Open Access Highly Accessed Methodology article

Applying Support Vector Machines for Gene ontology based gene function prediction

Arunachalam Vinayagam1*, Rainer König2, Jutta Moormann13, Falk Schubert2, Roland Eils2, Karl-Heinz Glatting1 and Sándor Suhai1

Author Affiliations

1 Department of Molecular Biophysics, Deutsches Krebsforschungszentrum (DKFZ), TP3, Im Neuenheimer Feld 580, Heidelberg, D-69120, Germany

2 Theoretical Bioinformatics, Deutsches Krebsforschungszentrum (DKFZ), TP3, Im Neuenheimer Feld 580, Heidelberg, D-69120, Germany

3 Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Johannes Gutenberg-Universität Mainz, 55101, Mainz, Germany

For all author emails, please log on.

BMC Bioinformatics 2004, 5:116  doi:10.1186/1471-2105-5-116

Published: 26 August 2004



The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions.


We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was benchmarked with a large dataset. An organism-wise cross-validation was performed to define confidence estimates, resulting in an average precision of 80% for 74% of all test sequences. The validation results show that the prediction performance was organism-independent and could reproduce the annotation of other automated systems as well as high-quality manual annotations. We applied our trained classification system to Xenopus laevis sequences, yielding functional annotation for more than half of the known expressed genome. Compared to the currently available annotation, we provided more than twice the number of contigs with good quality annotation, and additionally we assigned a confidence value to each predicted GO term.


We present a complete automated annotation system that overcomes many of the usual problems by applying a controlled vocabulary of Gene Ontology and an established classification method on large and well-described sequence data sets. In a case study, the function for Xenopus laevis contig sequences was predicted and the results are publicly available at webcite.