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J Pathol Inform. 2016 Dec 30;7:53. doi: 10.4103/2153-3539.197197. eCollection 2016.

Use of application containers and workflows for genomic data analysis.

Author information

1
Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA.
2
Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA; Pathology and Laboratory Medicine Service, VA Connecticut Healthcare System, West Haven, CT, USA.

Abstract

BACKGROUND:

The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments.

METHODS:

Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing.

RESULTS:

While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data.

CONCLUSIONS:

With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.

KEYWORDS:

Big data; bioinformatics workflow; containerization; genomics

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