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Stochastic context-free grammars for tRNA modeling.

Authors
Type
Published Article
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Volume
22
Issue
23
Pages
5112–5112
Source
UCSC Bioengineering biomedical-ucsc
License
Unknown

Abstract

Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results show that after having been trained on as few as 20 tRNA sequences from only two tRNA subfamilies (mitochondrial and cytoplasmic), the model can discern general tRNA from similar-length RNA sequences of other kinds, can find secondary structure of new tRNA sequences, and can produce multiple alignments of large sets of tRNA sequences. Our results suggest potential improvements in the alignments of the D- and T-domains in some mitochondrial tRNAs that cannot be fit into the canonical secondary structure.

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