Paper Info
Title
Detecting LTR structures in human genomic sequences using profile hidden Markov models
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.
Year
DOI
Venue
2009
10.1016/j.eswa.2007.10.045
Expert Syst. Appl.
Keywords
Field
DocType
ltr instance,human genome,analyzing ltr sequence,proposed experimental approach,human genomic,ltr fingerprint,ltr,hmm profile,repeats,transposable elements,genome,markov model,novel ltrs,detecting ltr structure,hidden markov model,known ltrs,prediction model,long terminal repeat,transposable element
Genome,Data mining,Computer science,Transposable element,Computational biology,Human genome,Bioinformatics,Hidden Markov model,Retrotransposon,Long terminal repeat
Journal
Volume
Issue
ISSN
36
1
Expert Systems With Applications
Citations 
PageRank 
References 
1
0.35
5
Authors
5
Name
Order
Citations
PageRank
Li-Ching Wu1155.03
Hsien-Da Huang283563.83
Yu-Chung Chang3486.83
Ying-Chun Lee410.35
Jorng-Tzong Horng554167.78