title: Noncoding RNA gene detection using comparative sequence analysis.
abstract: BACKGROUND
  Noncoding RNA genes produce transcripts that exert their function without ever producing proteins. Noncoding RNA gene sequences do not have strong statistical signals, unlike protein coding genes. A reliable general purpose computational genefinder for noncoding RNA genes has been elusive.
  
  
  RESULTS
  We describe a comparative sequence analysis algorithm for detecting novel structural RNA genes. The key idea is to test the pattern of substitutions observed in a pairwise alignment of two homologous sequences. A conserved coding region tends to show a pattern of synonymous substitutions, whereas a conserved structural RNA tends to show a pattern of compensatory mutations consistent with some base-paired secondary structure. We formalize this intuition using three probabilistic "pair-grammars": a pair stochastic context free grammar modeling alignments constrained by structural RNA evolution, a pair hidden Markov model modeling alignments constrained by coding sequence evolution, and a pair hidden Markov model modeling a null hypothesis of position-independent evolution. Given an input pairwise sequence alignment (e.g. from a BLASTN comparison of two related genomes) we classify the alignment into the coding, RNA, or null class according to the posterior probability of each class.
  
  
  CONCLUSIONS
  We have implemented this approach as a program, QRNA, which we consider to be a prototype structural noncoding RNA genefinder. Tests suggest that this approach detects noncoding RNA genes with a fair degree of reliability.
authors.affiliation: Howard Hughes Medical Institute and Department of Genetics, Washington University School of Medicine, Saint Louis, Missouri, USA. elena@genetics.wustl.edu
authors.forename: E
authors.initials: E
authors.lastname: Rivas
authors.forename: S R
authors.initials: SR
authors.lastname: Eddy
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D001499:Bayes Theorem; D002107:Caenorhabditis / Q000235:genetics; D017173:Caenorhabditis elegans / Q000235:genetics; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics; D016678:Genome; D016680:Genome, Bacterial; D008957:Models, Genetic; D008969:Molecular Sequence Data; D012329:RNA, Bacterial / Q000235:genetics; D018510:RNA, Helminth / Q000235:genetics; D022661:RNA, Untranslated / Q000235:genetics*; D012485:Salmonella typhi / Q000235:genetics; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D012329:RNA, Bacterial; D018510:RNA, Helminth; D022661:RNA, Untranslated