GET THE APP

Use of GeneNet to infer Arabidopsis gene regulatory networks (GRN | 3826
Transcriptomics: Open Access

Transcriptomics: Open Access
Open Access

ISSN: 2329-8936

+44 1223 790975

Use of GeneNet to infer Arabidopsis gene regulatory networks (GRNs) from microarray data


International Conference on Transcriptomics

July 27-29, 2015 Orlando, USA

David Foutch

Accepted Abstracts: Transcriptomics

Abstract :

Since the introduction of microarray technology, a variety of statistical methods have been developed in an endeavor to reconstruct the underlying dynamics of transcription regulated gene networks. Pairwise correlation coefficients have been calculated to create gene co-expression networks. Co-expression networks have been used to predict gene function through association with functionally related clusters. PPI networks have served very similar predictive purposes. Like PPI networks, coexpression networks are static networks and therefore lack the temporal features that yield the hierarchical structure necessary for representing causal dynamics. In the present work, we report on the use of GeneNet to infer Arabidopsis gene regulatory networks (GRNs) from microarray data. GeneNet computes partial correlation coefficients to generate edge associations (topology) and partial variances are computed to infer edge direction (causality). GRNs were inferred from the At Gen Express microarray data sets cataloged at GEO. We compared the edge sets of the GRNs to edge sets of Arabidopsis co-expression networks generated from the AtGen Express microarray data and to the edge set belonging to the Bio-Analytical Resource Arabidopsis PPI. Furthermore, we evaluated the topology of all three types of networks for graph theoretical coefficients unique to topological features related to the propagation of information throughout the underlying biological organization. We report that the use of graph theoretical analyses demonstrates that the structure of gene regulatory networks differs from the structure of both co-expression and PPI networks.

Top