CartograTree: Enabling landscape genomics for forest trees
- Published
- Accepted
- Subject Areas
- Bioinformatics, Data Mining and Machine Learning, Databases
- Keywords
- open data, genotype, CartograTree, phenotype, association mapping, open-source software, landscape genomics, environment, GIS, forest trees
- Copyright
- © 2016 Herndon 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. CartograTree: Enabling landscape genomics for forest trees. PeerJ Preprints 4:e2345v4 https://doi.org/10.7287/peerj.preprints.2345v4
Abstract
Forest trees cover just over 30% of the earth's surface and are studied by researchers around the world for both their conservation and economic value. With the onset of high throughput technologies, tremendous phenotypic and genomic data sets have been generated for hundreds of species. These long-lived and immobile individuals serve as ideal models to assess population structure and adaptation to environment. Despite the availability of comprehensive data, researchers are challenged to integrate genotype, phenotype, and environment in one place. Towards this goal, CartograTree was designed and implemented as an open repository and open-source analytic framework for genomic, phenotypic, and environmental data for forest trees. One of its key components, the integration of geospatial data, allows the display of environmental layers and acquisition of environmental metrics relative to the positions of georeferenced individuals. Currently, CartograTree uses the Google Maps API to load environmental data. Limitations inherent to this API are driving new development with a focus on functionality to provide efficient queries of numerous environmental metrics.
Author Comment
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