Supporting data for "Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines."
Dataset type: Software
Data released on June 26, 2017
Processing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of pre-configured bioinformatics software and pipelines on any computational platform.
We present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simple as running a single bioinformatics tool. This is achieved using a “meta-script” that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipeline output is post-processed by integration with the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser.
Our goal is to enable easy access to NGS data analysis pipelines for non-bioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.
Read the peer-reviewed publication(s):
Kim, B., Ali, T., Lijeron, C., Afgan, E., & Krampis, K. (2017). Bio-Docklets: virtualization containers for single-step execution of NGS pipelines. GigaScience, 6(8). doi:10.1093/gigascience/gix048