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ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species
BACKGROUND: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, wh...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101090/ https://www.ncbi.nlm.nih.gov/pubmed/30367584 http://dx.doi.org/10.1186/s12859-018-2271-6 |
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author | Gao, Jianliang Song, Bo Hu, Xiaohua Yan, Fengxia Wang, Jianxin |
author_facet | Gao, Jianliang Song, Bo Hu, Xiaohua Yan, Fengxia Wang, Jianxin |
author_sort | Gao, Jianliang |
collection | PubMed |
description | BACKGROUND: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes. RESULTS: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species. CONCLUSIONS: We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species. |
format | Online Article Text |
id | pubmed-6101090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61010902018-08-27 ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species Gao, Jianliang Song, Bo Hu, Xiaohua Yan, Fengxia Wang, Jianxin BMC Bioinformatics Research BACKGROUND: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes. RESULTS: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species. CONCLUSIONS: We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species. BioMed Central 2018-08-13 /pmc/articles/PMC6101090/ /pubmed/30367584 http://dx.doi.org/10.1186/s12859-018-2271-6 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Gao, Jianliang Song, Bo Hu, Xiaohua Yan, Fengxia Wang, Jianxin ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species |
title | ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species |
title_full | ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species |
title_fullStr | ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species |
title_full_unstemmed | ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species |
title_short | ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species |
title_sort | connectedalign: a ppi network alignment method for identifying conserved protein complexes across multiple species |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101090/ https://www.ncbi.nlm.nih.gov/pubmed/30367584 http://dx.doi.org/10.1186/s12859-018-2271-6 |
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