CartograTree: Enabling Landscape Genomics for Forest Trees
- Published
- Accepted
- Subject Areas
- Bioinformatics, Data Mining and Machine Learning
- Keywords
- phenotype, enotype, forest trees, GIS, association mapping, environment, genomics
- 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:e2345v1 https://doi.org/10.7287/peerj.preprints.2345v1
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 a repository and analytic framework for genomic, phenotypic, and environmental data for forest trees. One of 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.
Author Comment
This is an article intended for the OGRS2016 Collection, Open computational landscape genetics.