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

TLDR

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.