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Bioinformatics Advance Access published online on January 3, 2008
Bioinformatics, doi:10.1093/bioinformatics/btm633
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Beyond the "Best" Match: Machine Learning Annotation of Protein Sequences by Integration of Different Sources of Information

Igor V. Tetko 1,*, Igor V. Rodchenkov 1, Mathias C. Walter 1, Thomas Rattei 2 and Hans-Werner Mewes 1,2

1GSF - National1Helmholtz Center Munich - German Research CentreCenter for Environment andEnvironmental Health, Institute for Bioinformatics, (MIPS), Ingolstädter Landstraße 1, Neuherberg, Germany,
2Department of Genome-Oriented Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München 85350 Freising, Germany

*To whom correspondence should be addressed. Dr. Igor V. Tetko, E-mail: i.tekto{at}gsf.de, itekto{at}yahoo.com


  Abstract

Accurate automatic assignment of protein functions remains a challenge for genome annotation. We have developed and compared the automatic annotation of four bacterial genomes employing a 5-fold cross-validation procedure and several machine learning methods. The analyzed genomes were manually annotated with FunCat categories in MIPS providing a gold standard. Features describing a pair of sequences rather than each sequence alone were used. The descriptors were derived from sequence alignment scores, InterPro domains, synteny information, sequence length, and calculated protein properties. Following training we scored all pairs from the validation sets, selected a pair with the highest predicted score and annotated the target protein with functional categories of the prototype protein. The data integration using machine-learning methods provided significantly higher annotation accuracy compared to the use of individual descriptors alone. NeuralThe neural network approach showed the best performance. The descriptors derived from the InterPro domains and sequence similarity provided the highest contribution to the method performance. The predicted annotation scores allow differentiation of reliable vs. non-reliable annotations. The developed approach was applied to annotate the protein sequences from 180 complete bacterial genomes. The FUNcat Annotation Tool (FUNAT) is available on-line as Web Services at http://mips.gsf.de/proj/funat.

Associate Editor: Prof. Burkhard Rost

Received on October 7, 2007; revised on November 29, 2007; accepted on December 18, 2007

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