The Application of Stochastic Context-Free Grammars to Folding, Aligning and Modeling Homologous RNA Sequences (1993)
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BibTeX
@MISC{Sakakibara93theapplication,
author = {Yasubumi Sakakibara and Michael Brown and Richard Hughey and I. Saira Mian and Kimmen Sjölander and Rebecca C. Underwood and David Haussler},
title = {The Application of Stochastic Context-Free Grammars to Folding, Aligning and Modeling Homologous RNA Sequences},
year = {1993}
}
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Abstract
Stochastic context-free grammars (SCFGs) are 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. 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 to do this. The new algorithm, Tree-Grammar EM, based on tree grammars and faster than the previously proposed SCFG inside-outside training algorithm, produced a model that we tested on the transfer RNA (tRNA) family. 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 similarlength RNA sequences of other kinds, can find secondary structure of new tRNA sequences, and c...
