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Given the importance of non-coding RNAs to cellular regulatory functions and rapid growth of RNA transcripts, computational prediction of RNA ter-tiary structure remains highly demanded yet significantly challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly(More)
Stochastic context-free grammar (SCFG) has been successful in modeling biomolecular structures, typically RNA secondary structure, for statistical analysis and structure prediction. Context-free grammar rules specify parallel and nested co-occurrences of terminals, and thus are ideal for modeling nucleotide canonical base pairs that constitute the RNA(More)
We prove that, for many parameterized problems in the class FPT, the existence of polynomial kernels implies the collapse of the W-hierarchy (i.e., W[P] = FPT). The collapsing results are also extended to assumed exponential kernels for problems in the class FPT. In particular , we establish a close relationship between polynomial (and exponential)(More)
MOTIVATION Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort(More)
UNLABELLED TRFolder-W is a web server capable of predicting core structures of telomerase RNA (TR) in yeast genomes. TRFolder is a command-line Python toolkit for TR-specific structure prediction. We developed a web-version built on the django web framework, leveraging the work done previously, to include enhancements to increase flexibility of usage. To(More)
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