title: In search of the small ones: improved prediction of short exons in vertebrates, plants, fungi and protists.
abstract: BACKGROUND
  Prediction of the coding potential for stretches of DNA is crucial in gene calling and genome annotation, where it is used to identify potential exons and to position their boundaries in conjunction with functional sites, such as splice sites and translation initiation sites. The ability to discriminate between coding and non-coding sequences relates to the structure of coding sequences, which are organized in codons, and by their biased usage. For statistical reasons, the longer the sequences, the easier it is to detect this codon bias. However, in many eukaryotic genomes, where genes harbour many introns, both introns and exons might be small and hard to distinguish based on coding potential.
  
  
  RESULTS
  Here, we present novel approaches that specifically aim at a better detection of coding potential in short sequences. The methods use complementary sequence features, combined with identification of which features are relevant in discriminating between coding and non-coding sequences. These newly developed methods are evaluated on different species, representative of four major eukaryotic kingdoms, and extensively compared to state-of-the-art Markov models, which are often used for predicting coding potential. The main conclusions drawn from our analyses are that (1) combining complementary sequence features clearly outperforms current Markov models for coding potential prediction in short sequence fragments, (2) coding potential prediction benefits from length-specific models, and these models are not necessarily the same for different sequence lengths and (3) comparing the results across several species indicates that, although our combined method consistently performs extremely well, there are important differences across genomes.
  
  
  METHODS
  http://bioinformatics.psb.ugent.be/.
authors.affiliation: Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be
authors.forename: Yvan
authors.initials: Y
authors.lastname: Saeys
authors.forename: Pierre
authors.initials: P
authors.lastname: Rouzé
authors.forename: Yves
authors.initials: Y
authors.lastname: Van de Peer
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001483:Base Sequence; D004269:DNA, Bacterial / Q000235:genetics*; D004271:DNA, Fungal / Q000235:genetics*; D018744:DNA, Plant / Q000235:genetics*; D005091:Exons / Q000235:genetics*; D008969:Molecular Sequence Data; D016366:Open Reading Frames / Q000235:genetics; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D014714:Vertebrates / Q000235:genetics*
chemical_list: D004269:DNA, Bacterial; D004271:DNA, Fungal; D018744:DNA, Plant