EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics
| Type | Article | ||||||||||||
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| Date | 2015-01 | ||||||||||||
| Language | English | ||||||||||||
| Author(s) | Kivela Mikko1, 2, Arnaud-Haond Sophie 3, Saramaki Jari2 |
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| Affiliation(s) | 1 : Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, UK 2 : Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, Helsinki, Finland 3 : Ifremer, UMR «Ecosystèmes Marins Exploités», Sète Cedex, France |
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| Source | Molecular Ecology Resources (1755-098X), 2015-01 , Vol. 15 , N. 1 , P. 117-122 | ||||||||||||
| DOI | 10.1111/1755-0998.12290 | ||||||||||||
| WOS© Times Cited | 81 | ||||||||||||
| Keyword(s) | biogeography, biological communities, graph theory, microbial ecology, network analysis, population genetics | ||||||||||||
| Abstract | The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data. | ||||||||||||
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