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Mol Biol Cell. 2014 Aug 15;25(16):2522-36. doi: 10.1091/mbc.E13-04-0221. Epub 2014 Jun 18.

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.

Author information

1
Cell Biology/Biophysics Unit, European Molecular Biology Laboratory, D-69117 Heidelberg, Germany.
2
Research Department of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom.
3
Department of Molecular Biology and Biochemistry-CIBER de Enfermedades Raras, University of Malaga, Malaga 29071, Spain.
4
Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia.
5
Structural Bioinformatics Group, Spanish National Cancer Research Centre and Spanish National Bioinformatics Institute, 28029 Madrid, Spain.
6
Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.
7
Research Department of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom c.orengo@ucl.ac.uk Jan.Ellenberg@EMBL-Heidelberg.de.
8
Cell Biology/Biophysics Unit, European Molecular Biology Laboratory, D-69117 Heidelberg, Germany c.orengo@ucl.ac.uk Jan.Ellenberg@EMBL-Heidelberg.de.

Abstract

The advent of genome-wide RNA interference (RNAi)-based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function-mitotic chromosome condensation-and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.

PMID:
24943848
PMCID:
PMC4142622
DOI:
10.1091/mbc.E13-04-0221
[Indexed for MEDLINE]
Free PMC Article

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