Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data

Liu, Qi, Yang, Yi, Li, Yixue and Zhang, Zili 2011, Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data, Current protein and peptide science, vol. 12, no. 7, pp. 631-642.

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Title Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data
Author(s) Liu, Qi
Yang, Yi
Li, Yixue
Zhang, Zili
Journal name Current protein and peptide science
Volume number 12
Issue number 7
Start page 631
End page 642
Total pages 12
Publisher Bentham Science Publishers
Place of publication Hilversum, Netherlands
Publication date 2011-11
ISSN 1389-2037
1875-5550
Keyword(s) ChIP-chip data
expression data
transcriptional regulatory networks
Summary Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2011
Copyright notice ©2011, Bentham Science Publishers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30045598

Document type: Journal Article
Collection: School of Information Technology
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