title: IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy.
abstract: BACKGROUND
  Gene prediction is a challenging but crucial part in most genome analysis pipelines. Various methods have evolved that predict genes ab initio on reference sequences or evidence based with the help of additional information, such as RNA-Seq reads or EST libraries. However, none of these strategies is bias-free and one method alone does not necessarily provide a complete set of accurate predictions.
  
  
  RESULTS
  We present IPred (Integrative gene Prediction), a method to integrate ab initio and evidence based gene identifications to complement the advantages of different prediction strategies. IPred builds on the output of gene finders and generates a new combined set of gene identifications, representing the integrated evidence of the single method predictions.
  
  
  CONCLUSIONS
  We evaluate IPred in simulations and real data experiments on Escherichia Coli and human data. We show that IPred improves the prediction accuracy in comparison to single method predictions and to existing methods for prediction combination.
authors.affiliation: Research Group Bioinformatics (NG4), Robert Koch-Institute, Berlin, Germany. zickmannf@rki.de.
authors.forename: Franziska
authors.initials: F
authors.lastname: Zickmann
authors.affiliation: Research Group Bioinformatics (NG4), Robert Koch-Institute, Berlin, Germany. renardb@rki.de.
authors.forename: Bernhard Y
authors.initials: BY
authors.lastname: Renard
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D004926:Escherichia coli; D020224:Expressed Sequence Tags; D016680:Genome, Bacterial; D015894:Genome, Human; D006801:Humans; D017423:Sequence Analysis, RNA; D012984:Software