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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The American Society for Cell Biology 2014
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.
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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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