A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling.

ABSTRACT:

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Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (C

SUBMITTER: Wang Z 

PROVIDER: S-EPMC3117333 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature