Loading…
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...
Saved in:
Published in: | PeerJ preprints 2016-09 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | |
container_issue | |
container_start_page | |
container_title | PeerJ preprints |
container_volume | |
creator | Herndon, Nic Grau, Emily S Batra, Iman Demurjian, Steven A Vasquez-Gross, Hans A Staton, Margaret E Wegrzyn, Jill L |
description | 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. |
doi_str_mv | 10.7287/peerj.preprints.2345v4 |
format | article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_1949648039</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1949648039</sourcerecordid><originalsourceid>FETCH-LOGICAL-p764-8e085809aff5877151f684a8445bd95c5358a642ab5f6b5e3df045a25d80e7693</originalsourceid><addsrcrecordid>eNotjUtLxDAURoMgOIzzF6TgujWvm8fspIwPGHDT_ZC2N6WlpjHp-Put6OLwbQ7fIeSB0Upzo58iYpqqmDCmMay54kLCt7whO86ULq2R4o4ccp4opYyD4truyLF2aV2G5JqEeCxOwbXzGIZidqHPnYtYDBiWz7HLhV_SL5jXYt3kfE9uvZszHv53T5qXU1O_leeP1_f6-VxGrWRpkBow1DrvwWjNgHllpDNSQttb6ECAcUpy14JXLaDoPZXgOPSGolZW7Mnj321My9d1q1-m5ZrCVrwwK62ShgorfgB4dktS</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1949648039</pqid></control><display><type>article</type><title>CartograTree: Enabling landscape genomics for forest trees</title><source>Publicly Available Content Database</source><creator>Herndon, Nic ; Grau, Emily S ; Batra, Iman ; Demurjian, Steven A ; Vasquez-Gross, Hans A ; Staton, Margaret E ; Wegrzyn, Jill L</creator><creatorcontrib>Herndon, Nic ; Grau, Emily S ; Batra, Iman ; Demurjian, Steven A ; Vasquez-Gross, Hans A ; Staton, Margaret E ; Wegrzyn, Jill L</creatorcontrib><description>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.</description><identifier>EISSN: 2167-9843</identifier><identifier>DOI: 10.7287/peerj.preprints.2345v4</identifier><language>eng</language><publisher>San Diego: PeerJ, Inc</publisher><subject>Gene mapping ; Population structure ; Researchers ; Trees</subject><ispartof>PeerJ preprints, 2016-09</ispartof><rights>2016 Herndon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “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. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1949648039?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25731,27901,27902,36989,44566</link.rule.ids></links><search><creatorcontrib>Herndon, Nic</creatorcontrib><creatorcontrib>Grau, Emily S</creatorcontrib><creatorcontrib>Batra, Iman</creatorcontrib><creatorcontrib>Demurjian, Steven A</creatorcontrib><creatorcontrib>Vasquez-Gross, Hans A</creatorcontrib><creatorcontrib>Staton, Margaret E</creatorcontrib><creatorcontrib>Wegrzyn, Jill L</creatorcontrib><title>CartograTree: Enabling landscape genomics for forest trees</title><title>PeerJ preprints</title><description>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.</description><subject>Gene mapping</subject><subject>Population structure</subject><subject>Researchers</subject><subject>Trees</subject><issn>2167-9843</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNotjUtLxDAURoMgOIzzF6TgujWvm8fspIwPGHDT_ZC2N6WlpjHp-Put6OLwbQ7fIeSB0Upzo58iYpqqmDCmMay54kLCt7whO86ULq2R4o4ccp4opYyD4truyLF2aV2G5JqEeCxOwbXzGIZidqHPnYtYDBiWz7HLhV_SL5jXYt3kfE9uvZszHv53T5qXU1O_leeP1_f6-VxGrWRpkBow1DrvwWjNgHllpDNSQttb6ECAcUpy14JXLaDoPZXgOPSGolZW7Mnj321My9d1q1-m5ZrCVrwwK62ShgorfgB4dktS</recordid><startdate>20160920</startdate><enddate>20160920</enddate><creator>Herndon, Nic</creator><creator>Grau, Emily S</creator><creator>Batra, Iman</creator><creator>Demurjian, Steven A</creator><creator>Vasquez-Gross, Hans A</creator><creator>Staton, Margaret E</creator><creator>Wegrzyn, Jill L</creator><general>PeerJ, Inc</general><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20160920</creationdate><title>CartograTree: Enabling landscape genomics for forest trees</title><author>Herndon, Nic ; Grau, Emily S ; Batra, Iman ; Demurjian, Steven A ; Vasquez-Gross, Hans A ; Staton, Margaret E ; Wegrzyn, Jill L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p764-8e085809aff5877151f684a8445bd95c5358a642ab5f6b5e3df045a25d80e7693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Gene mapping</topic><topic>Population structure</topic><topic>Researchers</topic><topic>Trees</topic><toplevel>online_resources</toplevel><creatorcontrib>Herndon, Nic</creatorcontrib><creatorcontrib>Grau, Emily S</creatorcontrib><creatorcontrib>Batra, Iman</creatorcontrib><creatorcontrib>Demurjian, Steven A</creatorcontrib><creatorcontrib>Vasquez-Gross, Hans A</creatorcontrib><creatorcontrib>Staton, Margaret E</creatorcontrib><creatorcontrib>Wegrzyn, Jill L</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Biological Sciences</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>PeerJ preprints</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Herndon, Nic</au><au>Grau, Emily S</au><au>Batra, Iman</au><au>Demurjian, Steven A</au><au>Vasquez-Gross, Hans A</au><au>Staton, Margaret E</au><au>Wegrzyn, Jill L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CartograTree: Enabling landscape genomics for forest trees</atitle><jtitle>PeerJ preprints</jtitle><date>2016-09-20</date><risdate>2016</risdate><eissn>2167-9843</eissn><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.</abstract><cop>San Diego</cop><pub>PeerJ, Inc</pub><doi>10.7287/peerj.preprints.2345v4</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2167-9843 |
ispartof | PeerJ preprints, 2016-09 |
issn | 2167-9843 |
language | eng |
recordid | cdi_proquest_journals_1949648039 |
source | Publicly Available Content Database |
subjects | Gene mapping Population structure Researchers Trees |
title | CartograTree: Enabling landscape genomics for forest trees |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T11%3A52%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=CartograTree:%20Enabling%20landscape%20genomics%20for%20forest%20trees&rft.jtitle=PeerJ%20preprints&rft.au=Herndon,%20Nic&rft.date=2016-09-20&rft.eissn=2167-9843&rft_id=info:doi/10.7287/peerj.preprints.2345v4&rft_dat=%3Cproquest%3E1949648039%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p764-8e085809aff5877151f684a8445bd95c5358a642ab5f6b5e3df045a25d80e7693%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1949648039&rft_id=info:pmid/&rfr_iscdi=true |