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Complementing the Eukaryotic Protein Interactome
Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interacti...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3688968/ https://www.ncbi.nlm.nih.gov/pubmed/23825550 http://dx.doi.org/10.1371/journal.pone.0066635 |
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author | Pesch, Robert Zimmer, Ralf |
author_facet | Pesch, Robert Zimmer, Ralf |
author_sort | Pesch, Robert |
collection | PubMed |
description | Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels. Availability: http://services.bio.ifi.lmu.de/coin-db/. |
format | Online Article Text |
id | pubmed-3688968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36889682013-07-02 Complementing the Eukaryotic Protein Interactome Pesch, Robert Zimmer, Ralf PLoS One Research Article Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels. Availability: http://services.bio.ifi.lmu.de/coin-db/. Public Library of Science 2013-06-18 /pmc/articles/PMC3688968/ /pubmed/23825550 http://dx.doi.org/10.1371/journal.pone.0066635 Text en © 2013 Pesch and Zimmer http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Pesch, Robert Zimmer, Ralf Complementing the Eukaryotic Protein Interactome |
title | Complementing the Eukaryotic Protein Interactome |
title_full | Complementing the Eukaryotic Protein Interactome |
title_fullStr | Complementing the Eukaryotic Protein Interactome |
title_full_unstemmed | Complementing the Eukaryotic Protein Interactome |
title_short | Complementing the Eukaryotic Protein Interactome |
title_sort | complementing the eukaryotic protein interactome |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3688968/ https://www.ncbi.nlm.nih.gov/pubmed/23825550 http://dx.doi.org/10.1371/journal.pone.0066635 |
work_keys_str_mv | AT peschrobert complementingtheeukaryoticproteininteractome AT zimmerralf complementingtheeukaryoticproteininteractome |