Global and Threshold-Free Transcriptional Regulatory Networks Reconstruction Through Integrating ChIP-Chip and Expression Data

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.

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