Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce | Zendy

Abhinav Nellore | Zendy; Christopher Wilks | Zendy; Kasper D. Hansen | Zendy; Jeffrey T. Leek | Zendy; Ben Langmead | Zendy

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Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce

Author(s) -

Abhinav Nellore,

Christopher Wilks,

Kasper D. Hansen,

Jeffrey T. Leek,

Ben Langmead

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw177

Subject(s) - protocol (science) , computer science , cloud computing , world wide web , medicine , alternative medicine , pathology , operating system

Public archives contain thousands of trillions of bases of valuable sequencing data. More than 40% of the Sequence Read Archive is human data protected by provisions such as dbGaP. To analyse dbGaP-protected data, researchers must typically work with IT administrators and signing officials to ensure all levels of security are implemented at their institution. This is a major obstacle, impeding reproducibility and reducing the utility of archived data.

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