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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/10713

Title: Computational reconstruction of transcriptional relationships from ChIP-Chip data
Authors: Le, Ngoc Tu
Ho, Tu Bao
Ho, Bich Hai
Keywords: bioinformatics
transcriptional relationship
Issue Date: 2012-08-01
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume: 10
Number: 2
Start page: 300
End page: 307
DOI: 10.1109/TCBB.2012.102
Abstract: 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 an unbiased method to reconstruct a Bayesian network-based model representing functional relationships among various transcriptional components. Independently, 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, TFIIBTBP, Pol II-H3K36Me3, H3K4Me3-H3K14Ac, etc. Moreover, it can distinguish co-locating 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. Also, the proposed method can be extended to reconstruct more accurate model as new data become available.
Rights: This is the author's version of the work. Copyright (C) 2012 IEEE. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(2), 2012, pp.300-307. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/10713
Material Type: author
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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