Transcriptome data analysis : methods and protocols / edited by Yejun Wang, Ming-an Sun.
Contributor(s): Wang, Yejun [editor.] | Sun, Ming-an [editor.]Material type: TextSeries: Methods in molecular biology (Clifton, N.J.): v. 1751.Publisher: New York, NY : Humana Press : Springer, Copyright date: ©2018Description: 1 online resource (x, 238 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9781493977109; 1493977105Subject(s): Genetic transcription | Transcriptome | Transcription, Genetic | Sequence Analysis, RNA | Genetic transcriptionGenre/Form: Laboratory Manual. | Electronic books. Additional physical formats: Print version:: No titleDDC classification: 572.8/845 LOC classification: QH450.2Online resources: Click here to access online
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Includes bibliographical references and index.
Comparison of gene expression profiles in nonmodel eukaryotic organisms with RNA-Seq / Han Cheng, Yejun Wang, and Ming-an Sun -- Microarray data analysis for transcriptome profiling / Ming-an Sun, Xiaojian Shao, and Yejun Wang -- Pathway and network analysis of differentially expressed genes in transcriptomes / Qianli Huang, Ming-an Sun, and Ping Yan -- QuickRNASeq : guide for pipeline implementation and for interactive results visualization / Wen He, Shanrong Zhao, Chi Zhang, Michael S. Vincent, and Baohong Zhang -- Tracking alternatively spliced isoforms from long reads by SpliceHunter / Zheng Kuang and Stefan Canzar -- RNA-seq-based transcript structure analysis with TrBorderExt / Yejun Wang, Ming-an Sun, and Aaron P. White -- Analysis of RNA editing sites from RNA-seq data using GIREMI / Qing Zhang -- Bioinformatic analysis of MicroRNA sequencing data / Xiaonan Fu and Daoyuan Dong -- Microarray-based MicroRNA expression data analysis with bioconductor / Emilio Mastriani, Rihong Zhai, and Songling Zhu -- Identification and expression analysis of long intergenic noncoding RNAs / Ming-an Sun, Rihong Zhai, Qing Zhang, and Yejun Wang -- Analysis of RNA-seq data using TEtranscripts / Ying Jin and Molly Hammell -- Computational analysis of RNA-protein interactions via deep sequencing / Lei Li, Konrad U. Forstner, and Yanjie Chao -- Predicting gene expression noise from gene expression variations / Xiaojian Shao and Ming-an Sun -- Protocol for epigenetic imprinting analysis with RNA-Seq data / Jinfeng Zou, Daoquan Xiang, Raju Datla, and Edwin Wang -- Single-cell transcriptome analysis using SINCERA pipeline / Minzhe Guo and Yan Xu -- Mathematical modeling and deconvolution of molecular heterogeneity identifies novel subpopulations in complex tissues / Niya Wang, Lulu Chen, and Yue Wang.
Online resource; title from PDF title page (SpringerProtocol, viewed March 6, 2018).
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.