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Gene prioritization through genomic data fusion

An Erratum to this article was published on 01 June 2006

Abstract

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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Figure 1: Concept of prioritization by Endeavour.
Figure 2: Cross-validation results.
Figure 3: Cross-validation results.
Figure 4: In vitro functional validation of Endeavour.
Figure 5: Functional validation of Endeavour in zebrafish.

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Acknowledgements

We wish to thank all groups and consortia that made their data freely available: Ensembl, NCBI (EntrezGene and Medline), Gene Ontology, BIND, KEGG, Atlas, InterPro, BioBase, the Disease Probabilities from Lopez-Bigas and Ouzounis9 and the Prospectr scores from Euan Adie8. Ouzounis8 and the Prospectr scores from Euan Adie9. We also thank the following people for their help in particular areas: Robert Vlietinck with the manuscript, Patrick Glenisson with text mining, Joke Allemeersch and Gert Thijs with the order statistics and Camilla Esguerra with the zebrafish experiments. S.A., D.L. and P.V.L. are sponsored by the Research Foundation Flanders (FWO). This work is supported by Flanders Institute for Biotechnology (VIB), Instituut voor de aanmoediging van Innovatie door Wetenschap en Technologie in Vlaanderen (IWT) (STWW-00162), Research Council KULeuven (GOA-Ambiorics, IDO genetic networks), FWO (G.0229.03 and G.0413.03), IUAP V-22, K.U.L. Excellentiefinanciering CoE SymBioSys (EF/05/007), EU NoE Biopattern and EU EST BIOPTRAIN to Y.M., and by the FWO (G.0405.06), GOA/2006/11 and GOA/2001/09, Squibb and EULSHB-CT-2004-503573 to P.C.

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Correspondence to Stein Aerts.

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Supplementary information

Supplementary Fig. 1

Variability of the performance of endeavour, evaluated for each data source. (PDF 44 kb)

Supplementary Fig. 2

Pairwise dependency of the data sources. (PDF 55 kb)

Supplementary Fig. 3

Endeavor is not biased to well-characterized genes. (PDF 318 kb)

Supplementary Table 1

Selection criteria and training sets for the 10 mono and 6 polygenic diseases. (PDF 75 kb)

Supplementary Table 2

Prioritization of 1048 test genes located on chromosome 3 using training genes of congenital heart defects (CHD), arrythmias (AR), and cardiomyopathies (CM). (PDF 93 kb)

Supplementary Methods (PDF 119 kb)

Supplementary Notes (PDF 87 kb)

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Aerts, S., Lambrechts, D., Maity, S. et al. Gene prioritization through genomic data fusion. Nat Biotechnol 24, 537–544 (2006). https://doi.org/10.1038/nbt1203

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