Abstract
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.
Keywords: ChIP-chip data, expression data, transcriptional regulatory networks, ChIP, TFs, ChIP-bindings, motif-based searches, GRAM, MOFA
Current Protein & Peptide Science
Title: Global and Threshold-Free Transcriptional Regulatory Networks Reconstruction Through Integrating ChIP-Chip and Expression Data
Volume: 12 Issue: 7
Author(s): Qi Liu, Yi Yang, Yixue Li and Zili Zhang
Affiliation:
Keywords: ChIP-chip data, expression data, transcriptional regulatory networks, ChIP, TFs, ChIP-bindings, motif-based searches, GRAM, MOFA
Abstract: 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.
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Cite this article as:
Liu Qi, Yang Yi, Li Yixue and Zhang Zili, Global and Threshold-Free Transcriptional Regulatory Networks Reconstruction Through Integrating ChIP-Chip and Expression Data, Current Protein & Peptide Science 2011; 12 (7) . https://dx.doi.org/10.2174/1389203711109070631
DOI https://dx.doi.org/10.2174/1389203711109070631 |
Print ISSN 1389-2037 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5550 |
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