DocumentCode :
2466233
Title :
Accelerated Non-coding RNA Searches with Covariance Model Approximations
Author :
Smith, Scott F.
Author_Institution :
Boise State Univ., Boise
fYear :
0
fDate :
0-0 0
Firstpage :
2728
Lastpage :
2733
Abstract :
Covariance models (CMs) are a very sensitive tool for finding non-coding RNA (ncRNA) genes in DNA sequence data. However, CMs are extremely slow. One reason why CMs are so slow is that they allow all possible combinations of insertions and deletions relative to the consensus model even though the vast majority of these are never seen in practice. In this paper we examine reduction in the number of states in covariance models. A simplified CM with reduced states which can be scored much faster is introduced. A comparison of the results of a full CM versus a reduced-state model found using a genetic algorithm is given for the let7 ncRNA family.
Keywords :
approximation theory; biology computing; covariance analysis; macromolecules; DNA sequence data; covariance model approximation; noncoding RNA gene; Acceleration; Biological system modeling; Collision mitigation; Databases; Frequency estimation; Genetic algorithms; Hidden Markov models; Proteins; RNA; Sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
Type :
conf
DOI :
10.1109/CEC.2006.1688650
Filename :
1688650
Link To Document :
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