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Bioinformatics Advance Access first published online on November 5, 2004
This version published online on November 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti103
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 27, 2004
Revised October 1, 2004
Accepted October 15, 2004

Article

Predicting gene function through systematic analysis and quality assessment of high-throughput data

Patrick Kemmeren 1, Thessa T. J. P. Kockelkorn 1, Theo Bijma 1, Rogier Donders 2, and Frank C. P. Holstege 1*

1 Department of Physiological Chemistry, University Medical Center Utrecht, P.O. box 85060, 3508 AB Utrecht, the Netherlands
2 Department of Innovation Studies, Copernicus Institute, Utrecht University, Utrecht, the Netherlands

* To whom correspondence should be addressed.
Frank C. P. Holstege, E-mail: f.c.p.holstege{at}med.uu.nl


  Abstract

Motivation: Determining gene function is an important challenge arising from the availability of whole genome sequences. Until recently, approaches based on sequence homology were the only high-throughput method for predicting gene function. Use of high-throughput generated experimental datasets for determining gene function has been limited for several reasons.

Results: Here a new approach is presented for integration of high-throughput datasets, leading to prediction of function based on relationships supported by multiple types and sources of data. This is achieved with a database containing 125 different high-throughput datasets describing phenotypes, cellular localizations, protein interactions and mRNA expression levels from S. cerevisiae, using a bit-vector representation and information content based ranking. The approach takes characteristic and qualitative differences between the datasets into account, is highly flexible, efficient and scalable. Database queries result in predictions for 543 uncharacterized genes, based on multiple functional relationships each supported by at least three types of experimental data. Some of these are experimentally verified, further demonstrating their reliability. The results also generate insights into the relative merits of different data types and provide a coherent framework for functional genomic datamining.

Availability: Free availability over the internet.

Supplementary Information: http://www.genomics.med.uu.nl/pub/pk/comb_gen_network.


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