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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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

Abhinav Nellore¹, Christopher Wilks², Kasper D Hansen³

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.

Bioinformatics (Oxford, England)

|May 7, 2016

Summary

This summary is machine-generated.

Researchers can now easily analyze protected human sequencing data in the cloud using the new Rail-dbGaP protocol and Rail-RNA tool. This simplifies access to valuable genomic datasets, enhancing reproducibility and data utility for biomedical studies.

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Area of Science:

Genomic data analysis
Bioinformatics
Cloud computing in research

Background:

Vast amounts of sequencing data, including human data, are publicly archived.
Accessing dbGaP-protected human data is challenging due to security requirements, hindering research and reproducibility.

Purpose of the Study:

To present a protocol and software tool for analyzing dbGaP-protected data in a commercial cloud environment.
To simplify the process for biomedical researchers to access and analyze sensitive genomic datasets.

Main Methods:

Developed the Rail-dbGaP protocol for analyzing data on Amazon Web Services Elastic MapReduce.
Created Rail-RNA v0.2, a spliced aligner for RNA-seq data, to automate the protocol's implementation.
Demonstrated the tool by analyzing 9662 samples from the dbGaP-protected GTEx consortium dataset.

Main Results:

The Rail-dbGaP protocol explicitly outlines steps for compliant data analysis on AWS Elastic MapReduce.
Rail-RNA automates the protocol, enabling typical biomedical investigators to study protected RNA-seq data.
Successful analysis of a large-scale, dbGaP-protected RNA-seq dataset was achieved.

Conclusions:

The Rail-dbGaP protocol and Rail-RNA tool significantly lower the barrier to analyzing protected genomic data.
This facilitates greater utilization of valuable archived sequencing data, promoting scientific reproducibility and discovery.
Biomedical researchers can now more readily access and analyze sensitive datasets without extensive IT infrastructure or expertise.