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Local versus global biological network alignment

Motivation: Network alignment (NA) aims to find regions of similarities between species’ molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and p...

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Autores principales: Meng, Lei, Striegel, Aaron, Milenković, Tijana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048063/
https://www.ncbi.nlm.nih.gov/pubmed/27357169
http://dx.doi.org/10.1093/bioinformatics/btw348
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author Meng, Lei
Striegel, Aaron
Milenković, Tijana
author_facet Meng, Lei
Striegel, Aaron
Milenković, Tijana
author_sort Meng, Lei
collection PubMed
description Motivation: Network alignment (NA) aims to find regions of similarities between species’ molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories. Results: We introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation. Availability and implementation: Software: http://www.nd.edu/~cone/LNA_GNA Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-50480632016-10-05 Local versus global biological network alignment Meng, Lei Striegel, Aaron Milenković, Tijana Bioinformatics Original Paper Motivation: Network alignment (NA) aims to find regions of similarities between species’ molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories. Results: We introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation. Availability and implementation: Software: http://www.nd.edu/~cone/LNA_GNA Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-10-15 2016-06-29 /pmc/articles/PMC5048063/ /pubmed/27357169 http://dx.doi.org/10.1093/bioinformatics/btw348 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Paper
Meng, Lei
Striegel, Aaron
Milenković, Tijana
Local versus global biological network alignment
title Local versus global biological network alignment
title_full Local versus global biological network alignment
title_fullStr Local versus global biological network alignment
title_full_unstemmed Local versus global biological network alignment
title_short Local versus global biological network alignment
title_sort local versus global biological network alignment
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048063/
https://www.ncbi.nlm.nih.gov/pubmed/27357169
http://dx.doi.org/10.1093/bioinformatics/btw348
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