Bioinformatics Advance Access originally published online on July 24, 2006
Bioinformatics 2006 22(19):2413-2420; doi:10.1093/bioinformatics/btl396
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Inferring gene regulatory networks from multiple microarray datasets
1 Department of Electrical Engineering and Electronics, Osaka Sangyo University Osaka 574-8530, Japan
2 Academy of Mathematics and Systems Science, CAS Beijing 100080, China
3 Computer Science Department and Christopher S. Bond Life Sciences Center, University of Missouri Columbia, MO 65211, USA
4 Institute of Systems Biology, Shanghai University Shanghai 200444, China
*To whom correspondence should be addressed.
Motivation: Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points.
Results: This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. The proposed method is called GNR (Gene Network Reconstruction tool) which is based on linear programming and a decomposition procedure. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR in terms of predicting new gene regulatory relationship in yeast and Arabidopsis.
Availability: The software is available from http://zhangorup.aporc.org/bioinfo/grninfer/, http://digbio.missouri.edu/grninfer/ and http://intelligent.eic.osaka-sandai.ac.jp or upon request from the authors.
Contact: chen{at}eic.osaka-sandai.ac.jp, xudong{at}missouri.edu, zxs{at}amt.ac.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on March 5, 2006; revised on June 25, 2006; accepted on July 17, 2006
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