Abstract
The introduction of next generation sequencing technologies did not bring only huge amounts of biological data but also highly sophisticated and versatile analysis workflows and systems. These new challenges require reliable and fast deployment methods over high performance servers in the local infrastructure or in the cloud. The use of virtualization technology has provided an efficient solution to overcome the complexity of deployment procedures and to provide a safe personalized execution box. However, the performance of applications running in virtual machines is worse than that of those running on the native infrastructure. Docker is a light weight alternative to the usual virtualization technology achieving notable better performance. In this paper, we explore the use case scenarios for using Docker to deploy and execute sophisticated bioinformatics tools and workflows, with a focus on the sequence analysis domain. We also introduce an efficient implementation of the package elasticHPC-Docker to enable creation of a docker-based computer cluster in the private cloud and in commercial clouds like Amazon and Google. We demonstrate by experiments that the use of elasticHPC-Docker is efficient and reliable in both private and commercial clouds.
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A Appendix
A Appendix
The following Dockerfile is used to build Docker image for Variant Calling detection:
Build Docker Image: To build a docker image you have to install Docker engine on your local host; as explained in http://docs.docker.com/engine/installation/. Once installed, write the code as shown above in a file called Dockerfile on the same directory where you will build your image. Finally run the following command line to build variant calling detection Docker image.
Start Docker Container: To start container using Docker engine, run the following command line:
Now user is ready to call any program for the variant detection workflow.
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Ali, A.A., El-Kalioby, M., Abouelhoda, M. (2016). The Case for Docker in Multicloud Enabled Bioinformatics Applications. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_52
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DOI: https://doi.org/10.1007/978-3-319-31744-1_52
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