Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

Handle URI:
http://hdl.handle.net/10754/597027
Title:
Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data
Authors:
Wong, Ka-Chun; Peng, Chengbin ( 0000-0002-7445-2638 ) ; Li, Yue
Abstract:
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. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data 2016:1 IEEE Transactions on Cybernetics
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Cybernetics
Issue Date:
2-Feb-2016
DOI:
10.1109/TCYB.2016.2519380
Type:
Article
ISSN:
2168-2267; 2168-2275
Sponsors:
This work was supported in part by the City University of Hong Kong under Project 7200444/CS, and in part by the Amazon Web Service Research Grant.
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7396941
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWong, Ka-Chunen
dc.contributor.authorPeng, Chengbinen
dc.contributor.authorLi, Yueen
dc.date.accessioned2016-02-23T14:29:16Zen
dc.date.available2016-02-23T14:29:16Zen
dc.date.issued2016-02-02en
dc.identifier.citationEvolving Transcription Factor Binding Site Models From Protein Binding Microarray Data 2016:1 IEEE Transactions on Cyberneticsen
dc.identifier.issn2168-2267en
dc.identifier.issn2168-2275en
dc.identifier.doi10.1109/TCYB.2016.2519380en
dc.identifier.urihttp://hdl.handle.net/10754/597027en
dc.description.abstractProtein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.en
dc.description.sponsorshipThis work was supported in part by the City University of Hong Kong under Project 7200444/CS, and in part by the Amazon Web Service Research Grant.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7396941en
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.en
dc.subjectGenetic algorithmen
dc.subjectmotif discoveryen
dc.subjectprotein binding microarray (PBM)en
dc.subjecttranscription factor (TF) binding siteen
dc.titleEvolving Transcription Factor Binding Site Models From Protein Binding Microarray Dataen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Cyberneticsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Hong Kongen
dc.contributor.institutionComputer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology, Boston, MA 02139 USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorPeng, Chengbinen
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