Author: Seemann, Stefan E.; Gorodkin, Jan; Backofen, Rolf
Title: Unifying evolutionary and thermodynamic information for RNA folding of multiple alignments Document date: 2008_10_4
ID: wtvfow2f_4
Snippet: Probabilistic approaches to the problem of simultaneously aligning and folding a set of RNA sequences, e.g. Stemloc (19) and Consan (20) , are usually based on stochastic context-free grammars (SCFG). Unlike the aforementioned Sankoff-like methods where the energy is explicitly reflected in the scoring scheme, these approaches rely purely on statistical learning methods to determine their parameters. A mixed approach is employed by CMfinder (21) .....
Document: Probabilistic approaches to the problem of simultaneously aligning and folding a set of RNA sequences, e.g. Stemloc (19) and Consan (20) , are usually based on stochastic context-free grammars (SCFG). Unlike the aforementioned Sankoff-like methods where the energy is explicitly reflected in the scoring scheme, these approaches rely purely on statistical learning methods to determine their parameters. A mixed approach is employed by CMfinder (21) that implicitly combines energy contributions with an SCFG. As a seed CMfinder uses energetically folded structures from which a covariance model (SCFG) is constructed in successive rounds of optimization. Another approach is SimulFold (22) , which simultaneously infers structures (including pseudoknots), alignments and trees. There are no sequence-dependent energy contributions, but an energy term that depends on the topology of the consensus structure.
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