Stochastic context-free grammars for modeling RNA

Yasubumi Sakakibara, Michael Brown, Rebecca C. Underwood, I. Saira Mian, David Haussler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

Stochastic context-free grammars (SCFGs) are used to fold, align and model a family of homologous RNA 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. The novel aspect of this work is that SCFG parameters are learned automatically from unaligned, unfolded training sequences. A generalization of the HMM forward-backward algorithm is introduced. The new algorithm, based on tree grammars and faster than the previously proposed SCFG inside-outside algorithm, is tested on the transfer RNA (tRNA) family. Results show the model can discern tRNA from similar-length RNA sequences, can find secondary structure of new tRNA sequences, and can give multiple alignments of large sets of tRNA sequences. The model is extended to handle introns in tRNA.

Original languageEnglish
Title of host publicationProceedings of the Hawaii International Conference on System Sciences
EditorsJay F. Nunamaker, Ralph H.Jr. Sprague
PublisherPubl by IEEE
Pages284-293
Number of pages10
ISBN (Print)0818650907
Publication statusPublished - 1995 Jan 1
Externally publishedYes
EventProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5) - Wailea, HI, USA
Duration: 1994 Jan 41994 Jan 7

Publication series

NameProceedings of the Hawaii International Conference on System Sciences
Volume5
ISSN (Print)1060-3425

Other

OtherProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5)
CityWailea, HI, USA
Period94/1/494/1/7

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Sakakibara, Y., Brown, M., Underwood, R. C., Mian, I. S., & Haussler, D. (1995). Stochastic context-free grammars for modeling RNA. In J. F. Nunamaker, & R. H. J. Sprague (Eds.), Proceedings of the Hawaii International Conference on System Sciences (pp. 284-293). (Proceedings of the Hawaii International Conference on System Sciences; Vol. 5). Publ by IEEE.