Mathematical modeling and deconvolution of molecular heterogeneity identifies novel subpopulations in complex tissues / Niya Wang, Lulu Chen, and Yue Wang. 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.
Summary:
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.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.