Abstract
BackgroundAmbiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars. Several types of analysis are invalidated by the presence of ambiguity. As this problem inherits undecidability (as we show here) from the namely problem for context free languages, there is no complete algorithmic solution to the problem of ambiguity checking.ResultsWe explain frequently observed sources of ambiguity, and show how to avoid them. We suggest four testing procedures that may help to detect ambiguity when present, including a just-in-time test that permits to work safely with a potentially ambiguous grammar. We introduce, for the special case of stochastic context free grammars and RNA structure modeling, an automated partial procedure for proving non-ambiguity. It is used to demonstrate non-ambiguity for several relevant grammars.ConclusionOur mechanical proof procedure and our testing methods provide a powerful arsenal of methods to ensure non-ambiguity.
Highlights
Ambiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars
Our mechanical proof procedure and our testing methods provide a powerful arsenal of methods to ensure non-ambiguity
We have presented testing methods and a partial proof procedure to analyze the semantic ambiguity of SCFGs
Summary
Ambiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars. Several types of analysis are invalidated by the presence of ambiguity As this problem inherits undecidability (as we show here) from the namely problem for context free languages, there is no complete algorithmic solution to the problem of ambiguity checking. The ambiguity problem in biosequence analysis Biosequence analysis problems are typically optimization problems – we seek the best alignment of two protein sequences under a similarity score, or the most stable secondary structure of an RNA molecule under a thermodynamic model. In such a problem, there is a "good" and a "bad" type of ambiguity. In striving for avoidance of ambiguity, we want to get rid of the bad type and retain the good
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