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    A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

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    Type
    Article
    Authors
    Wong, Ka-Chun
    Li, Yue
    Peng, Chengbin cc
    Wong, Hau-San
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extreme Computing Research Center
    Date
    2015-06-11
    Online Publication Date
    2015-06-11
    Print Publication Date
    2016-03-01
    Permanent link to this record
    http://hdl.handle.net/10754/584252
    
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    Abstract
    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.
    Citation
    A Comparison Study for DNA Motif Modeling on Protein Binding Microarray 2015:1 IEEE/ACM Transactions on Computational Biology and Bioinformatics
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    DOI
    10.1109/TCBB.2015.2443782
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7122289
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCBB.2015.2443782
    Scopus Count
    Collections
    Articles; Extreme Computing Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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