Identification of structured motifs

Huitao Sheng; Kishan Mehrotra; Chilukuri Mohan; Ramesh Raina

doi:10.1109/BIBMW.2009.5332103

Identification of structured motifs

Huitao Sheng, Kishan Mehrotra, Chilukuri Mohan, Ramesh Raina

Research output: Chapter in Book/Entry/Poem › Conference contribution

Abstract

Structured motifs consist of two simpler patterns (half-sites) separated from each other by a gap, with no restriction on the nucleotides that may occur within the gap. This paper proposes a new algorithm to identify structured motifs. First, a simpler motif searching algorithm is used to search for half-sites. Candidate structured motif models are then evaluated, based on the relative frequency of occurrence of half-sites (not attributable to randomness), and the distribution of gap length. Unlike other recent structured motif detection algorithms, the new algorithm does not require the gap length to be prespecified.

Original language	English (US)
Title of host publication	Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Pages	249-253
Number of pages	5
DOIs	https://doi.org/10.1109/BIBMW.2009.5332103
State	Published - 2009
Event	2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 - Washington, DC, United States Duration: Nov 1 2009 → Nov 4 2009

Publication series

Name	Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

Other

Other	2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Country/Territory	United States
City	Washington, DC
Period	11/1/09 → 11/4/09

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

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10.1109/BIBMW.2009.5332103

Cite this

Sheng, H., Mehrotra, K., Mohan, C., & Raina, R. (2009). Identification of structured motifs. In Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 (pp. 249-253). Article 5332103 (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009). https://doi.org/10.1109/BIBMW.2009.5332103

Identification of structured motifs. / Sheng, Huitao; Mehrotra, Kishan; Mohan, Chilukuri et al.
Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009. 2009. p. 249-253 5332103 (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Sheng, H, Mehrotra, K, Mohan, C & Raina, R 2009, Identification of structured motifs. in Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009., 5332103, Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, pp. 249-253, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, Washington, DC, United States, 11/1/09. https://doi.org/10.1109/BIBMW.2009.5332103

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abstract = "Structured motifs consist of two simpler patterns (half-sites) separated from each other by a gap, with no restriction on the nucleotides that may occur within the gap. This paper proposes a new algorithm to identify structured motifs. First, a simpler motif searching algorithm is used to search for half-sites. Candidate structured motif models are then evaluated, based on the relative frequency of occurrence of half-sites (not attributable to randomness), and the distribution of gap length. Unlike other recent structured motif detection algorithms, the new algorithm does not require the gap length to be prespecified.",

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AB - Structured motifs consist of two simpler patterns (half-sites) separated from each other by a gap, with no restriction on the nucleotides that may occur within the gap. This paper proposes a new algorithm to identify structured motifs. First, a simpler motif searching algorithm is used to search for half-sites. Candidate structured motif models are then evaluated, based on the relative frequency of occurrence of half-sites (not attributable to randomness), and the distribution of gap length. Unlike other recent structured motif detection algorithms, the new algorithm does not require the gap length to be prespecified.

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Identification of structured motifs

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