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Detecting LTR structures in human genomic sequences using profile hidden Markov models

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Title: Detecting LTR structures in human genomic sequences using profile hidden Markov models
Authors: Wu, Li-Ching1, Huang, Hsien-Da2, Chang, Yu-Chung3, Lee, Ying-Chun4, Horng, Jorng-Tzong1,4 horng@db.csie.ncu.edu.tw
Source: Expert Systems with Applications. Jan2009, Vol. 36 Issue 1, p668-674. 7p.
Subjects: Human genome, Markov processes, Material plasticity, Transposons, Genomics, Genetic regulation
Abstract: More 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. [Copyright &y& Elsevier]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 34892999
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Detecting LTR structures in human genomic sequences using profile hidden Markov models
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  Data: <searchLink fieldCode="DE" term="%22Human+genome%22">Human genome</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Material+plasticity%22">Material plasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Transposons%22">Transposons</searchLink><br /><searchLink fieldCode="DE" term="%22Genomics%22">Genomics</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+regulation%22">Genetic regulation</searchLink>
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  Data: More 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. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.eswa.2007.10.045
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      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 668
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      – SubjectFull: Human genome
        Type: general
      – SubjectFull: Markov processes
        Type: general
      – SubjectFull: Material plasticity
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      – SubjectFull: Transposons
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      – SubjectFull: Genomics
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      – SubjectFull: Genetic regulation
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      – TitleFull: Detecting LTR structures in human genomic sequences using profile hidden Markov models
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            NameFull: Wu, Li-Ching
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            NameFull: Huang, Hsien-Da
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            NameFull: Chang, Yu-Chung
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            NameFull: Horng, Jorng-Tzong
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              Text: Jan2009
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              Y: 2009
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