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dc.contributor.authorÓ Broin, Pilib
dc.contributor.authorSmith, Terry J.
dc.contributor.authorGolden, Aaron
dc.date.accessioned2016-08-04T08:25:54Z
dc.date.available2016-08-04T08:25:54Z
dc.date.issued2015-01-28
dc.identifier.citationBroin, PO,Smith, TJ,Golden, AAJ (2015) 'Alignment-free clustering of transcription factor binding motifs using a genetic-k-medoids approach'. BMC Bioinformatics, 16 .en_IE
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/10379/5923
dc.descriptionJournal articleen_IE
dc.description.abstractBackground: Familial binding profiles (FBPs) represent the average binding specificity for a group of structurally related DNA-binding proteins. The construction of such profiles allows the classification of novel motifs based on similarity to known families, can help to reduce redundancy in motif databases and de novo prediction algorithms, and can provide valuable insights into the evolution of binding sites. Many current approaches to automated motif clustering rely on progressive tree-based techniques, and can suffer from so-called frozen sub-alignments, where motifs which are clustered early on in the process remain 'locked' in place despite the potential for better placement at a later stage. In order to avoid this scenario, we have developed a genetic-k-medoids approach which allows motifs to move freely between clusters at any point in the clustering process.Results: We demonstrate the performance of our algorithm, GMACS, on multiple benchmark motif datasets, comparing results obtained with current leading approaches. The first dataset includes 355 position weight matrices from the TRANSFAC database and indicates that the k-mer frequency vector approach used in GMACS outperforms other motif comparison techniques. We then cluster a set of 79 motifs from the JASPAR database previously used in several motif clustering studies and demonstrate that GMACS can produce a higher number of structurally homogeneous clusters than other methods without the need for a large number of singletons. Finally, we show the robustness of our algorithm to noise on multiple synthetic datasets consisting of known motifs convolved with varying degrees of noise.Conclusions: Our proposed algorithm is generally applicable to any DNA or protein motifs, can produce highly stable and biologically meaningful clusters, and, by avoiding the problem of frozen sub-alignments, can provide improved results when compared with existing techniques on benchmark datasets.en_IE
dc.description.sponsorshipScience Foundation Ireland (Grant Number 05/RFP/CMS0001)en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherBioMed Centralen_IE
dc.relation.ispartofBMC Bioinformaticsen
dc.subjectTranscription factoren_IE
dc.subjectMotifen_IE
dc.subjectClusteringen_IE
dc.subjectGenetic algorithmen_IE
dc.subjectPosition frequency matricesen_IE
dc.subjectSequence alignmenten_IE
dc.subjectSitesen_IE
dc.subjectSimilaritiesen_IE
dc.subjectAlgorithmen_IE
dc.subjectProfilesen_IE
dc.subjectDiscoveryen_IE
dc.subjectFamiliesen_IE
dc.subjectDatabaseen_IE
dc.titleAlignment-free clustering of transcription factor binding motifs using a genetic-k-medoids approachen_IE
dc.typeArticleen_IE
dc.date.updated2016-07-27T11:08:02Z
dc.identifier.doi10.1186/s12859-015-0450-2
dc.local.publishedsourcehttp://dx.doi.org/10.1186/s12859-015-0450-2en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|
dc.internal.rssid9392298
dc.local.contactTerry Smith, School Of Natural Sciences, Nui Galway. 2022 Email: terry.smith@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionPUBLISHED
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