Overview

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.

Full Product Details

Author:   Yejun Wang ,  Ming-an Sun
Publisher:   Humana Press Inc.
Imprint:   Humana Press Inc.
Edition:   2018 ed.
Volume:   1751
Weight:   0.702kg
ISBN:  

9781493977093

ISBN 10:   1493977091
Pages:   238
Publication Date:   06 March 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Comparison of Gene Expression Profiles in Non-Model Eukaryotic Organisms with RNA-Seq.- Microarray Data Analysis for Transcriptome Profiling.- Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes.- QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization.- Tracking Alternatively Spliced Isoforms from Long Reads by SpliceHunter.- RNA-Seq-Based Transcript Structure Analysis with TrBorderExt.- Analysis of RNA Editing Sites from RNA-Seq Data Using GIREMI.- Bioinformatic Analysis of MicroRNA Sequencing Data.- Microarray-Based MicroRNA Expression Data Analysis with Bioconductor.- Identification and Expression Analysis of Long Intergenic Non-Coding RNAs.- Analysis of RNA-Seq Data Using TEtranscripts.- Computational Analysis of RNA-Protein Interactions via Deep Sequencing.- Predicting Gene Expression Noise from Gene Expression Variations.- A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data.- Single-Cell Transcriptome Analysis Using SINCERA Pipeline.- Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues.

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