GeNOSA: Inferring and experimentally supporting quantitative gene regulatory networks in prokaryotes

Yi Hsiung Chen, Chi Dung Yang, Ching-Ping Tseng, Hsien Da Huang, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Motivation: The establishment of quantitative gene regulatory networks (qGRNs) through existing network component analysis (NCA) approaches suffers from shortcomings such as usage limitations of problem constraints and the instability of inferred qGRNs. The proposed GeNOSA framework uses a global optimization algorithm (OptNCA) to cope with the stringent limitations of NCA approaches in large-scale qGRNs. Results: OptNCA performs well against existing NCA-derived algorithms in terms of utilization of connectivity information and reconstruction accuracy of inferred GRNs using synthetic and real Escherichia coli datasets. For comparisons with other non-NCA-derived algorithms, OptNCA without using known qualitative regulations is also evaluated in terms of qualitative assessments using a synthetic Saccharomyces cerevisiae dataset of the DREAM3 challenges. We successfully demonstrate GeNOSA in several applications including deducing condition-dependent regulations, establishing high-consensus qGRNs and validating a sub-network experimentally for dose-response and time-course microarray data, and discovering and experimentally confirming a novel regulation of CRP on AscG. Availability and implementation: All datasets and the GeNOSA framework are freely available from http://e045.life.nctu.edu.tw/GeNOSA.

Original languageEnglish
Pages (from-to)2151-2158
Number of pages8
JournalBioinformatics
Volume31
Issue number13
DOIs
StatePublished - 1 Jul 2015

Keywords

  • TRANSCRIPTION FACTOR ACTIVITIES; COMPONENT ANALYSIS; ESCHERICHIA-COLI; MICROARRAY DATA; OPTIMIZATION; RECONSTRUCTION; IDENTIFICATION; EXPRESSION; INFERENCE; ALGORITHM

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