Open computational landscape genetics
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Spatial and Geographic Information Systems
- Keywords
- GIS, Landscape genetics, open-source, geocomputation, population genetics, software, evolutionary biology, spatial statistics
- Copyright
- © 2016 Joost et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Open computational landscape genetics. PeerJ Preprints 4:e1721v2 https://doi.org/10.7287/peerj.preprints.1721v2
Abstract
Geographical Information Systems (GIS) are considered to be applications-led technology. Consequently, geographic information scientists commonly find themselves as guest in host disciplines in order to best exploit spatial analysis tools and methods, appropriately guided by experts in the field. An example is population genetics in evolutionary biology. Genetic information being linked to living organisms can be partially characterized by geographic coordinates. A research field named landscape genetics emerged at the intersection of genetics, environmental and geographic information science. Geocomputation and programming efforts carried out with the help of open sources technologies and dedicated to the analysis of genetic data gather together a key scientific community whose goal is to extract new knowledge from the present data tsunami caused by the advent of high throughput molecular data and of new sources of high resolution environmental data. While the level of sophistication of the population genetics functions included in the analytical frameworks developed until now are cutting-edge, advanced geo-competences are also required to reinforce the spatial side of this discipline. They will be particularly useful in conservation programmes for wildlife preservation, but also in farm animal genetic resources conservation.
Author Comment
Corrections and useful feedbacks provided by reviewers have been included. In particular, a paragraph and a relevant reference were added to describe the role of least-path modelling in landscape genetics.