DocumentCode :
2191059
Title :
Combined covariance model for non-coding RNA gene finding
Author :
Jiang, Wenbo ; Wiese, Kay C.
Author_Institution :
Sch. of Comput. Sci., Simon Fraser Univ., Surrey, BC, Canada
fYear :
2011
fDate :
11-15 April 2011
Firstpage :
1
Lastpage :
5
Abstract :
The use of covariance models in finding non-coding RNA gene members in genome sequence databases has been shown quite effective in many studies. However, it has a significant drawback, which is the very large computational burden. A combined covariance model is proposed to reduce the search complexity when a genome sequence is searched for more than one ncRNA gene family. The covariance models that are combined are selected using a hierarchical clustering algorithm. This study shows that when a small number of original covariance models are combined, the combined covariance model can find members from all original ncRNA families thus successfully reducing the search time.
Keywords :
biology computing; genomics; macromolecules; pattern clustering; covariance models; genome sequence databases; hierarchical clustering algorithm; ncRNA families; noncoding RNA gene finding; noncoding RNA gene members; search complexity reduction; Bioinformatics; Computational modeling; Databases; Genomics; Hidden Markov models; Periodic structures; RNA;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2011 IEEE Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9896-3
Type :
conf
DOI :
10.1109/CIBCB.2011.5948474
Filename :
5948474
Link To Document :
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