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Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes

  • Qianli Huang
  • Ming-an Sun
  • Ping YanEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1751)

Abstract

In recent years, transcriptome sequencing has become very popular, encompassing a wide variety of applications from simple mRNA profiling to discovery and analysis of the entire transcriptome. One of the most common aims of transcriptome sequencing is to identify genes that are differentially expressed (DE) between two or more biological conditions, and to infer associated pathways and gene networks from expression profiles. It can provide avenues for further systematic investigation into potential biologic mechanisms. Gene Set (GS) enrichment analysis is a popular approach to identify pathways or sets of genes that are significantly enriched in the context of differentially expressed genes. However, the approach considers a pathway as a simple gene collection disregarding knowledge of gene or protein interactions. In contrast, topology-based methods integrate the topological structure of a pathway and gene network into the analysis. To provide a panoramic view of such approaches, this chapter demonstrates several recent computational workflows, including gene set enrichment and topology-based methods, for analysis of the DE pathways and gene networks from transcriptome-wide sequencing data.

Key words

Transcriptome RNA-Seq Microarray Pathway Network Topology Enrichment analysis 

Notes

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. JZ2017YYPY0899). The authors are grateful to the editors and the anonymous reviewers for their valuable suggestions and comments facilitating the improvement of this chapter.

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Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.School of Biological and Medical EngineeringHefei University of TechnologyHefeiChina
  2. 2.Epigenomics and Computational Biology LabBiocomplexity Institute of Virginia TechBlacksburgUSA

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