Implementation of Cloud based Next Generation Sequencing data analysis in a clinical laboratory

BMC Research Notes

Bharat Thyagarajan³  Kevin A T Silverstein²  Matthew Bower⁵  Matthew Schomaker⁵  Sophia Yohe³  Adam Hauge⁴  Kenneth B Beckman⁴  John Chilton¹  Michael D Spears³  Jesse Erdmann²  Getiria Onsongo² 

[1] Applications Development, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, USA;Research Informatics Support Systems, Minnesota Supercomputing Institute, University of Minnesota, Room 599 Walter Library 117 Pleasant St SE, Minneapolis, MN 55455, USA;Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, USA;University of Minnesota Genomics Center, University of Minnesota, Minneapolis, USA;Molecular Diagnostics Laboratory, University of Minnesota Medical Center Fairview, Minneapolis, USA

关键词: Molecular diagnostics;    Variant detection;    Cloud computing;    Next generation sequencing;   

Others  :  1132698
DOI  :  10.1186/1756-0500-7-314

 received in 2013-09-30, accepted in 2014-05-06,  发布年份 2014

【 摘 要 】

Background

The introduction of next generation sequencing (NGS) has revolutionized molecular diagnostics, though several challenges remain limiting the widespread adoption of NGS testing into clinical practice. One such difficulty includes the development of a robust bioinformatics pipeline that can handle the volume of data generated by high-throughput sequencing in a cost-effective manner. Analysis of sequencing data typically requires a substantial level of computing power that is often cost-prohibitive to most clinical diagnostics laboratories.

Findings

To address this challenge, our institution has developed a Galaxy-based data analysis pipeline which relies on a web-based, cloud-computing infrastructure to process NGS data and identify genetic variants. It provides additional flexibility, needed to control storage costs, resulting in a pipeline that is cost-effective on a per-sample basis. It does not require the usage of EBS disk to run a sample.

Conclusions

We demonstrate the validation and feasibility of implementing this bioinformatics pipeline in a molecular diagnostics laboratory. Four samples were analyzed in duplicate pairs and showed 100% concordance in mutations identified. This pipeline is currently being used in the clinic and all identified pathogenic variants confirmed using Sanger sequencing further validating the software.

【 授权许可】

   
2014 Onsongo et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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