CartograTree: Enabling Landscape Genomics for Forest Trees

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1 Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, Connecticut, United States
2 Department of Plant Sciences, University of California, Davis, Davis, California, United States
3 Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, Tennessee, United States
DOI
10.7287/peerj.preprints.2345v1
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
Herndon N, Grau ES, Batra I, Demurjian Jr. SA, Vasquez-Gross HA, Staton ME, Wegrzyn JL. 2016. CartograTree: Enabling Landscape Genomics for Forest Trees. PeerJ Preprints 4:e2345v1

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.