Bio-Docklets

Bio-Docklets is a software tool that abstracts complex data operations of multistep, bioinformatics pipelines for next-generation sequencing (NGS) data analysis. It leverages virtualization containers to deploy preconfigured bioinformatics software and pipelines on any computational platform. Bio-Docklets provides a simple way of running pipelines as a single bioinformatics tool, using a "meta-script" that automatically starts the Bio-Docklets and controls pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is post-processed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. The goal of Bio-Docklets 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. Additionally, Bio-Docklets enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

Topic

NGS;Software engineering;RNA-seq

Detail

  • Operation: Service invocation

  • Software interface: Command-line user interface

  • Language: Shell;Python

  • License: The MIT licence

  • Cost: Free

  • Version name: -

  • Credit: The Center for Translational and Basic Research (CTBR), Research Center for Minority Institutions (RCMI) grant from National Institute for Minority Health Disparities (NIMHD), Weill Cornell Medical College (WCMC)-Clinical and Translational Science Center (CTSC).

  • Input: -

  • Output: -

  • Contact: kk104@hunter.cuny.edu

  • Collection: -

  • Maturity: -

Publications

  • Bio-Docklets: virtualization containers for single-step execution of NGS pipelines.
  • Kim B, et al. Bio-Docklets: virtualization containers for single-step execution of NGS pipelines. Bio-Docklets: virtualization containers for single-step execution of NGS pipelines. 2017; 6:1-7. doi: 10.1093/gigascience/gix048
  • https://doi.org/10.1093/gigascience/gix048
  • PMID: 28854616
  • PMC: PMC5569920

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