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Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12

Authors :
Trelinski Michael
Stanley Frank
Ensman Lisa
Haidar Alaa
Tuncay Kagan
Sun Jingjun
Ortoleva Peter
Source :
Algorithms for Molecular Biology, Vol 2, Iss 1, p 2 (2007)
Publication Year :
2007
Publisher :
BMC, 2007.

Abstract

Abstract Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.

Details

Language :
English
ISSN :
17487188
Volume :
2
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Algorithms for Molecular Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.766805b0465d40bbb19795e0cf46d9e2
Document Type :
article
Full Text :
https://doi.org/10.1186/1748-7188-2-2