CNSA: a data repository for archiving omics data.
Journal
Database : the journal of biological databases and curation
ISSN: 1758-0463
Titre abrégé: Database (Oxford)
Pays: England
ID NLM: 101517697
Informations de publication
Date de publication:
01 01 2020
01 01 2020
Historique:
received:
16
04
2020
revised:
31
05
2020
accepted:
25
06
2020
entrez:
25
7
2020
pubmed:
25
7
2020
medline:
6
7
2021
Statut:
ppublish
Résumé
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/.
Identifiants
pubmed: 32705130
pii: 5875523
doi: 10.1093/database/baaa055
pmc: PMC7377928
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
Références
Nucleic Acids Res. 2018 Jan 4;46(D1):D30-D35
pubmed: 29040613
New Genet Soc. 2017 Sep 06;36(4):336-353
pubmed: 29238265
Plant J. 2020 Apr;102(2):222-229
pubmed: 31788877
Nucleic Acids Res. 2020 Jan 8;48(D1):D9-D16
pubmed: 31602479
Nat Biotechnol. 2019 Feb;37(2):179-185
pubmed: 30718868
Yi Chuan. 2019 Aug 20;41(8):761-772
pubmed: 31447427
Lancet Glob Health. 2020 Apr;8(4):e591-e602
pubmed: 32199125
Nucleic Acids Res. 2018 Jan 4;46(D1):D48-D51
pubmed: 29190397
Nucleic Acids Res. 2018 Jan 4;46(D1):D21-D29
pubmed: 29186510
Bioinformatics. 2018 Sep 1;34(17):i884-i890
pubmed: 30423086
Database (Oxford). 2016 Oct 2;2016:
pubmed: 27694206
Nature. 2010 Apr 15;464(7291):993-8
pubmed: 20393554
J Comput Aided Mol Des. 2014 Oct;28(10):1035-41
pubmed: 25038897
Gigascience. 2019 Apr 1;8(4):
pubmed: 30689836
Genet Test Mol Biomarkers. 2014 Jun;18(6):375-6
pubmed: 24896853
Contemp Oncol (Pozn). 2015;19(1A):A68-77
pubmed: 25691825
Nucleic Acids Res. 2018 Jan 4;46(D1):D14-D20
pubmed: 29036542
Nucleic Acids Res. 2020 Jan 8;48(D1):D24-D33
pubmed: 31702008