GenGIS 2: Geospatial analysis of traditional and genetic biodiversity, with new gradient algorithms and an extensible plugin framework
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Abstract
GenGIS is free and open source software designed to integrate biodiversity data with a digital map and information about geography and habitat. While originally developed with microbial community analyses and phylogeography in mind, GenGIS has been applied to a wide range of datasets. A key feature of GenGIS is the ability to test geographic axes that can correspond to routes of migration or gradients that influence community similarity. Here we introduce GenGIS version 2, which extends the linear gradient tests introduced in the first version to allow comprehensive testing of all possible linear geographic axes. GenGIS v2 also includes a new plugin framework that supports the development and use of graphically driven analysis packages: initial plugins include implementations of linear regression and the Mantel test, calculations of alpha-diversity (e.g., Shannon Index) for all samples, and geographic visualizations of dissimilarity matrices. We have also implemented a recently published method for biomonitoring reference condition analysis (RCA), which compares observed species richness and diversity to predicted values to determine whether a given site has been impacted. The newest version of GenGIS supports vector data in addition to raster files. We demonstrate the new features of GenGIS by performing a full gradient analysis of an Australian kangaroo apple data set, by using plugins and embedded statistical commands to analyze human microbiome sample data, and by applying RCA to a set of samples from Atlantic Canada.GenGIS release versions, tutorials and documentation are freely available at http://kiwi.cs.dal.ca/GenGIS, and source code is available at https://github.com/beiko-lab/gengis.
Cite this as
2013. GenGIS 2: Geospatial analysis of traditional and genetic biodiversity, with new gradient algorithms and an extensible plugin framework. PeerJ PrePrints 1:e15v1 https://doi.org/10.7287/peerj.preprints.15v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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Competing Interests
We have no competing interests.
Author Contributions
Robert G Beiko conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Donovan H Parks conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Timothy Mankowski contributed reagents/materials/analysis tools.
Somayyeh Zangooei contributed reagents/materials/analysis tools.
Michael S Porter contributed reagents/materials/analysis tools.
David G Armanini conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Donald J Baird conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Morgan G I Langille conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Data Deposition
The following information was supplied regarding the deposition of related data:
Datasets and scripts are available at the GenGIS website (http://kiwi.cs.dal.ca/GenGIS)
Funding
D.H.P. is supported by the Killam Trusts and Natural Sciences and Engineering Research Council of Canada; T.M., S. Z., and M.S.P. are supported by Genome Canada and the Ontario Genomics Institute; D. G. A. was supported by an Environment Canada contract; D. J. B. is supported by Environment Canada. M. G. I. L. is supported by the Canadian Institutes of Health Research. R.G.B. is supported by Genome Atlantic, the Canada Foundation for Innovation, and the Canada Research Chairs program. This project was funded by the Government of Canada through Genome Canada and the Ontario Genomics Institute through the Biomonitoring 2.0 project (OGI-050: see http://biomonitoring2.org) and grant number 2009-OGI-ABC-1405. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
