About

EDENetworks is an easy to use tool for analysing genetic networks. It allows researchers to use the tools from complex networks science to analyse genetic data using a user friendly graphical interface. You can study both individual level or population level networks by giving either a genetic marker file or a distance matrix as an input.

In recent years, complex networks have attracted considerable attention in the scientific community, providing a framework for modelling and analysing a wide range of systems, from statistical physics to ecology or gene expression. The first issues were structural, and the target was to define unifying principles in the web topology of complex systems. Network is a simple construct to understand a complex system, composed of several agents -or nodes- related among them by edges -or links. Since structure affects function, network topology can be used to understand nodes interactions and behaviour in networks, thereby extracting the dynamic of information flow through networks.

Network analysis is a rather new but promising method in ecology and evolution (Bascompte, et al., 2003; Hernández-García, et al., 2007; Proulx, et al., 2005) that have proven useful to analyse population genetics data and unravel ecological and evolutionary process acting at local and regional scales at the level of individuals (Becheler, et al., 2010; Hernandez-Garcia, et al., 2006; Rozenfeld, et al., 2007) and populations (Fortuna, et al., 2009; Rozenfeld, et al., 2008). Ecological networks based on the distance thresholding approach have also been recently proposed to illustrate and analyse relationships between communities (genetic groups) and define biogeographic provinces, based on ecological distances describing their taxonomic composition (Moalic, et al., soumis).

These methods allow an exploratory approach free of a priori assumptions, such as geographic clustering, usually underlying population genetics as well as some ecological data analysis. The tools and indices developed in the framework of network theory also allow unravelling unique properties such as the importance of certain agents (specific individuals, populations or communities) in a population, metapopulation or biogeographic system (Rozenfeld et al., 2008; Becheler et al., accepted; Moalic et al., submitted for publication). Finally, network analysis tools provide graphical representations of the relatedness between agents in a multidimensional space, free of some of the constraints (such as binary branching) compulsory in classical methods based on phylograms or trees describing individuals, populations or communities relationships. EDENetwork provides a tool implementing those new methods through a user friendly interface, allowing a standardized and widespread use of those network analyses for the community of ecologists and population geneticists.

EDENetworks is an open-source program written entirely in Python. The source code is published under GPL2 license and is available at the download section. Windows version also includes graphical installer and the Linux version is distributed in a package suitable for example for Ubuntu linux. In addition to standard Python libraries, EDENetworks uses third party libraries such as Numpy for matrix manipulations, Matplotlib for plotting (BSD type licenses) and Himmeli for layout calculations in network visualizations (GPL license).