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Scalable Workflows and Reproducible Data Analysis for Genomics.

Francesco Strozzi1, Roel Janssen2, Ricardo Wurmus3

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Summary
This summary is machine-generated.

Researchers can create reproducible bioinformatics pipelines using workflow systems like CWL, GWL, Snakemake, and Nextflow. These scalable, shareable workflows integrate diverse biological data and tools for robust scientific analysis.

Keywords:
Big dataBiocondaBioinformaticsCWLCloud computingCluster computingCommon Workflow LanguageDebian LinuxEvolutionary biologyGNU GuixGuix Workflow LanguageMPIMrBayesNextflowParallelizationSnakemakeVirtual machine

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Area of Science:

  • Bioinformatics and computational biology
  • Genomics, transcriptomics, proteomics, and interactomics analysis
  • High-throughput data integration and interpretation

Background:

  • Modern biological research generates massive datasets from high-throughput technologies, exceeding typical desktop computing capabilities.
  • Analyzing and integrating these large-scale biological datasets requires specialized computational approaches and infrastructure.
  • Ensuring reproducibility in complex bioinformatics analyses is a significant challenge for researchers.

Purpose of the Study:

  • To demonstrate how to describe and execute bioinformatics analyses using various workflow systems.
  • To illustrate the creation of reusable, reproducible, and shareable bioinformatics pipelines.
  • To compare different workflow engines and software distribution strategies for biological data analysis.

Main Methods:

  • Utilized four workflow engines: Common Workflow Language (CWL), Guix Workflow Language (GWL), Snakemake, and Nextflow.
  • Employed containerization technologies including Docker, GNU Guix, and Singularity for software bundling.
  • Leveraged software distributions such as Debian, GNU Guix, and Bioconda for biological tools (e.g., PAML, BLAST).

Main Results:

  • Successfully created scalable, reusable, and shareable workflows executable across different environments (desktop, cloud, clusters).
  • Demonstrated the integration of major biological software packages within lightweight containers.
  • Provided a comparative overview of different workflow solutions for bioinformatics tasks.

Conclusions:

  • Bundling software via public distributions and workflow engines enhances bioinformatics reproducibility.
  • Reusable and shareable pipelines reduce redundant effort and advance the goal of reproducible science.
  • The presented examples facilitate a rapid comparison of diverse solutions for complex biological data analysis.