Semantics and Ambiguity of Stochastic RNA Family Models

Stochastic models such as hidden Markov models or stochastic context free grammars can fail to return the correct, maximum likelihood solution in the case of semantic ambiguity. This problem arises when the algorithm implementing the model inspects the same solution in different guises. It is a difficult problem in the sense that proving semantic non-ambiguity has been shown to be algorithmically undecidable, while compensating for it (by coalescing scores of equivalent solutions) has been shown to be NP-hard. For stochastic context free grammars modeling RNA secondary structure, it has been shown that the distortion of results can be quite severe. Much less is known about the case when stochastic context free grammars model the matching of a query sequence to an implicit consensus structure for an RNA family. We find that three different, meaningful semantics can be associated with the matching of a query against the model – a structural, an alignment, and a trace semantics. Rfam...

• Type: Journal
• Year: 2011
• Where: TCBB
• Authors: Robert Giegerich, Christian Höner zu Siederdissen
• Keywords: Context Free Grammars, Semantics, Software Engineering, Stochastic Context, TCBB 2011