Use of application containers and workflows for genomic data analysis.
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.
Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA.
Wade L
WL
Schulz
Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA.
Thomas J S
TJ
Durant
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.
Alexa J
AJ
Siddon
Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA.
Richard
R
Torres
Big data; bioinformatics workflow; containerization; genomics