Compi

Compi provides a framework for developing portable, reproducible computational pipelines for research workflows.


Key Features:

  • Packaging with compi-dk: compi-dk packages pipelines and their dependencies into Docker images for reproducible execution.
  • Docker integration: Supports containerization using Docker to encapsulate runtime environments and dependencies.
  • Automatic job scheduling: Provides automatic scheduling and execution of pipeline jobs.
  • Logging: Records execution metadata and job logs for provenance and debugging.
  • Shell-script-based pipelines: Supports pipelines implemented as shell scripts while supplying workflow engine features.
  • Open-source license: Distributed under the Apache License 2.0.

Scientific Applications:

  • Reproducible bioinformatics workflows: Implementation of reproducible computational workflows for bioinformatics and related computational research.
  • Containerized workflow deployment: Deployment of pipelines as Docker images created with compi-dk for consistent execution environments.
  • Batch job execution and monitoring: Execution and monitoring of batch computational jobs using automatic scheduling and logging features.

Methodology:

Pipelines are implemented as shell scripts with automatic job scheduling and logging and can be packaged into Docker images using compi-dk.

Topics

Details

License:
Apache-2.0
Cost:
Free of charge
Tool Type:
workflow
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Java, Shell
Added:
11/23/2021
Last Updated:
11/23/2021

Operations

Publications

López-Fernández H, Graña-Castro O, Nogueira-Rodríguez A, Reboiro-Jato M, Glez-Peña D. Compi: a framework for portable and reproducible pipelines. PeerJ Computer Science. 2021;7:e593. doi:10.7717/peerj-cs.593. PMID:34239974. PMCID:PMC8237318.

PMID: 34239974
PMCID: PMC8237318
Funding: - Consellería de Educación, Universidades e Formación Profesional: ED431C2018/55-GRC - Ministerio de Economía, Industria y Competitividad, Gobierno de España under the scope of the PolyDeep project: DPI2017-87494-R - Xunta de Galicia: ED481A-2019/299

Links