Nature Biotechnology 24, 537 - 544 (2006)
Published online: 5 May 2006; | doi:10.1038/nbt1203
There is an Erratum (June 2006) associated with this Analysis.
Gene prioritization through genomic data fusionStein Aerts1, 4, 5, Diether Lambrechts2, 5, Sunit Maity2, 5, Peter Van Loo3, 4, 5, Bert Coessens4, 5, Frederik De Smet2, Leon-Charles Tranchevent4, Bart De Moor4, Peter Marynen3, Bassem Hassan1, Peter Carmeliet2
& Yves Moreau41
Laboratory of Neurogenetics, Department of Human Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Leuven, Herestraat 49, bus 602, 3000 Leuven, Belgium. 2
The Center for Transgene Technology and Gene Therapy, Flanders Interuniversity Institute for Biotechnology (VIB), University of Leuven, Herestraat 49, bus 602, 3000 Leuven, Belgium. 3
Human Genome Laboratory, Department of Human Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Leuven, Herestraat 49, bus 602, 3000 Leuven, Belgium. 4
Bioinformatics Group, Department of Electrical Engineering (ESAT-SCD), University of Leuven, Belgium. 5
These authors contributed equally to this work.
Correspondence should be addressed to Stein Aerts stein.aerts@med.kuleuven.be The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.
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