GEOME, A web-based landscape genomics platform for the integrated analysis of spatial environmental and molecular data

GEOME is a web-based platform for the combined analysis of spatial environmental and molecular data. It implements tools for population genetics and spatial analysis. It has been developed to provide objective criteria for optimal biodiversity conservation measures. Although resource conservation managers increasingly have to use geo-environmental information and spatial analysis approaches, they do not have the tools and are not trained to do so. The web component of GEOME allows centralized service and technical support, encouraging the emergence of a community of users. GEOME therefore supplies a unique service of decision support based on free environmental data and open-source technologies.

GEOME, a web-based platform for an integrated analysis of spatial environmental and molecular data

Pauline Emery

2012

GEOME: Towards an integrated web-based landscape genomics platform

Stéphane Joost, Michaël Kalbermatten

The aim of the GEOME project is to develop a WebGIS-based platform for the integrated analysis of environmental, ecological and molecular data through the implementation of an original set of combined databases, spatial analysis and population genetics tools, in a High Performance Computing context. GEOME will support tasks of landscape ecologists and resource conservation managers who increasingly have to use geo-referenced data but are not trained to efficiently use Geographical Information Systems together with appropriate geoenvironmental information and spatial analysis approaches. Preliminary developments (three first applications) are already available here: http://lasigpc8.epfl.ch/geome/

Instituto Politécnico de Bragança2010

GEOME : A web-based landscape genomics geocomputation platform

Stéphane Joost, Michaël Kalbermatten

Landscape ecologists and resource conservation managers increasingly need geo-referenced data but are not trained to efficiently use Geographical Information Systems together with appropriate geo-environmental information and spatial analysis approaches. In the present context of rapid global climate change, they show a renewed interest to study adaptation in species of interest (wildlife, livestock, plants) with the help of landscape genomics. This emerging research field is at the interface of genome sciences, environmental resources analysis, bioinformatics, geocomputation and spatial statistics. Their combination permits to assess the level of association between specific genomic regions of living organisms and environmental factors, to better understand the genetic basis of local adaptation. Landscape genomics is thus able to provide objective criteria to prioritize species which are the most worthwhile preserving. In livestock science for instance, husbandry based on adapted breeds is of key importance to emerging countries. Consequently, to facilitate the use of landscape genomics, the GEOME project proposes a WebGIS-based platform for the integrated analysis of environmental, ecological and molecular data through the implementation of an original set of combined geocomputation, databases, spatial analysis and population genetics tools. The platform will gather two existing software to identify genomic regions under selection (BayeScan and MatSAM) using a WebGIS solution named MapIntera. The latter is a multiple views exploratory visualization interface allowing users to interactively browse and query multiple dynamic layouts (e.g. graphs, maps). It is based on a SVG graphical interface coupled to Javascript interactivity and AJAX technology for the retrieval of information from a spatial database. The platform will be connected to a High Performance Computing infrastructure, able to handle and process very large genome and environmental data sets

International Society for Photogrammetry and Remote Sensing2010

GEOME : A web-based landscape genomics geocomputation platform

Stéphane Joost, Michaël Kalbermatten

International Society for Photogrammetry and Remote Sensing2010