A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.
Identifieur interne : 001557 ( Ncbi/Curation ); précédent : 001556; suivant : 001558A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.
Auteurs : Ka-Chun Wong ; Yue Li ; Chengbin Peng ; Hau-San WongSource :
- IEEE/ACM transactions on computational biology and bioinformatics [ 1557-9964 ]
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA, Transcription Factors.
- chemical , metabolism : DNA, Transcription Factors.
- methods : Computational Biology, Protein Array Analysis.
- Algorithms, Binding Sites, Chromatin Immunoprecipitation, Nucleotide Motifs, Protein Binding.
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 = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA 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 if choosing 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.
DOI: 10.1109/TCBB.2015.2443782
PubMed: 27045826
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<author><name sortKey="Li, Yue" sort="Li, Yue" uniqKey="Li Y" first="Yue" last="Li">Yue Li</name>
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<author><name sortKey="Peng, Chengbin" sort="Peng, Chengbin" uniqKey="Peng C" first="Chengbin" last="Peng">Chengbin Peng</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Binding Sites</term>
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<term>DNA (chemistry)</term>
<term>DNA (metabolism)</term>
<term>Nucleotide Motifs</term>
<term>Protein Array Analysis (methods)</term>
<term>Protein Binding</term>
<term>Transcription Factors (chemistry)</term>
<term>Transcription Factors (metabolism)</term>
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<term>ADN (métabolisme)</term>
<term>Algorithmes</term>
<term>Analyse par réseau de protéines ()</term>
<term>Biologie informatique ()</term>
<term>Facteurs de transcription ()</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Immunoprécipitation de la chromatine</term>
<term>Liaison aux protéines</term>
<term>Motifs nucléotidiques</term>
<term>Sites de fixation</term>
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<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term>
<term>Transcription Factors</term>
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<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA</term>
<term>Transcription Factors</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
<term>Protein Array Analysis</term>
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<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>ADN</term>
<term>Facteurs de transcription</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Binding Sites</term>
<term>Chromatin Immunoprecipitation</term>
<term>Nucleotide Motifs</term>
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<term>Algorithmes</term>
<term>Analyse par réseau de protéines</term>
<term>Biologie informatique</term>
<term>Facteurs de transcription</term>
<term>Immunoprécipitation de la chromatine</term>
<term>Liaison aux protéines</term>
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<front><div type="abstract" xml:lang="en">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 = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA 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 if choosing 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.</div>
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