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CNSA: a data repository for archiving omics data

With the application and development of high-throughput sequencing technology in life and health sciences, massive multi-omics data brings the problem of efficient management and utilization. Database development and biocuration are the prerequisites for the reuse of these big data. Here, relying on...

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Autores principales: Guo, Xueqin, Chen, Fengzhen, Gao, Fei, Li, Ling, Liu, Ke, You, Lijin, Hua, Cong, Yang, Fan, Liu, Wanliang, Peng, Chunhua, Wang, Lina, Yang, Xiaoxia, Zhou, Feiyu, Tong, Jiawei, Cai, Jia, Li, Zhiyong, Wan, Bo, Zhang, Lei, Yang, Tao, Zhang, Minwen, Yang, Linlin, Yang, Yawen, Zeng, Wenjun, Wang, Bo, Wei, Xiaofeng, Xu, Xun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377928/
https://www.ncbi.nlm.nih.gov/pubmed/32705130
http://dx.doi.org/10.1093/database/baaa055
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author Guo, Xueqin
Chen, Fengzhen
Gao, Fei
Li, Ling
Liu, Ke
You, Lijin
Hua, Cong
Yang, Fan
Liu, Wanliang
Peng, Chunhua
Wang, Lina
Yang, Xiaoxia
Zhou, Feiyu
Tong, Jiawei
Cai, Jia
Li, Zhiyong
Wan, Bo
Zhang, Lei
Yang, Tao
Zhang, Minwen
Yang, Linlin
Yang, Yawen
Zeng, Wenjun
Wang, Bo
Wei, Xiaofeng
Xu, Xun
author_facet Guo, Xueqin
Chen, Fengzhen
Gao, Fei
Li, Ling
Liu, Ke
You, Lijin
Hua, Cong
Yang, Fan
Liu, Wanliang
Peng, Chunhua
Wang, Lina
Yang, Xiaoxia
Zhou, Feiyu
Tong, Jiawei
Cai, Jia
Li, Zhiyong
Wan, Bo
Zhang, Lei
Yang, Tao
Zhang, Minwen
Yang, Linlin
Yang, Yawen
Zeng, Wenjun
Wang, Bo
Wei, Xiaofeng
Xu, Xun
author_sort Guo, Xueqin
collection PubMed
description With the application and development of high-throughput sequencing technology in life and health sciences, massive multi-omics data brings the problem of efficient management and utilization. Database development and biocuration are the prerequisites for the reuse of these big data. Here, relying on China National GeneBank (CNGB), we present CNGB Sequence Archive (CNSA) for archiving omics data, including raw sequencing data and its further analyzed results which are organized into six objects, namely Project, Sample, Experiment, Run, Assembly and Variation at present. Moreover, CNSA has created a correlation model of living samples, sample information and analytical data on some projects. Both living samples and analytical data are directly correlated with the sample information. From either one, information or data of the other two can be obtained, so that all data can be traced throughout the life cycle from the living sample to the sample information to the analytical data. Complying with the data standards commonly used in the life sciences, CNSA is committed to building a comprehensive and curated data repository for storing, managing and sharing of omics data. We will continue to improve the data standards and provide free access to open-data resources for worldwide scientific communities to support academic research and the bio-industry. Database URL: https://db.cngb.org/cnsa/.
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spelling pubmed-73779282020-07-28 CNSA: a data repository for archiving omics data Guo, Xueqin Chen, Fengzhen Gao, Fei Li, Ling Liu, Ke You, Lijin Hua, Cong Yang, Fan Liu, Wanliang Peng, Chunhua Wang, Lina Yang, Xiaoxia Zhou, Feiyu Tong, Jiawei Cai, Jia Li, Zhiyong Wan, Bo Zhang, Lei Yang, Tao Zhang, Minwen Yang, Linlin Yang, Yawen Zeng, Wenjun Wang, Bo Wei, Xiaofeng Xu, Xun Database (Oxford) Original Article With the application and development of high-throughput sequencing technology in life and health sciences, massive multi-omics data brings the problem of efficient management and utilization. Database development and biocuration are the prerequisites for the reuse of these big data. Here, relying on China National GeneBank (CNGB), we present CNGB Sequence Archive (CNSA) for archiving omics data, including raw sequencing data and its further analyzed results which are organized into six objects, namely Project, Sample, Experiment, Run, Assembly and Variation at present. Moreover, CNSA has created a correlation model of living samples, sample information and analytical data on some projects. Both living samples and analytical data are directly correlated with the sample information. From either one, information or data of the other two can be obtained, so that all data can be traced throughout the life cycle from the living sample to the sample information to the analytical data. Complying with the data standards commonly used in the life sciences, CNSA is committed to building a comprehensive and curated data repository for storing, managing and sharing of omics data. We will continue to improve the data standards and provide free access to open-data resources for worldwide scientific communities to support academic research and the bio-industry. Database URL: https://db.cngb.org/cnsa/. Oxford University Press 2020-07-23 /pmc/articles/PMC7377928/ /pubmed/32705130 http://dx.doi.org/10.1093/database/baaa055 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Guo, Xueqin
Chen, Fengzhen
Gao, Fei
Li, Ling
Liu, Ke
You, Lijin
Hua, Cong
Yang, Fan
Liu, Wanliang
Peng, Chunhua
Wang, Lina
Yang, Xiaoxia
Zhou, Feiyu
Tong, Jiawei
Cai, Jia
Li, Zhiyong
Wan, Bo
Zhang, Lei
Yang, Tao
Zhang, Minwen
Yang, Linlin
Yang, Yawen
Zeng, Wenjun
Wang, Bo
Wei, Xiaofeng
Xu, Xun
CNSA: a data repository for archiving omics data
title CNSA: a data repository for archiving omics data
title_full CNSA: a data repository for archiving omics data
title_fullStr CNSA: a data repository for archiving omics data
title_full_unstemmed CNSA: a data repository for archiving omics data
title_short CNSA: a data repository for archiving omics data
title_sort cnsa: a data repository for archiving omics data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377928/
https://www.ncbi.nlm.nih.gov/pubmed/32705130
http://dx.doi.org/10.1093/database/baaa055
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