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This article is part of the supplement: Selected articles from the 4th International Conference on Computational Systems Biology (ISB 2010)

Open Access Report

Regulatory link mapping between organisms

Rachita Sharma*, Patricia A Evans and Virendrakumar C Bhavsar

Author affiliations

Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B5A3, Canada

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Citation and License

BMC Systems Biology 2011, 5(Suppl 1):S4  doi:10.1186/1752-0509-5-S1-S4

Published: 20 June 2011

Abstract

Background

Identification of gene regulatory networks is useful in understanding gene regulation in any organism. Some regulatory network information has already been determined experimentally for model organisms, but much less has been identified for non-model organisms, and the limited amount of gene expression data available for non-model organisms makes inference of regulatory networks difficult.

Results

This paper proposes a method to determine the regulatory links that can be mapped from a model to a non-model organism. Mapping a regulatory network involves mapping the transcription factors and target genes from one genome to another. In the proposed method, Basic Local Alignment Search Tool (BLAST) and InterProScan are used to map the transcription factors, whereas BLAST along with transcription factor binding site motifs and the GALF-P tool are used to map the target genes. Experiments are performed to map the regulatory network data of S. cerevisiae to A. thaliana and analyze the results. Since limited information is available about gene regulatory network links, gene expression data is used to analyze results. A set of rules are defined on the gene expression experiments to identify the predicted regulatory links that are well supported.

Conclusions

Combining transcription factors mapped using BLAST and subfamily classification, together with target genes mapped using BLAST and binding site motifs, produced the best regulatory link predictions. More than two-thirds of these predicted regulatory links that were analyzed using gene expression data have been verified as correctly mapped regulatory links in the target genome.