Volume 16 Supplement 2

Highlights from the Tenth International Society for Computational Biology (ISCB) Student Council Symposium 2014

Open Access

COSMOS: cloud enabled NGS analysis

  • Yassine Souilmi1, 2,
  • Jae-Yoon Jung2,
  • Alex Lancaster2,
  • Erik Gafni3,
  • Saaid Amzazi1,
  • Hassan Ghazal4,
  • Dennis Wall2, 5 and
  • Peter Tonellato2
BMC Bioinformatics201516(Suppl 2):A2

DOI: 10.1186/1471-2105-16-S2-A2

Published: 28 January 2015

Background

The dramatic fall of next generation sequencing (NGS) cost in recent years positions the price in range of typical medical testing, and thus whole genome analysis (WGA) may be a viable clinical diagnostic tool. Modern sequencing platforms routinely generate petabyte data. The current challenge lies in calling and analyzing this large-scale data, which has become the new time and cost rate-limiting step.

Methods

To address the computational limitations and optimize the cost, we have developed COSMOS (http://cosmos.hms.harvard.edu) , a scalable, parallelizable workflow management system running on clouds (e.g., Amazon Web Services or Google Clouds). Using COSMOS [1], we have constructed a NGS analysis pipeline implementing the Genome Analysis Toolkit - GATK v3.1 - best practice protocol [2, 3], a widely accepted industry standard developed by the Broad Institute. COSMOS performs a thorough sequence analysis, including quality control, alignment, variant calling and an unprecedented level of annotation using a custom extension of ANNOVAR. COSMOS takes advantage of parallelization and the resources of a high-performance compute cluster, either local or in the cloud, to process datasets of up to the petabyte scale, which is becoming standard in NGS.

Conclusion

This approach enables the timely and cost-effective implementation of NGS analysis, allowing for it to be used in a clinical setting and translational medicine. With COSMOS we reduced the whole genome data analysis cost under the $100 barrier, placing it within a reimbursable cost point and in clinical time, providing a significant change to the landscape of genomic analysis and cement the utility of cloud environment as a resource for Petabyte-scale genomic research.

Authors’ Affiliations

(1)
Department of Biology, Faculty of Sciences of Rabat
(2)
Center for Biomedical Informatics, Harvard Medical School
(3)
INVITAE
(4)
Department of Biology, Mohamed First University
(5)
Department of Pediatrics, Division of Systems Medicine, Stanford University

References

  1. Gafni E, Luquette LJ, Lancaster AK, Hawkins JB, Jung J-Y, Souilmi Y, Wall DP, Tonellato PJ: COSMOS: Python library for massively parallel workflows. Bioinformatics. 2014, btu385-Google Scholar
  2. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011, 43: 491-498. 10.1038/ng.806.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella KV, Altshuler D, Gabriel S, DePristo MA: From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. 2013Google Scholar

Copyright

© Souilmi et al; licensee BioMed Central Ltd. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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