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Bioinformatics. 2013 Sep 1;29(17):2075-83. doi: 10.1093/bioinformatics/btt352. Epub 2013 Jun 20.

Harnessing virtual machines to simplify next-generation DNA sequencing analysis.

Author information

1
Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.

Abstract

MOTIVATION:

The growth of next-generation sequencing (NGS) has not only dramatically accelerated the pace of research in the field of genomics, but it has also opened the door to personalized medicine and diagnostics. The resulting flood of data has led to the rapid development of large numbers of bioinformatic tools for data analysis, creating a challenging situation for researchers when choosing and configuring a variety of software for their analysis, and for other researchers trying to replicate their analysis. As NGS technology continues to expand from the research environment into clinical laboratories, the challenges associated with data analysis have the potential to slow the adoption of this technology.

RESULTS:

Here we discuss the potential of virtual machines (VMs) to be used as a method for sharing entire installations of NGS software (bioinformatic 'pipelines'). VMs are created by programs designed to allow multiple operating systems to co-exist on a single physical machine, and they can be made following the object-oriented paradigm of encapsulating data and methods together. This allows NGS data to be distributed within a VM, along with the pre-configured software for its analysis. Although VMs have historically suffered from poor performance relative to native operating systems, we present benchmarking results demonstrating that this reduced performance can now be minimized. We further discuss the many potential benefits of VMs as a solution for NGS analysis and describe several published examples. Lastly, we consider the benefits of VMs in facilitating the introduction of NGS technology into the clinical environment.

CONTACT:

brian.wilhelm@umontreal.ca.

PMID:
23786767
DOI:
10.1093/bioinformatics/btt352
[Indexed for MEDLINE]
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