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Efficient prediction of human protein-protein interactions at a global scale
BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a glob...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272565/ https://www.ncbi.nlm.nih.gov/pubmed/25492630 http://dx.doi.org/10.1186/s12859-014-0383-1 |
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author | Schoenrock, Andrew Samanfar, Bahram Pitre, Sylvain Hooshyar, Mohsen Jin, Ke Phillips, Charles A Wang, Hui Phanse, Sadhna Omidi, Katayoun Gui, Yuan Alamgir, Md Wong, Alex Barrenäs, Fredrik Babu, Mohan Benson, Mikael Langston, Michael A Green, James R Dehne, Frank Golshani, Ashkan |
author_facet | Schoenrock, Andrew Samanfar, Bahram Pitre, Sylvain Hooshyar, Mohsen Jin, Ke Phillips, Charles A Wang, Hui Phanse, Sadhna Omidi, Katayoun Gui, Yuan Alamgir, Md Wong, Alex Barrenäs, Fredrik Babu, Mohan Benson, Mikael Langston, Michael A Green, James R Dehne, Frank Golshani, Ashkan |
author_sort | Schoenrock, Andrew |
collection | PubMed |
description | BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. CONCLUSIONS: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4272565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42725652015-01-02 Efficient prediction of human protein-protein interactions at a global scale Schoenrock, Andrew Samanfar, Bahram Pitre, Sylvain Hooshyar, Mohsen Jin, Ke Phillips, Charles A Wang, Hui Phanse, Sadhna Omidi, Katayoun Gui, Yuan Alamgir, Md Wong, Alex Barrenäs, Fredrik Babu, Mohan Benson, Mikael Langston, Michael A Green, James R Dehne, Frank Golshani, Ashkan BMC Bioinformatics Research Article BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. CONCLUSIONS: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-10 /pmc/articles/PMC4272565/ /pubmed/25492630 http://dx.doi.org/10.1186/s12859-014-0383-1 Text en © Schoenrock et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Schoenrock, Andrew Samanfar, Bahram Pitre, Sylvain Hooshyar, Mohsen Jin, Ke Phillips, Charles A Wang, Hui Phanse, Sadhna Omidi, Katayoun Gui, Yuan Alamgir, Md Wong, Alex Barrenäs, Fredrik Babu, Mohan Benson, Mikael Langston, Michael A Green, James R Dehne, Frank Golshani, Ashkan Efficient prediction of human protein-protein interactions at a global scale |
title | Efficient prediction of human protein-protein interactions at a global scale |
title_full | Efficient prediction of human protein-protein interactions at a global scale |
title_fullStr | Efficient prediction of human protein-protein interactions at a global scale |
title_full_unstemmed | Efficient prediction of human protein-protein interactions at a global scale |
title_short | Efficient prediction of human protein-protein interactions at a global scale |
title_sort | efficient prediction of human protein-protein interactions at a global scale |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272565/ https://www.ncbi.nlm.nih.gov/pubmed/25492630 http://dx.doi.org/10.1186/s12859-014-0383-1 |
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