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Nucleic Acids Research, 1994, Vol. 22, No. 23 5112-5120
© 1994

COMPUTATIONAL BIOLOGY

Stochastic context-free grammers for tRNA modeling

Yasubumi Sakakibara+, Michael Brown, Richard Hughey1, I. Saira Mian2, Kimmen Sjölander, Rebecca C. Underwood and David Haussler*

Computer and Information Sciences Santa Cruz, CA 95064, USA 1Computer Engineering Santa Cruz, CA 95064, USA 2Sinsheimer Laboratories, University of California Santa Cruz, CA 95064, USA

*To whom correspondence shoul be addressed

Received April 29, 1994. Revised October 12, 1994. Accepted October 13, 1994.

Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results show that after having been trained on as few as 20 tRNA sequences from only two tRNA subfamilies (mitochondrial and cytoplasmic), the model can discern general tRNA from similar-length RNA sequences of other kinds, can find secondary structure of new tRNA sequences, and can produce multiple alignments of large sets of tRNA sequences. Our results suggest potential improvements in the alignments of the D- and T-domains in some mitochdondrial tRNAs that cannot be fit into the canonical secondary structure.

+Present address: ISIS, Fujitsu Labs Ltd., 140, Miyamoto, Numazu, shizuoka 410-03, Japan


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