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BinAligner: a heuristic method to align biological networks

The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the ev...

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Autores principales: Yang, Jialiang, Li, Jun, Grünewald, Stefan, Wan, Xiu-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851320/
https://www.ncbi.nlm.nih.gov/pubmed/24266981
http://dx.doi.org/10.1186/1471-2105-14-S14-S8
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author Yang, Jialiang
Li, Jun
Grünewald, Stefan
Wan, Xiu-Feng
author_facet Yang, Jialiang
Li, Jun
Grünewald, Stefan
Wan, Xiu-Feng
author_sort Yang, Jialiang
collection PubMed
description The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and infection.
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spelling pubmed-38513202013-12-13 BinAligner: a heuristic method to align biological networks Yang, Jialiang Li, Jun Grünewald, Stefan Wan, Xiu-Feng BMC Bioinformatics Proceedings The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and infection. BioMed Central 2013-10-09 /pmc/articles/PMC3851320/ /pubmed/24266981 http://dx.doi.org/10.1186/1471-2105-14-S14-S8 Text en Copyright © 2013 Yang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Yang, Jialiang
Li, Jun
Grünewald, Stefan
Wan, Xiu-Feng
BinAligner: a heuristic method to align biological networks
title BinAligner: a heuristic method to align biological networks
title_full BinAligner: a heuristic method to align biological networks
title_fullStr BinAligner: a heuristic method to align biological networks
title_full_unstemmed BinAligner: a heuristic method to align biological networks
title_short BinAligner: a heuristic method to align biological networks
title_sort binaligner: a heuristic method to align biological networks
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851320/
https://www.ncbi.nlm.nih.gov/pubmed/24266981
http://dx.doi.org/10.1186/1471-2105-14-S14-S8
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