title: Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics.
abstract: BACKGROUND
  The conservation of sequences between related genomes has long been recognised as an indication of functional significance and recognition of sequence homology is one of the principal approaches used in the annotation of newly sequenced genomes. In the context of recent findings that the number non-coding transcripts in higher organisms is likely to be much higher than previously imagined, discrimination between conserved coding and non-coding sequences is a topic of considerable interest. Additionally, it should be considered desirable to discriminate between coding and non-coding conserved sequences without recourse to the use of sequence similarity searches of protein databases as such approaches exclude the identification of novel conserved proteins without characterized homologs and may be influenced by the presence in databases of sequences which are erroneously annotated as coding.
  
  
  RESULTS
  Here we present a machine learning-based approach for the discrimination of conserved coding sequences. Our method calculates various statistics related to the evolutionary dynamics of two aligned sequences. These features are considered by a Support Vector Machine which designates the alignment coding or non-coding with an associated probability score.
  
  
  CONCLUSIONS
  We show that our approach is both sensitive and accurate with respect to comparable methods and illustrate several situations in which it may be applied, including the identification of conserved coding regions in genome sequences and the discrimination of coding from non-coding cDNA sequences.
authors.affiliation: Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano, Via Celoria 26, 20133 Milano, Italia. matteo.re@unimi.it
authors.forename: Matteo
authors.initials: M
authors.lastname: Rè
authors.forename: Graziano
authors.initials: G
authors.lastname: Pesole
authors.forename: David S
authors.initials: DS
authors.lastname: Horner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D030541:Databases, Genetic; D019143:Evolution, Molecular; D016415:Sequence Alignment / Q000379:methods