CartograTree: Enabling Landscape Genomics for Forest Trees

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.

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CartograTree: Enabling landscape genomics for forest trees

10.7287/peerj.preprints.2345v3 ◽

2016 ◽

Author(s):

Nic Herndon ◽

Emily S Grau ◽

Iman Batra ◽

Steven A Demurjian Jr. ◽

Hans A Vasquez-Gross ◽

...

Keyword(s):

Economic Value ◽

Environmental Data ◽

Geospatial Data ◽

Data Sets ◽

Forest Trees ◽

Analytic Framework ◽

Landscape Genomics ◽

Data Limitations ◽

New Development ◽

The World

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.

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CartograTree: Enabling landscape genomics for forest trees

10.7287/peerj.preprints.2345v4 ◽

2016 ◽

Author(s):

Nic Herndon ◽

Emily S Grau ◽

Iman Batra ◽

Steven A Demurjian Jr. ◽

Hans A Vasquez-Gross ◽

...

Keyword(s):

Economic Value ◽

Environmental Data ◽

Geospatial Data ◽

Data Sets ◽

Forest Trees ◽

Analytic Framework ◽

Landscape Genomics ◽

Data Limitations ◽

New Development ◽

The World

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.

Download Full-text

CartograTree: Enabling landscape genomics for forest trees

10.7287/peerj.preprints.2345 ◽

2016 ◽

Author(s):

Nic Herndon ◽

Emily S Grau ◽

Iman Batra ◽

Steven A Demurjian Jr. ◽

Hans A Vasquez-Gross ◽

...

Keyword(s):

Economic Value ◽

Environmental Data ◽

Geospatial Data ◽

Data Sets ◽

Forest Trees ◽

Analytic Framework ◽

Landscape Genomics ◽

Data Limitations ◽

New Development ◽

The World

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.

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Scaling probabilistic models of genetic variation to millions of humans

10.1101/013227 ◽

2014 ◽

Cited By ~ 1

Author(s):

Prem Gopalan ◽

Wei Hao ◽

David M. Blei ◽

John D. Storey

Keyword(s):

Population Genetics ◽

Population Structure ◽

Genetic Variation ◽

Probabilistic Models ◽

Simulated Data ◽

Genomic Data ◽

Human Populations ◽

Data Sets ◽

Complex Population ◽

Simulated Data Sets

One of the major goals of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. To this end, researchers have developed sophisticated statistical methods to capture the complex population structure that underlies observed genotypes in humans, and such methods have been effective for analyzing modestly sized genomic data sets. However, the number of genotyped humans has grown significantly in recent years, and it is accelerating. In aggregate about 1M individuals have been genotyped to date. Analyzing these data will bring us closer to a nearly complete picture of human genetic variation; but existing methods for population genetics analysis do not scale to data of this size. To solve this problem we developed TeraStructure. TeraStructure is a new algorithm to fit Bayesian models of genetic variation in human populations on tera-sample-sized data sets (1012observed genotypes, e.g., 1M individuals at 1M SNPs). It is a principled approach to Bayesian inference that iterates between subsampling locations of the genome and updating an estimate of the latent population structure of the individuals. On data sets of up to 2K individuals, TeraStructure matches the existing state of the art in terms of both speed and accuracy. On simulated data sets of up to 10K individuals, TeraStructure is twice as fast as existing methods and has higher accuracy in recovering the latent population structure. On genomic data simulated at the tera-sample-size scales, TeraStructure continues to be accurate and is the only method that can complete its analysis.

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Minor allele frequency thresholds strongly affect population structure inference with genomic data sets

Molecular Ecology Resources ◽

10.1111/1755-0998.12995 ◽

2019 ◽

Vol 19 (3) ◽

pp. 639-647 ◽

Cited By ~ 77

Author(s):

Ethan Linck ◽

C. J. Battey

Keyword(s):

Population Structure ◽

Allele Frequency ◽

Minor Allele Frequency ◽

Genomic Data ◽

Minor Allele ◽

Data Sets

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A spatial genomic approach identifies time lags and historic barriers to gene flow in a rapidly fragmenting Appalachian landscape

10.1101/777920 ◽

2019 ◽

Author(s):

Thomas A. Maigret ◽

John J. Cox ◽

David W. Weisrock

Keyword(s):

Population Structure ◽

Gene Flow ◽

Genomic Data ◽

Data Sets ◽

Time Lags ◽

Genetic Structuring ◽

Data Set ◽

Landscape Modification ◽

Snp Data ◽

Genetic Patterns

AbstractThe resolution offered by genomic data sets coupled with recently developed spatially informed analyses are allowing researchers to quantify population structure at increasingly fine temporal and spatial scales. However, uncertainties regarding data set size and quality thresholds and the time scale at which barriers to gene flow become detectable have limited both empirical research and conservation measures. Here, we used restriction site associated DNA sequencing to generate a large SNP data set for the copperhead snake (Agkistrodon contortrix) and address the population genomic impacts of recent and widespread landscape modification across an approximately 1000 km2 region of eastern Kentucky. Nonspatial population-based assignment and clustering methods supported little to no population structure. However, using individual-based spatial autocorrelation approaches we found evidence for genetic structuring which closely follows the path of a historic highway which experienced high traffic volumes from ca. 1920 to 1970. We found no similar spatial genomic signatures associated with more recently constructed highways or surface mining activity, though a time lag effect may be responsible for the lack of any emergent spatial genetic patterns. Subsampling of our SNP data set suggested that similar results could be obtained with as few as 250 SNPs, and thresholds for missing data exhibited limited impacts on the spatial patterns we detected outside of very strict or permissive extremes. Our findings highlight the importance of temporal factors in landscape genetics approaches, and suggest the potential advantages of large genomic data sets and fine-scale, spatially-informed approaches for quantifying subtle genetic patterns in temporally complex landscapes.

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A Platform for Oncogenomic Reporting and Interpretation

10.1101/2021.04.13.439667 ◽

2021 ◽

Author(s):

Caralyn Reisle ◽

Laura Williamson ◽

Erin Pleasance ◽

Anna Davies ◽

Brayden Pellegrini ◽

...

Keyword(s):

Knowledge Base ◽

Open Source ◽

Unmet Need ◽

Genomic Data ◽

Molecular Data ◽

Data Sets ◽

Precision Oncology ◽

Analytic Framework ◽

Manual Curation ◽

Rate Limiting

AbstractManual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we created the Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework created to facilitate the interpretation and reporting of somatic variants in cancer. PORI is unique in its integration of reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents one of the first open-source platform alternatives to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 TCGA tumours to the graph knowledge base, revealing that 88.2% have at least one potentially targetable alteration, and making available reports describing select individual samples.

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