Transcriptome Data Analysis

1. Front Matter

   Pages i-x

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2. General Protocols on Transcriptome Data Analysis

   1. Front Matter

      Pages 1-1

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   2. Comparison of Gene Expression Profiles in Nonmodel Eukaryotic Organisms with RNA-Seq

      • Han Cheng, Yejun Wang, Ming-an Sun

      Pages 3-16

   3. Microarray Data Analysis for Transcriptome Profiling

      • Ming-an Sun, Xiaojian Shao, Yejun Wang

      Pages 17-33

   4. Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes

      • Qianli Huang, Ming-an Sun, Ping Yan

      Pages 35-55

   5. QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization

      • Wen He, Shanrong Zhao, Chi Zhang, Michael S. Vincent, Baohong Zhang

      Pages 57-70

3. Objective-Specialized Transcriptome Data Analysis

   1. Front Matter

      Pages 71-71

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   2. Tracking Alternatively Spliced Isoforms from Long Reads by SpliceHunter

      • Zheng Kuang, Stefan Canzar

      Pages 73-88

   3. RNA-Seq-Based Transcript Structure Analysis with TrBorderExt

      • Yejun Wang, Ming-an Sun, Aaron P. White

      Pages 89-99

   4. Analysis of RNA Editing Sites from RNA-Seq Data Using GIREMI

      • Qing Zhang

      Pages 101-108

   5. Bioinformatic Analysis of MicroRNA Sequencing Data

      • Xiaonan Fu, Daoyuan Dong

      Pages 109-125

   6. Microarray-Based MicroRNA Expression Data Analysis with Bioconductor

      • Emilio Mastriani, Rihong Zhai, Songling Zhu

      Pages 127-138

   7. Identification and Expression Analysis of Long Intergenic Noncoding RNAs

      • Ming-an Sun, Rihong Zhai, Qing Zhang, Yejun Wang

      Pages 139-152

   8. Analysis of RNA-Seq Data Using TEtranscripts

      • Ying Jin, Molly Hammell

      Pages 153-167

4. New Applications of Transcriptome

   1. Front Matter

      Pages 169-169

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   2. Computational Analysis of RNA–Protein Interactions via Deep Sequencing

      • Lei Li, Konrad U. Förstner, Yanjie Chao

      Pages 171-182

   3. Predicting Gene Expression Noise from Gene Expression Variations

      • Xiaojian Shao, Ming-an Sun

      Pages 183-198

   4. A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data

      • Jinfeng Zou, Daoquan Xiang, Raju Datla, Edwin Wang

      Pages 199-208

   5. Single-Cell Transcriptome Analysis Using SINCERA Pipeline

      • Minzhe Guo, Yan Xu

      Pages 209-222

   6. Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues

      • Niya Wang, Lulu Chen, Yue Wang

      Pages 223-236

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.

• RNA-seq
• Biomarker discovery
• RNA Editing
• Data processing
• Gene structure analysis
• Computational techniques
• Imprinting studies

• Department of Cell Biology and Genetics, School of Basic Medicine, Shenzhen University Health, Science Center, Shenzhen, China

  Yejun Wang

• Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, USA

  Ming-an Sun

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