.) : $b online resource
490 1 $a Methods in Molecular Biology, $x 1064-3745 ; $v 1751
504 $a Includes bibliographical references and index.
505 0 # $a Comparison 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 # $a This 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.
650 # 0 $a Medicine. $3 193819
650 # 0 $a Human genetics. $3 192904
650 1 2 $a Gene Expression Profiling. $3 209042
655 # 2 $a Laboratory Manual. $3 854751
700 1 # $a Wang, Yejun, $e editor. $3 854748
700 1 # $a Sun, Ming-an, $e editor. $3 854749
776 0 8 $i Printed edition: $z 9781493977093
830 0 $a Methods in molecular biology ; $v 1751. $x 1064-3745 $3 854750
856 4 0 $3 SpringerLink $u https://doi.org/10.1007/978-1-4939-7710-9
856 4 0 $3 SpringerLink $u https://link.springer.com/book/10.1007/978-1-4939-7709-3
856 4 0 $3 SpringerLink $u https://link.springer.com/book/10.1007/978-1-4939-7710-9
938 $a YBP Library Services $b YANK $n 15252786
994 $a C0 $b TWNUK

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https://doi.org/10.1007/978-1-4939-7710-9
https://link.springer.com/book/10.1007/978-1-4939-7709-3
https://link.springer.com/book/10.1007/978-1-4939-7710-9

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