Title :
Simple alignment constraints for efficient alignment of RNA sequences using family-specific models
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
In this extended abstract, we present a simple method for finding alignment constraints that can be used for efficient alignment of RNAs based on family-specific models, such as profile context-sensitive HMMs (profile-csHMMs) and covariance models (CMs). The alignment constraints are established based on the alignment positions predicted by a profile-HMM. Application of the proposed constraints to the profile-csHMM based structural RNA alignment method significantly improved the average alignment speed without degrading the alignment accuracy.
Keywords :
covariance analysis; hidden Markov models; macromolecules; molecular biophysics; organic compounds; RNA sequence; covariance model; family-specific model; hidden Markov model; profile context-sensitive HMM; structural RNA alignment method; Collision mitigation; Computational efficiency; Context modeling; Databases; Degradation; Hidden Markov models; Prediction algorithms; Predictive models; RNA; Tin;
Conference_Titel :
Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop on
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-4761-9
Electronic_ISBN :
978-1-4244-4762-6
DOI :
10.1109/GENSIPS.2009.5174323
