Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

BMC Bioinformatics

Methodology Article

Yao Fu¹  Julie A Dickerson²  Laura R Jarboe³ 

[1] Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA;Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA;Electrical and Computer Engineering Department, Iowa State University, Ames, Iowa, USA;Chemical and Biological Engineering Department, Iowa State University, Ames, Iowa, USA;

关键词: Mutual Information;    Transcription Factor Activity;    Gene Regulatory Network;    Regulatory Link;    Relevance Score;   

DOI  :  10.1186/1471-2105-12-233

 received in 2010-11-15, accepted in 2011-06-13,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundGene 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.ResultsThis 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 G ene expression and T ranscription factor activity based R elevance N etwork (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network.ConclusionsThe 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.

【 授权许可】

Unknown   
© Fu et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.