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Kronos: a workflow assembler for genome analytics and informatics.

M Jafar Taghiyar1,2, Jamie Rosner1, Diljot Grewal1,2

  • 1Department of Molecular Oncology, British Columbia Cancer Agency, 675 West 10th Ave, V5Z 1L3 Vancouver, BC, Canada.

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|June 29, 2017
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Summary
This summary is machine-generated.

Kronos simplifies complex next-generation sequencing (NGS) data analysis by enabling the creation of modular, auditable, and distributable bioinformatics workflows from configuration files, reducing programming needs.

Keywords:
genomicspipelinereproducibilityworkflow

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) informatics has advanced, but implementing complex analytical workflows requires substantial programming expertise.
  • Automated analysis of NGS datasets into "best practices" remains a significant challenge.

Purpose of the Study:

  • To present Kronos, a software platform designed to streamline the development and execution of bioinformatics workflows.
  • To facilitate modular, auditable, and distributable workflow creation for NGS data analysis.

Main Methods:

  • Kronos compiles text configuration files into executable Python applications, eliminating the need for explicit workflow coding.
  • It includes a run manager for local, cluster, or cloud execution, task parallelization, and runtime event logging.
  • The platform supports the creation of shareable, modular components (nodes) for workflow construction.

Main Results:

  • Kronos generates highly modular and configurable workflows, enabling flexible meta-applications editable via configuration files.
  • Workflows are fully encoded for easy distribution and instantiation on external systems, promoting reproducible research.
  • The platform offers a standard framework for tool implementation, reuse, and community contribution.

Conclusions:

  • Kronos provides a robust solution for developing and executing complex bioinformatics workflows, reducing programming barriers.
  • The platform is available with Docker and AWS Machine Images, is open-source, and accessible via PyPI and GitHub.