tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence

Todd M. Lowe, Sean R. Eddy

Nucleic Acids Research · 1997 · 3.6K citations · 24 references

Concepts

TL;DR

RNA covariance models, probabilistic secondary‑structure profiles based on stochastic context‑free grammars, are applied here to detect tRNA genes. The authors present tRNAscan‑SE, a program that identifies 99–100 % of tRNA genes with fewer than one false positive per 15 Gb of DNA. tRNAscan‑SE first applies two fast prefilter programs to find candidate tRNAs, then refines them with a highly selective covariance model, and includes extensions to detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA‑derived repetitive elements, and tRNA pseudogenes. tRNAscan‑SE achieves 99–100 % detection accuracy with fewer than one false positive per 15 Gb and processes sequences at about 30 000 bp/s.

Abstract

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 ∼30 000 bp/s. Additional extensions to tRNAscan-SE detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA-derived repetitive elements and tRNA pseudo-genes.

References

24