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Stochastic context-free grammers for tRNA modeling
Open accessAbstract
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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Cited by
RNA Pseudoknot Prediction in Energy-Based Models
Lyngsø, R. B., & Pedersen, C. N. S. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050862 · Cited by 359
Rivas, E., & Eddy, S. R. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.7.583 · Cited by 279
The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective
Rivas, E. (2013) · RNA Biology · doi:10.4161/rna.24971 · Cited by 57
Metadata
- Authors
- Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sjölander, K., Underwood, R. C., & Haussler, D.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 1994
- Volume
- 22
- Issue
- 23
- Pages
- 5112-5120
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2023206235
- Cited by
- 383
- References
- 0
- FWCI
- 2.33
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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