by Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjolander, Rebecca C. Underwood, David Haussler
ftp://ftp.cse.ucsc.edu/pub/rna/cpm94.ps.Z
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Abstract:
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. Tools for analyzing RNA will become increasingly important as in vitro evolution and selection techniques produce greater numbers of synthesized RNA families to supplement those related by phylogeny. Recent efforts have applied stochastic context-free grammars (SCFGs) to the problems of statistical modeling, multiple alignment, discrimination and prediction of the secondary structure of RNA families. Our approach in applying SCFGs to modeling RNA is highly related to our work on modeling protein families and domains with HMMs
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