Transcriptome Data Analysis

Methods and Protocols
Herausgegeben:Wang, Yejun; Sun, Ming-an

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Methods in Molecular Biology 1751
• Verlag: Humana / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4939-9264-5
• Softcover reprint of the original 1st ed. 2018
• Seitenzahl: 238
• Erscheinungstermin: 10. Dezember 2019
• Englisch
• Abmessung: 14mm x 181mm x 254mm
• Gewicht: 466g
• ISBN-13: 9781493992645
• ISBN-10: 1493992643
• Artikelnr.: 55119752

Inhaltsangabe

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.