Optimal Network Alignment with Graphlet Degree Vectors
Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduc...
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doaj-1534fbbb4f4747418b290205f16347b02020-11-25T03:33:53ZengSAGE PublishingCancer Informatics1176-93512010-01-01910.4137/CIN.S4744Optimal Network Alignment with Graphlet Degree VectorsTijana Milenković0Weng Leong Ng1Wayne Hayes2NatašA PržUlj3Department of Computer Science, University of California, Irvine, CA 92697-3435, USA.Department of Computer Science, University of California, Irvine, CA 92697-3435, USA.Department of Mathematics, Imperial College London SW7 2AZ, UK.Department of Computing, Imperial College London SW7 2AZ, UK.Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.https://doi.org/10.4137/CIN.S4744 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tijana Milenković Weng Leong Ng Wayne Hayes NatašA PržUlj |
spellingShingle |
Tijana Milenković Weng Leong Ng Wayne Hayes NatašA PržUlj Optimal Network Alignment with Graphlet Degree Vectors Cancer Informatics |
author_facet |
Tijana Milenković Weng Leong Ng Wayne Hayes NatašA PržUlj |
author_sort |
Tijana Milenković |
title |
Optimal Network Alignment with Graphlet Degree Vectors |
title_short |
Optimal Network Alignment with Graphlet Degree Vectors |
title_full |
Optimal Network Alignment with Graphlet Degree Vectors |
title_fullStr |
Optimal Network Alignment with Graphlet Degree Vectors |
title_full_unstemmed |
Optimal Network Alignment with Graphlet Degree Vectors |
title_sort |
optimal network alignment with graphlet degree vectors |
publisher |
SAGE Publishing |
series |
Cancer Informatics |
issn |
1176-9351 |
publishDate |
2010-01-01 |
description |
Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology. |
url |
https://doi.org/10.4137/CIN.S4744 |
work_keys_str_mv |
AT tijanamilenkovic optimalnetworkalignmentwithgraphletdegreevectors AT wengleongng optimalnetworkalignmentwithgraphletdegreevectors AT waynehayes optimalnetworkalignmentwithgraphletdegreevectors AT natasaprzulj optimalnetworkalignmentwithgraphletdegreevectors |
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