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Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data
journal contribution
posted on 2011-11-01, 00:00 authored by Q Liu, Y Yang, Y Li, Zili ZhangZili ZhangInferring 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.
History
Journal
Current protein and peptide scienceVolume
12Issue
7Pagination
631 - 642Publisher
Bentham Science PublishersLocation
Hilversum, NetherlandsISSN
1389-2037eISSN
1875-5550Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2011, Bentham Science PublishersUsage metrics
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