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Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation
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 fun...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The American Society for Cell Biology
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142622/ https://www.ncbi.nlm.nih.gov/pubmed/24943848 http://dx.doi.org/10.1091/mbc.E13-04-0221 |
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author | Hériché, Jean-Karim Lees, Jon G. Morilla, Ian Walter, Thomas Petrova, Boryana Roberti, M. Julia Hossain, M. Julius Adler, Priit Fernández, José M. Krallinger, Martin Haering, Christian H. Vilo, Jaak Valencia, Alfonso Ranea, Juan A. Orengo, Christine Ellenberg, Jan |
author_facet | Hériché, Jean-Karim Lees, Jon G. Morilla, Ian Walter, Thomas Petrova, Boryana Roberti, M. Julia Hossain, M. Julius Adler, Priit Fernández, José M. Krallinger, Martin Haering, Christian H. Vilo, Jaak Valencia, Alfonso Ranea, Juan A. Orengo, Christine Ellenberg, Jan |
author_sort | Hériché, Jean-Karim |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4142622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | The American Society for Cell Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-41426222014-10-30 Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation Hériché, Jean-Karim Lees, Jon G. Morilla, Ian Walter, Thomas Petrova, Boryana Roberti, M. Julia Hossain, M. Julius Adler, Priit Fernández, José M. Krallinger, Martin Haering, Christian H. Vilo, Jaak Valencia, Alfonso Ranea, Juan A. Orengo, Christine Ellenberg, Jan Mol Biol Cell Articles 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. The American Society for Cell Biology 2014-08-15 /pmc/articles/PMC4142622/ /pubmed/24943848 http://dx.doi.org/10.1091/mbc.E13-04-0221 Text en © 2014 Hériché et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0). “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society of Cell Biology. |
spellingShingle | Articles Hériché, Jean-Karim Lees, Jon G. Morilla, Ian Walter, Thomas Petrova, Boryana Roberti, M. Julia Hossain, M. Julius Adler, Priit Fernández, José M. Krallinger, Martin Haering, Christian H. Vilo, Jaak Valencia, Alfonso Ranea, Juan A. Orengo, Christine Ellenberg, Jan Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
title | Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
title_full | Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
title_fullStr | Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
title_full_unstemmed | Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
title_short | Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
title_sort | integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142622/ https://www.ncbi.nlm.nih.gov/pubmed/24943848 http://dx.doi.org/10.1091/mbc.E13-04-0221 |
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