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Non-coding RNA gene finding with combined partial covariance models

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2 Author(s)
Wenbo Jiang ; Sch. of Comput. Sci., Simon Fraser Univ., Surrey, BC, Canada ; Wiese, K.C.

Covariance models provide excellent accuracy for ncRNA homology search. However, high computational complexity has limited their usefulness. This research improves the covariance model's search efficiency by building combined models for a group of different RNA families, which is selected using a clustering strategy. A series of combined partial covariance models are built from the stem loop structural elements that the ncRNA gene families share. Experimental results suggest that for most RNA gene families investigated, our combination search method successfully provides run time improvement with acceptable accuracy. Although there still exist limitations such as recall loss for a few RNA gene families, this novel combination approach has implications for future studies of reducing covariance model's search complexity.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012 IEEE Symposium on

Date of Conference:

9-12 May 2012