Transcriptome Data Analysis: Methods and Protocols

ISBN-13: 9781493992645 / Angielski / Miękka / 2019 / 238 str.

Yejun Wang; Ming-An Sun

Spis treści

Part I: General Protocols on Transcriptome Data Analysis

 

1. Comparison of Gene Expression Profiles in Non-Model Eukaryotic Organisms with RNA-Seq

            Han Cheng, Yejun Wang, and Ming-an Sun

 

2. Microarray Data Analysis for Transcriptome Profiling

            Ming-an Sun, Xiaojian Shao, and Yejun Wang

 

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

            Qianli Huang, Ming-an Sun, and Ping Yan

 

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

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

 

Part II: Objective-Specialized Transcriptome Data Analysis

 

5. Tracking Alternatively Spliced Isoforms from Long Reads by SpliceHunter

            Zheng Kuang and Stefan Canzar

 

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

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

 

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

            Qing Zhang

 

8. Bioinformatic Analysis of MicroRNA Sequencing Data

            Xiaonan Fu and Daoyuan Dong

 

9. Microarray-Based MicroRNA Expression Data Analysis with Bioconductor

            Emilio Mastriani, Rihong Zhai, and Songling Zhu

 

10. Identification and Expression Analysis of Long Intergenic Non-Coding RNAs

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

 

11. Analysis of RNA-Seq Data Using TEtranscripts

            Ying Jin and Molly Hammell

 

Part III: New Applications of Transcriptome

 

12. Computational Analysis of RNA-Protein Interactions via Deep Sequencing

            Lei Li, Konrad U. Förstner, and Yanjie Chao

 

13. Predicting Gene Expression Noise from Gene Expression Variations

            Xiaojian Shao and Ming-an Sun

 

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

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

 

15. Single-Cell Transcriptome Analysis Using SINCERA Pipeline

            Minzhe Guo and Yan Xu

 

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

            Niya Wang, Lulu Chen, and Yue Wang

Blurb

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.