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Transcriptome Data Analysis

Methods and Protocols

  • Book
  • © 2018

Overview

Editors:
  1. Yejun Wang

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

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  2. Ming-an Sun

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Includes cutting-edge techniques for the study of transcriptome data analysis
  • Provides step-by-step detail essential for reproducible results
  • Contains key implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 1751)

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Table of contents (16 protocols)

  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
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Keywords

About this book

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.

Editors and Affiliations

  • 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

Bibliographic Information

  • Book Title: Transcriptome Data Analysis

  • Book Subtitle: Methods and Protocols

  • Editors: Yejun Wang, Ming-an Sun

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-4939-7710-9

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC 2018

  • Hardcover ISBN: 978-1-4939-7709-3Published: 06 March 2018

  • Softcover ISBN: 978-1-4939-9264-5Published: 10 December 2019

  • eBook ISBN: 978-1-4939-7710-9Published: 05 March 2018

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: X, 238

  • Number of Illustrations: 5 b/w illustrations, 50 illustrations in colour

  • Topics: Human Genetics

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