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Nucleic Acids Research, 1994, Vol. 22, No. 11 2079-2088
© 1994


COMPUTATIONAL BIOLOGY

RNA sequence analysis using covariance models

Sean R. Eddy* and Richard Durbin

MRC Laboratory of Molecular Biology Hills Road, Cambridge CB2 2QH, UK

*To whom correspondence should be addressed

Received February 16, 1994. Revised April 26, 1994. Accepted April 26, 1994.

We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models ‘covariance models’. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA scondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences.


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