GeNOSA: inferring and experimentally supporting quantitative gene regulatory networks in prokaryotes.
Bioinformatics
; 31(13): 2151-8, 2015 Jul 01.
Article
in En
| MEDLINE
| ID: mdl-25717191
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. CONTACT syho@mail.nctu.edu.tw SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Full text:
1
Database:
MEDLINE
Main subject:
Saccharomyces cerevisiae
/
Algorithms
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Gene Expression Profiling
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Escherichia coli
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Gene Regulatory Networks
Type of study:
Qualitative_research
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2015
Type:
Article
Affiliation country:
China