Identification of temporal network changes in short-course gene expression from C. Elegans reveals structural volatility

Research output: Contribution to journalArticle

Abstract

Many bioinformatics algorithms attempt to extract relevant biological information from datasets obtained at specific data points. However, it is critical to identify changing genes in temporal data so that studies can focus on the dynamics of gene expression. While networks continue to play a significant role in characterising biological relationships, most biomedical network modelling studies focus on 'static' network-based analysis. In this study, we use a temporal, network-based approach to identify and rank genes that exhibit variation in short-course gene expression. We use a Caenorhabditis elegans (C. elegans) gene correlation network obtained from mRNA expression to illustrate the value of the proposed approach, and compare the results of this method to results obtained from traditional differential gene expression analysis. We show that temporal network analysis identifies genes that are inherently different from differentially expressed genes, raising new questions about structural meaning in expression networks and how changes in expression are observed.

Original languageEnglish (US)
Pages (from-to)171-188
Number of pages18
JournalInternational Journal of Computational Biology and Drug Design
Volume12
Issue number2
DOIs
StatePublished - Jan 1 2019

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Volatilization
Gene expression
Genes
Gene Expression
Gene Regulatory Networks
Caenorhabditis elegans
Computational Biology
Bioinformatics
Electric network analysis
Messenger RNA

Keywords

  • Correlation networks
  • Gene expression
  • Node ranking
  • Reliability
  • Temporal network analysis

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications

Cite this

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abstract = "Many bioinformatics algorithms attempt to extract relevant biological information from datasets obtained at specific data points. However, it is critical to identify changing genes in temporal data so that studies can focus on the dynamics of gene expression. While networks continue to play a significant role in characterising biological relationships, most biomedical network modelling studies focus on 'static' network-based analysis. In this study, we use a temporal, network-based approach to identify and rank genes that exhibit variation in short-course gene expression. We use a Caenorhabditis elegans (C. elegans) gene correlation network obtained from mRNA expression to illustrate the value of the proposed approach, and compare the results of this method to results obtained from traditional differential gene expression analysis. We show that temporal network analysis identifies genes that are inherently different from differentially expressed genes, raising new questions about structural meaning in expression networks and how changes in expression are observed.",
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