Transcriptome Data Analysis : Methods and Protocols / edited by Yejun Wang, Ming-an Sun.

Format:

Book

Contributor:

Wang, Yejun, editor.

Sun, Ming-an, editor.

SpringerLink (Online service)

Series:

Methods in molecular biology 1064-3745 ; 1751.

Methods in Molecular Biology, 1064-3745 ; 1751

Language:

English

Subjects (All):

Medicine.

Human genetics.

Biomedicine.

Human Genetics.

Local Subjects:

Biomedicine.

Human Genetics.

Physical Description:

1 online resource (X, 238 pages) : 55 illustrations, 50 illustrations in color.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer New York : Imprint: Humana Press, 2018.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

9781493977109

Access Restriction:

Restricted for use by site license.