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Article: Finding motifs from all sequences with and without binding sites

TitleFinding motifs from all sequences with and without binding sites
Authors
Issue Date2006
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2006, v. 22 n. 18, p. 2217-2223 How to Cite?
AbstractMotivation: Finding common patterns, motifs, from a set of promoter regions of coregulated genes is an important problem in molecular biology. Most existing motif-finding algorithms consider a set of sequences bound by the transcription factor as the only input. However, we can get better results by considering sequences that are not bound by the transcription factor as an additional input. Results: First, instead of using the simple hyper-geometric analysis, we propose to calculate the likelihood based on a more precise probabilistic analysis which considers motif length, sequence length and number of binding sites as input parameters for testing whether motif is found. Second, we adopt an heuristic algorithm bases on our analysis to find motifs. For the simulated and real datasets, our algorithm ALSE compares favorably against common motif-finding programs such as SeedSearch and MEME in all cases and performs very well, especially when each input sequence contains more than one binding site. © 2006 Oxford University Press.
Persistent Identifierhttp://hdl.handle.net/10722/88894
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, HCMen_HK
dc.contributor.authorChin, FYLen_HK
dc.date.accessioned2010-09-06T09:49:48Z-
dc.date.available2010-09-06T09:49:48Z-
dc.date.issued2006en_HK
dc.identifier.citationBioinformatics, 2006, v. 22 n. 18, p. 2217-2223en_HK
dc.identifier.issn1367-4803en_HK
dc.identifier.urihttp://hdl.handle.net/10722/88894-
dc.description.abstractMotivation: Finding common patterns, motifs, from a set of promoter regions of coregulated genes is an important problem in molecular biology. Most existing motif-finding algorithms consider a set of sequences bound by the transcription factor as the only input. However, we can get better results by considering sequences that are not bound by the transcription factor as an additional input. Results: First, instead of using the simple hyper-geometric analysis, we propose to calculate the likelihood based on a more precise probabilistic analysis which considers motif length, sequence length and number of binding sites as input parameters for testing whether motif is found. Second, we adopt an heuristic algorithm bases on our analysis to find motifs. For the simulated and real datasets, our algorithm ALSE compares favorably against common motif-finding programs such as SeedSearch and MEME in all cases and performs very well, especially when each input sequence contains more than one binding site. © 2006 Oxford University Press.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/en_HK
dc.relation.ispartofBioinformaticsen_HK
dc.rightsBioinformatics. Copyright © Oxford University Press.en_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshAmino Acid Motifsen_HK
dc.subject.meshBase Sequenceen_HK
dc.subject.meshBinding Sitesen_HK
dc.subject.meshModels, Statisticalen_HK
dc.subject.meshMolecular Sequence Dataen_HK
dc.subject.meshPromoter Regions, Genetic - geneticsen_HK
dc.subject.meshProtein Bindingen_HK
dc.subject.meshSequence Alignment - methodsen_HK
dc.subject.meshSequence Analysis, DNA - methodsen_HK
dc.subject.meshTranscription Factors - chemistry - genetics - metabolismen_HK
dc.titleFinding motifs from all sequences with and without binding sitesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1367-4803&volume=22&issue=18&spage=2217&epage=2223&date=2006&atitle=Finding+motifs+from+all+sequences+with+and+without+binding+sitesen_HK
dc.identifier.emailLeung, HCM:cmleung2@cs.hku.hken_HK
dc.identifier.emailChin, FYL:chin@cs.hku.hken_HK
dc.identifier.authorityLeung, HCM=rp00144en_HK
dc.identifier.authorityChin, FYL=rp00105en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/bioinformatics/btl371en_HK
dc.identifier.pmid16870937-
dc.identifier.scopuseid_2-s2.0-33748711021en_HK
dc.identifier.hkuros126291en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33748711021&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume22en_HK
dc.identifier.issue18en_HK
dc.identifier.spage2217en_HK
dc.identifier.epage2223en_HK
dc.identifier.isiWOS:000240588800007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLeung, HCM=35233742700en_HK
dc.identifier.scopusauthoridChin, FYL=7005101915en_HK
dc.identifier.citeulike780941-

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