A case study for cloud based high throughput analysis of NGS data using the globus genomics system | JoVE Visualize

A case study for cloud based high throughput analysis of NGS data using the globus genomics system

Krithika Bhuvaneshwar¹, Dinanath Sulakhe², Robinder Gauba¹

¹Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC 20007, USA.

Computational and Structural Biotechnology Journal|March 1, 2016

View abstract on PubMed

Summary

Next generation sequencing (NGS) generates massive data. Globus Genomics offers a cloud-based solution for efficient NGS data management and analysis, simplifying complex workflows.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next generation sequencing (NGS) technologies generate vast datasets.
Handling and analyzing NGS data requires significant computational infrastructure and specialized bioinformatics expertise.
Existing solutions often struggle with the scale and complexity of terabyte-sized genomic data.

Purpose of the Study:

To present a practical solution for managing and analyzing large-scale NGS data.
To introduce Globus Genomics as a service simplifying NGS data handling and analysis.
To address the end-to-end NGS analysis requirements in translational genomics.

Main Methods:

Implementation of Globus Genomics, an enhanced Galaxy workflow system.
Leveraging Amazon's cloud computing infrastructure for elastic scaling and parallel processing.
Development of a service-based platform for easy, reliable, and fast data processing and transfer.

Main Results:

Successful simplification of terabyte-scale data handling for NGS.
Provision of advanced tools for comprehensive NGS data analysis.
Enabled parallel execution of multiple workflows to meet scale-out analysis needs.

Conclusions:

Globus Genomics provides a robust and scalable solution for NGS data challenges.
The system facilitates efficient translational genomics research by simplifying data analysis.
Cloud-based infrastructure is crucial for addressing the computational demands of modern genomics.

Keywords: