Stochastic context-free grammars for modeling RNA

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

研究成果: Conference contribution

33 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the Hawaii International Conference on System Sciences
編集者Jay F. Nunamaker, Ralph H.Jr. Sprague
出版社Publ by IEEE
ページ284-293
ページ数10
ISBN(印刷版)0818650907
出版ステータスPublished - 1995 1 1
外部発表はい
イベントProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5) - Wailea, HI, USA
継続期間: 1994 1 41994 1 7

出版物シリーズ

名前Proceedings of the Hawaii International Conference on System Sciences
5
ISSN(印刷版)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

  • コンピュータ サイエンス(全般)

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