title: Identifying potential tRNA genes in genomic DNA sequences.
abstract: We have developed an algorithm that automatically and reproducibly identifies potential tRNA genes in genomic DNA sequences, and we present a general strategy for testing the sensitivity of such algorithms. This algorithm is useful for the flagging and characterization of long genomic sequences that have not been experimentally analyzed for identification of functional regions, and for the scanning of nucleotide sequence databases for errors in the sequences and the functional assignments associated with them. In an exhaustive scan of the GenBank database, 97.5% of the 744 known tRNA genes were correctly identified (true-positives), and 42 previously unidentified sequences were predicted to be tRNAs. A detailed analysis of these latter predictions reveals that 16 of the 42 are very similar to known tRNA genes, and we predict that they do, in fact, code for tRNA, yielding a false-positive rate for the algorithm of 0.003%. The new algorithm and testing strategy are a considerable improvement over any previously described strategies for recognizing tRNA genes, and they allow detections of genes (including introns) embedded in long genomic sequences.
authors.affiliation: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, NM 87545.
authors.forename: G A
authors.initials: GA
authors.lastname: Fichant
authors.forename: C
authors.initials: C
authors.lastname: Burks
mesh_terms: D000465:Algorithms; D000818:Animals; D000926:Anticodon / Q000235:genetics; D001483:Base Sequence; D004247:DNA / Q000235:genetics*; D005796:Genes; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012343:RNA, Transfer / Q000235:genetics*
chemical_list: D000926:Anticodon; D004247:DNA; D012343:RNA, Transfer