Extending TCGA queries to automatically identify analogous genomic data from dbGaP

Data sharing is critical to advance genomic research by reducing the demand to collect new data by reusing and combining existing data and by promoting reproducible research. The Cancer Genome Atlas (TCGA) is a popular resource for individual-level genotype-phenotype cancer related data. The Databas...

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Autores principales: Wagner, Erin K., Raje, Satyajeet, Amos, Liz, Kurata, Jessica, Badve, Abhijit S., Li, Yingquan, Busby, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2017
Materias:
Software Tool Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5538035/
https://www.ncbi.nlm.nih.gov/pubmed/28794857
http://dx.doi.org/10.12688/f1000research.9837.1
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author Wagner, Erin K.
Raje, Satyajeet
Amos, Liz
Kurata, Jessica
Badve, Abhijit S.
Li, Yingquan
Busby, Ben
author_facet Wagner, Erin K.
Raje, Satyajeet
Amos, Liz
Kurata, Jessica
Badve, Abhijit S.
Li, Yingquan
Busby, Ben
author_sort Wagner, Erin K.
collection PubMed
description Data sharing is critical to advance genomic research by reducing the demand to collect new data by reusing and combining existing data and by promoting reproducible research. The Cancer Genome Atlas (TCGA) is a popular resource for individual-level genotype-phenotype cancer related data. The Database of Genotypes and Phenotypes (dbGaP) contains many datasets similar to those in TCGA. We have created a software pipeline that will allow researchers to discover relevant genomic data from dbGaP, based on matching TCGA metadata. The resulting research provides an easy to use tool to connect these two data sources.
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spelling pubmed-55380352017-08-08 Extending TCGA queries to automatically identify analogous genomic data from dbGaP Wagner, Erin K. Raje, Satyajeet Amos, Liz Kurata, Jessica Badve, Abhijit S. Li, Yingquan Busby, Ben F1000Res Software Tool Article Data sharing is critical to advance genomic research by reducing the demand to collect new data by reusing and combining existing data and by promoting reproducible research. The Cancer Genome Atlas (TCGA) is a popular resource for individual-level genotype-phenotype cancer related data. The Database of Genotypes and Phenotypes (dbGaP) contains many datasets similar to those in TCGA. We have created a software pipeline that will allow researchers to discover relevant genomic data from dbGaP, based on matching TCGA metadata. The resulting research provides an easy to use tool to connect these two data sources. F1000Research 2017-03-24 /pmc/articles/PMC5538035/ /pubmed/28794857 http://dx.doi.org/10.12688/f1000research.9837.1 Text en Copyright: © 2017 Wagner EK et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions.
spellingShingle Software Tool Article
Wagner, Erin K.
Raje, Satyajeet
Amos, Liz
Kurata, Jessica
Badve, Abhijit S.
Li, Yingquan
Busby, Ben
Extending TCGA queries to automatically identify analogous genomic data from dbGaP
title Extending TCGA queries to automatically identify analogous genomic data from dbGaP
title_full Extending TCGA queries to automatically identify analogous genomic data from dbGaP
title_fullStr Extending TCGA queries to automatically identify analogous genomic data from dbGaP
title_full_unstemmed Extending TCGA queries to automatically identify analogous genomic data from dbGaP
title_short Extending TCGA queries to automatically identify analogous genomic data from dbGaP
title_sort extending tcga queries to automatically identify analogous genomic data from dbgap
topic Software Tool Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5538035/
https://www.ncbi.nlm.nih.gov/pubmed/28794857
http://dx.doi.org/10.12688/f1000research.9837.1
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