Please use this identifier to cite or link to this item: https://doi.org/10.1089/cmb.2005.12.1137
DC FieldValue
dc.titleQuick, practical selection of effective seeds for homology search
dc.contributor.authorPreparata, F.P.
dc.contributor.authorZhang, L.
dc.contributor.authorChoi, K.P.
dc.date.accessioned2014-10-28T02:52:42Z
dc.date.available2014-10-28T02:52:42Z
dc.date.issued2005-11
dc.identifier.citationPreparata, F.P., Zhang, L., Choi, K.P. (2005-11). Quick, practical selection of effective seeds for homology search. Journal of Computational Biology 12 (9) : 1137-1152. ScholarBank@NUS Repository. https://doi.org/10.1089/cmb.2005.12.1137
dc.identifier.issn10665277
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104699
dc.description.abstractIt has been observed that in homology search gapped seeds have better sensitivity than ungapped ones for the same cost (weight). In this paper, we propose a probability leakage model (a dissipative Markov system) to elucidate the mechanism that confers power to spaced seeds. Based on this model, we identify desirable features of gapped search seeds and formulate an extremely efficient procedure for seed design: it samples from the set of spaced seed exhibiting those features, evaluates their sensitivity, and then selects the best. The sensitivity of the constructed seeds is negligibly less than that of the corresponding known optimal seeds. While the challenging mathematical question of characterizing optimal search seeds remains open, we believe that our eminently efficient and effective approach represents a satisfactory solution from a practitioner's viewpoint. © Mary Ann Liebert, Inc.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1089/cmb.2005.12.1137
dc.sourceScopus
dc.subjectFiltration technique
dc.subjectHomology search
dc.subjectLeakage model
dc.subjectq-gram
dc.subjectSequence alignment
dc.subjectSpaced seeds
dc.typeReview
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1089/cmb.2005.12.1137
dc.description.sourcetitleJournal of Computational Biology
dc.description.volume12
dc.description.issue9
dc.description.page1137-1152
dc.description.codenJCOBE
dc.identifier.isiut000233857000001
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