Latest revision as of 13:44, 12 February 2021

Abstract

Background: The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date. Results: We describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information. Conclusions: Integrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.

Document type: Article

Full document

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1186/s12859-018-2576-5 under the license http://creativecommons.org/licenses/by/4.0/

• http://dx.doi.org/10.5445/ir/1000090640 under the license http://creativecommons.org/licenses/by/4.0

• http://europepmc.org/articles/PMC6334444 under the license https://creativecommons.org/licenses/by/4.0/deed.de

• https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2576-5

• http://link.springer.com/article/10.1186/s12859-018-2576-5,

https://doaj.org/toc/1471-2105 under the license cc-by

• http://link.springer.com/content/pdf/10.1186/s12859-018-2576-5.pdf,

http://link.springer.com/article/10.1186/s12859-018-2576-5/fulltext.html,

http://dx.doi.org/10.1186/s12859-018-2576-5

• https://dblp.uni-trier.de/db/journals/bmcbi/bmcbi20.html#NierodaMTLSAP19,

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2576-5,

https://www.ncbi.nlm.nih.gov/pubmed/30646845,

https://publikationen.bibliothek.kit.edu/1000090640/22755823,

https://doi.org/10.1186/s12859-018-2576-5,

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2576-5?optIn=true,

https://academic.microsoft.com/#/detail/2914661225 under the license http://creativecommons.org/licenses/by/4.0/

• https://publikationen.bibliothek.kit.edu/1000090640,

https://publikationen.bibliothek.kit.edu/1000090640/22755823,

https://doi.org/10.5445/IR/1000090640,

http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:swb:90-906400

DOIS: 10.5445/ir/1000090640 10.1186/s12859-018-2576-5