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Recent methods for RNA modeling using stochastic context-free grammars

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Combinatorial Pattern Matching (CPM 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 807))

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Abstract

Stochastic context-free grammars (SCFGs) can be applied to the problems of folding, aligning and modeling families 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. This paper discusses our new algorithm, Tree-Grammar EM, for deducing SCFG parameters automatically from unaligned, unfolded training sequences. Tree-Grammar EM, a generalization of the HMM forward-backward algorithm, is based on tree grammars and is faster than the previously proposed inside-outside SCFG training algorithm. Independently, Sean Eddy and Richard Durbin have introduced a trainable “covariance model” (CM) to perform similar tasks. We compare and contrast our methods with theirs.

We thank Anders Krogh, Harry Noller and Bryn Weiser for discussions and assistance, and Michael Waterman and David Searls for discussions. This work was supported by NSF grants CDA-9115268 and IRI-9123692 and NIH grant number GM17129. This material is based upon work supported under a National Science Foundation Graduate Research Fellowship.

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Author information

Author notes

  1. Yasubumi Sakakibara

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

Authors and Affiliations

  1. Computer and Information Sciences, University of California, 95064, Santa Cruz, CA, USA

    Yasubumi Sakakibara, Michael Brown, Kimmen Sjölander, Rebecca C. Underwood & David Haussler

  2. Computer Engineering, University of California, 95064, Santa Cruz, CA, USA

    Richard Hughey

  3. Sinsheimer Laboratories, University of California, 95064, Santa Cruz, CA, USA

    Saira Mian

Authors
  1. Yasubumi Sakakibara
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  2. Michael Brown
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  3. Richard Hughey
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  4. Saira Mian
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  5. Kimmen Sjölander
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  6. Rebecca C. Underwood
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  7. David Haussler
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Maxime Crochemore Dan Gusfield

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© 1994 Springer-Verlag Berlin Heidelberg

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Sakakibara, Y. et al. (1994). Recent methods for RNA modeling using stochastic context-free grammars. In: Crochemore, M., Gusfield, D. (eds) Combinatorial Pattern Matching. CPM 1994. Lecture Notes in Computer Science, vol 807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58094-8_25

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  • DOI: https://doi.org/10.1007/3-540-58094-8_25

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