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Global and Threshold-free Transcriptional Regulatory Networks Reconstruction through Integrating ChIP-chip and Expression Data.
MedLine Citation:
PMID:  21827425     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Qi Liu; Yi Yang; Yixue Li; Zili Zhang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-8-9
Journal Detail:
Title:  Current protein & peptide science     Volume:  -     ISSN:  1875-5550     ISO Abbreviation:  -     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2011-8-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100960529     Medline TA:  Curr Protein Pept Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn.
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