Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars

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.

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