Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities
1 Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA
2 Chemical and Biological Engineering Department, Iowa State University, Ames, Iowa, USA
3 Electrical and Computer Engineering Department, Iowa State University, Ames, Iowa, USA
BMC Bioinformatics 2011, 12:233 doi:10.1186/1471-2105-12-233Published: 13 June 2011
Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element.
This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called
The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on E. coli gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions.