Issue No. 02 - March-April (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.102
Ngoc Tu Le , Sch. of Knowledge Sci., Japan Adv. Inst. of Sci. & Technol., Nomi, Japan
Tu Bao Ho , Sch. of Knowledge Sci., Japan Adv. Inst. of Sci. & Technol., Nomi, Japan
Bich Hai Ho , Inst. of Inf. Technol., Hanoi, Vietnam
Eukaryotic gene transcription is a complex process, which requires the orchestrated recruitment of a large number of proteins, such as sequence-specific DNA binding factors, chromatin remodelers and modifiers, and general transcription machinery, to regulatory regions. Previous works have shown that these regulatory proteins favor specific organizational theme along promoters. Details about how they cooperatively regulate transcriptional process, however, remain unclear. We developed a method to reconstruct a Bayesian network (BN) model representing functional relationships among various transcriptional components. The positive/negative influence between these components was measured from protein binding and nucleosome occupancy data and embedded into the model. Application on S.cerevisiae ChIP-Chip data showed that the proposed method can recover confirmed relationships, such as Isw1-Pol II, TFIIH-Pol II, TFIIB-TBP, Pol II-H3K36Me3, H3K4Me3-H3K14Ac, etc. Moreover, it can distinguish colocating components from functionally related ones. Novel relationships, e.g., ones between Mediator and chromatin remodeling complexes (CRCs), and the combinatorial regulation of Pol II recruitment and activity by CRCs and general transcription factors (GTFs), were also suggested. Conclusion: protein binding events during transcription positively influence each other. Among contributing components, GTFs and CRCs play pivotal roles in transcriptional regulation. These findings provide insights into the regulatory mechanism.
Bioinformatics, Proteins, Stability analysis, Genomics, Machinery, Bayesian methods, Data models
Ngoc Tu Le, Tu Bao Ho and Bich Hai Ho, "Computational Reconstruction of Transcriptional Relationships from ChIP-Chip Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 300-307, 2013.