title: EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance.
abstract: BACKGROUND
  Contrary to other areas of sequence analysis, a measure of statistical significance of a putative gene has not been devised to help in discriminating real genes from the masses of random Open Reading Frames (ORFs) in prokaryotic genomes. Therefore, many genomes have too many short ORFs annotated as genes.
  
  
  RESULTS
  In this paper, we present a new automated gene-finding method, EasyGene, which estimates the statistical significance of a predicted gene. The gene finder is based on a hidden Markov model (HMM) that is automatically estimated for a new genome. Using extensions of similarities in Swiss-Prot, a high quality training set of genes is automatically extracted from the genome and used to estimate the HMM. Putative genes are then scored with the HMM, and based on score and length of an ORF, the statistical significance is calculated. The measure of statistical significance for an ORF is the expected number of ORFs in one megabase of random sequence at the same significance level or better, where the random sequence has the same statistics as the genome in the sense of a third order Markov chain.
  
  
  CONCLUSIONS
  The result is a flexible gene finder whose overall performance matches or exceeds other methods. The entire pipeline of computer processing from the raw input of a genome or set of contigs to a list of putative genes with significance is automated, making it easy to apply EasyGene to newly sequenced organisms. EasyGene with pre-trained models can be accessed at http://www.cbs.dtu.dk/services/EasyGene.
authors.affiliation: Center for Biological Sequence Analysis BioCentrum, Technical University of Denmark Building 208, 2800 Lyngby, Denmark. thsl@novozymes.com
authors.forename: Thomas Schou
authors.initials: TS
authors.lastname: Larsen
authors.forename: Anders
authors.initials: A
authors.lastname: Krogh
mesh_terms: D001412:Bacillus subtilis / Q000235:genetics; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D004926:Escherichia coli / Q000235:genetics; D029968:Escherichia coli Proteins / Q000737:chemistry / Q000235:genetics; D005798:Genes, Bacterial / Q000235:genetics*; D016680:Genome, Bacterial; D016480:Helicobacter pylori / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009169:Mycobacterium tuberculosis / Q000235:genetics; D016366:Open Reading Frames / Q000235:genetics*; D011237:Predictive Value of Tests; D012270:Ribosomes / Q000737:chemistry / Q000235:genetics; D012680:Sensitivity and Specificity
chemical_list: D001426:Bacterial Proteins; D029968:Escherichia coli Proteins