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tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence
Open accessAbstract
We describe a program, tRNAscan-SE, which identifies 99-100% of transfer RNA genes in DNA sequence while giving less than one false positive per 15 gigabases. Two previously described tRNA detection programs are used as fast, first-pass prefilters to identify candidate tRNAs, which are then analyzed by a highly selective tRNA covariance model. This work represents a practical application of RNA covariance models, which are general, probabilistic secondary structure profiles based on stochastic context-free grammars. tRNAscan-SE searches at approximately 30 000 bp/s. Additional extensions to tRNAscan-SE detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA-derived repetitive elements and tRNA pseudogenes.
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Metadata
- Authors
- Lowe, T. M., & Eddy, S. R.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-03-01
- Volume
- 25
- Issue
- 5
- Pages
- 955-964
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2149429041
- Cited by
- 10,905
- References
- 1
- FWCI
- 10.24
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.