FN Archimer Export Format
PT J
TI EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics
BT 
AF KIVELA, Mikko
   ARNAUD-HAOND, Sophie
   SARAMAKI, Jari
AS 1:1,2;2:3;3:2;
FF 1:;2:PDG-RBE-HM-RHSETE;3:;
C1 Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, UK
   Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, Helsinki, Finland
   Ifremer, UMR «Ecosystèmes Marins Exploités», Sète Cedex, France
C2 UNIV OXFORD, UK
   UNIV AALTO, FINLAND
   IFREMER, FRANCE
SI SETE
SE PDG-RBE-HM-RHSETE
UM MARBEC
IN WOS Ifremer jusqu'en 2018
   copubli-europe
IF 5.298
TC 81
UR https://archimer.ifremer.fr/doc/00197/30853/29217.pdf
LA English
DT Article
DE ;biogeography;biological communities;graph theory;microbial ecology;network analysis;population genetics
AB 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.
PY 2015
PD JAN
SO Molecular Ecology Resources
SN 1755-098X
VL 15
IS 1
UT 000346699100012
BP 117
EP 122
DI 10.1111/1755-0998.12290
ID 30853
ER

EF