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.2345v4
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
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:e2345v4

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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