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DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative

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This summary is machine-generated.

Reproducible research workflows for large omics datasets are challenging for collaborative teams. This study introduces DataPackageR, a tool simplifying reproducible data preprocessing for computational scientists, ensuring data integrity and traceability.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Reproducible research requires sharing data and code for verification.
  • Implementing reproducible workflows for large omics datasets in collaborative teams presents challenges.
  • Existing methods for managing large datasets can be cumbersome and poorly adopted.

Purpose of the Study:

  • To present a lightweight, reproducible research workflow for preprocessing large omics datasets.
  • To introduce the DataPackageR tool for managing and sharing analysis-ready data.

Main Methods:

  • Leveraged open-source tools: R, Rmarkdown, git, Bioconductor.
  • Developed DataPackageR to decouple data processing from analysis.
  • Utilized R's package system for data management and version control.

Main Results:

  • DataPackageR ensures traceable data processing with documented package vignettes.
  • The workflow facilitates checksum verification and basic package version management.
  • Successfully implemented for pre-clinical immunological trial data over three years.

Conclusions:

  • DataPackageR offers a practical solution for reproducible omics data preprocessing in teams.
  • The workflow integrates smoothly into existing analyst workflows with minimal disruption.
  • This approach enhances data integrity and facilitates collaborative analysis of complex biological data.