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A computational framework for nucleic acid sub-sequence identification

Mann, Scott, Chen, Yi-Ping Phoebe and Eaton, Luke 2004, A computational framework for nucleic acid sub-sequence identification, in BIBE 2004: Fourth IEEE Symposium on BioInformatics and BioEngineering proceedings, IEEE, Piscataway, N.J., pp. 467-474.

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Title A computational framework for nucleic acid sub-sequence identification
Author(s) Mann, Scott
Chen, Yi-Ping Phoebe
Eaton, Luke
Conference name IEEE International Symposium on Bioinformatics and Bioengineering (4th : 2004 : Taichung, Taiwan)
Conference location Taichung, Taiwan
Conference dates 19-21 May 2004
Title of proceedings BIBE 2004: Fourth IEEE Symposium on BioInformatics and BioEngineering proceedings
Editor(s) [Unknown]
Publication date 2004
Conference series IEEE International Symposium on Bioinformatics and Bioengineering
Start page 467
End page 474
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) TATA box basal promoter element
scoring optimisations
nucleic acid subsequence identification
generic algorithms
hidden Markov models
Summary Identification of nucleic acid sub-sequences within larger background sequences is a fundamental need of the biology community. The applicability correlates to research studies looking for homologous regions, diagnostic purposes and many other related activities. This paper serves to detail the approaches taken leading to sub-sequence identification through the use of hidden Markov models and associated scoring optimisations. The investigation of techniques for locating conserved basal promoter elements correlates to promoter thus gene identification techniques. The case study centred on the TATA box basal promoter element, as such the background is a gene sequence with the TATA box the target. Outcomes from the research conducted, highlights generic algorithms for sub-sequence identification, as such these generic processes can be transposed to any case study where identification of a target sequence is required. Paths extending from the work conducted in this investigation have led to the development of a generic framework for the future applicability of hidden Markov models to biological sequence analysis in a computational context.
ISBN 0769521738
9780769521732
Language eng
Field of Research 080399 Computer Software not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005265

Document type: Conference Paper
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