Stochastic context-free grammers for tRNA modeling

Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood, David Haussler

Research output: Contribution to journalArticle

288 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)5112-5120
Number of pages9
JournalNucleic acids research
Volume22
Issue number23
DOIs
Publication statusPublished - 1994 Nov 25

ASJC Scopus subject areas

  • Genetics

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    Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sjölander, K., Underwood, R. C., & Haussler, D. (1994). Stochastic context-free grammers for tRNA modeling. Nucleic acids research, 22(23), 5112-5120. https://doi.org/10.1093/nar/22.23.5112