Scalable and cost-effective NGS genotyping in the cloud

BMC Medical Genomics

Dennis P. Wall⁵  Peter J. Tonellato¹  Hassan Ghazal⁶  Saaïd Amzazi³  Ryan Powles⁷  Jared B. Hawkins⁷  Ettore Rizzo⁴  Jae-Yoon Jung⁷  Alex K. Lancaster²  Yassine Souilmi³ 

[1] Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston 02215, MA, USA;Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston 02215, MA, USA;Department of Biology, Mohamed Vth University, 4 Ibn Battouta Avenue, Rabat, Morocco;Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 1, Pavia 27100, Italy;Department of Pediatrics and Psychiatry (by courtesy), Division of Systems Medicine & Program in Biomedical Informatics, Stanford University, Stanford 94305, CA, USA;Department of Biology, Mohamed First University, Oujda, Nador, Morocco;Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston 02115, MA, USA

关键词: Parallel computing;    Bioinformatics;    Software;    Medical genomics;    Cloud computing;    Clinical sequencing;    Next-generation sequencing;   

Others  :  1228948
DOI  :  10.1186/s12920-015-0134-9

 received in 2015-04-16, accepted in 2015-09-11,  发布年份 2015

【 摘 要 】

Background

While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10’s of dollars.

Results

We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets.

Conclusions

Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.

【 授权许可】

   
2015 Souilmi et al.

【 图 表 】

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