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

Background: 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. Results: This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over clas...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Yao Fu, Laura R. Jarboe, Julie A. Dickerson
• Keywords: Biometrics, BMCBI 2011, Gene Regulatory Networks, Relevance Score, Target Genes