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

Bioinformatics. 2016 Aug 15;32(16):2551-3. doi: 10.1093/bioinformatics/btw177. Epub 2016 Apr 21.


Motivation: 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.

Results: We present a protocol and software tool for analyzing protected data in a commercial cloud. The protocol, Rail-dbGaP, is applicable to any tool running on Amazon Web Services Elastic MapReduce. The tool, Rail-RNA v0.2, is a spliced aligner for RNA-seq data, which we demonstrate by running on 9662 samples from the dbGaP-protected GTEx consortium dataset. The Rail-dbGaP protocol makes explicit for the first time the steps an investigator must take to develop Elastic MapReduce pipelines that analyse dbGaP-protected data in a manner compliant with NIH guidelines. Rail-RNA automates implementation of the protocol, making it easy for typical biomedical investigators to study protected RNA-seq data, regardless of their local IT resources or expertise.

Availability and implementation: Rail-RNA is available from http://rail.bio Technical details on the Rail-dbGaP protocol as well as an implementation walkthrough are available at https://github.com/nellore/rail-dbgap Detailed instructions on running Rail-RNA on dbGaP-protected data using Amazon Web Services are available at http://docs.rail.bio/dbgap/

Contacts: : anellore@gmail.com or langmea@cs.jhu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Algorithms
  • Computational Biology*
  • Databases, Genetic*
  • High-Throughput Nucleotide Sequencing
  • Humans
  • RNA
  • Reproducibility of Results
  • Software*


  • RNA