FunSiP : A Modular and Extensible Classifier for the Prediction of Functional Sites in DNA

  • Authors : Michiel Van Bel, Yvan Saeys and Yves Van de Peer
  • Corresponding author :
  • Program description :


    Many problems in genome annotation are tackled by using a classification model to predict functional sites such as splice sites, translation start sites or stop codons. Locating the correct position of these sites remains one of the most important but also one of the most difficult issues in the structural annotation of genomes. Most of the software currently in use is written for a very specific problem, thereby limiting the possibilities for reuse.


    We developed a software platform that uses a very general approach towards the classification of functional sites in DNA sequences. The program uses an ab initio approach towards the identification of these sites, and extends SpliceMachine, a previously developed splice site predictor that shows state-of-the-art performance for both donor and acceptor splice site recognition in the human and Arabidopsis thaliana genome.


    The program is developed as a stand-alone Java application, and is available as GPLv3 open-source software.

  • Additional File 1Zip file of the FunSiP program
    This file is the archive containing the FunSiP program.
  • Additional File 2Manual and documentation of FunSiP
    This file contains the manual and some extra documentation of FunSiP.
  • Additional File 3Zip file containing the source code of FunSiP
    This archive contains all the source code files of FunSiP.
  • Additional File 4Zip file containing pre-built models
    This archive contains some pre-built models (Arabidopsis thaliana, human, Melampsora and tomato), together with the configuration files that were used to create these models, and with configuration files that can be used to evaluate DNA-sequences with these models.
  • Additional File 5Zip file containing training data
    This archive contains the training data files that were used to generate the classification models.

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