title: Automated gene-model curation using global discriminative learning.
abstract: BACKGROUND
  Gene-model curation creates consensus gene models by combining multiple sources of protein-coding evidence that may be incomplete or inconsistent. To date, manual curation still produces the highest quality models. However, manual curation is too slow and costly to be completed even for the most important organisms. In recent years, machine-learned ensemble gene predictors have become a viable alternative to manual curation. Current approaches make use of signal and genomic region consistency among sources and some voting scheme to resolve conflicts in the evidence. As a further step in that direction, we have developed eCRAIG (ensemble CRAIG), an automated curation tool that combines multiple sources of evidence using global discriminative training. This allows efficient integration of different types of genomic evidence with complex statistical dependencies to maximize directly annotation accuracy. Our method goes beyond previous work in integrating novel non-linear annotation agreement features, as well as combinations of intrinsic features of the target sequence and extrinsic annotation features.
  
  
  RESULTS
  We achieved significant improvements over the best ensemble predictors available for Homo sapiens, Caenorhabditis elegans and Arabidopsis thaliana. In particular, eCRAIG achieved a relative mean improvement of 5.1% over Jigsaw, the best published ensemble predictor in all our experiments.
  
  
  BACKGROUND
  The source code and datasets are both available at http://www.seas.upenn.edu/abernal/ecraig.tgz.
authors.affiliation: Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA. abernal@seas.upenn.edu
authors.forename: Axel
authors.initials: A
authors.lastname: Bernal
authors.forename: Koby
authors.initials: K
authors.lastname: Crammer
authors.forename: Fernando
authors.initials: F
authors.lastname: Pereira
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D001185:Artificial Intelligence; D017173:Caenorhabditis elegans / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D023281:Genomics; D006801:Humans; D008957:Models, Genetic