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dc.contributor.authorWu, Li-Chingen_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.contributor.authorChang, Yu-Chungen_US
dc.contributor.authorLee, Ying-Chunen_US
dc.contributor.authorHorng, Jorng-Tzongen_US
dc.date.accessioned2014-12-08T15:10:26Z-
dc.date.available2014-12-08T15:10:26Z-
dc.date.issued2009-01-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2007.10.045en_US
dc.identifier.urihttp://hdl.handle.net/11536/7966-
dc.description.abstractMore than 45% of human genome has been annotated as transposable elements (TEs). The human genome is expanded by the mobilization of these TEs, which they may increase the plasticity and variation of the genome. Long terminal repeat (LTR) retrotransposons are important components in TEs. LTRs include regulatory sites, which the authors believe could be conserved in evolution. Therefore, these significant motifs in the sequence of LTRs are found and are used to train a Hidden Markov Model. These models are used as fingerprints to detect most of the known LTRs detected by RepeatMasker. LTR instances are classified into families using the predictive models proposed. These LTRs can support evolutionary analysis. A new method of detecting LTR is proposed. Analyzing LTR sequences reveals some specific motifs as LTR fingerprints, which can be built into HMM profiles. Experimental results reveal that the proposed experimental approach not only discovers most of the LTRs found by RepeatMasker, but also detects some novel LTRs. Moreover, the novel LTRs may be structurally incomplete or degenerate. (C) 2008 Published by Elsevier Ltd.en_US
dc.language.isoen_USen_US
dc.subjectGenomeen_US
dc.subjectHidden Markov modelen_US
dc.subjectLTRen_US
dc.subjectRepeatsen_US
dc.subjectTransposable elementsen_US
dc.titleDetecting LTR structures in human genomic sequences using profile hidden Markov modelsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2007.10.045en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume36en_US
dc.citation.issue1en_US
dc.citation.spage668en_US
dc.citation.epage674en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000264182800066-
dc.citation.woscount3-
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