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Summary
This summary is machine-generated.

ClarID provides a unified framework for generating human- and machine-readable identifiers for biomedical subjects and biospecimens. This system embeds crucial metadata, enhancing data integration and analysis in research.

Keywords:
Biosample identifiersHealth data integrationMetadata harmonizationOntology-based annotationStructured BarcodingSubject identifiers

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Area of Science:

  • Biomedical Informatics
  • Data Management
  • Genomics

Background:

  • Biomedical research often uses simple IDs (e.g., UUIDs) for subjects and biospecimens, lacking embedded semantic information.
  • Separate storage of contextual metadata (tissue type, diagnosis) complicates data integration, cohort selection, and analysis.
  • Existing structured barcoding systems are often limited to specific consortia or domains, lacking a unified, extensible framework.

Purpose of the Study:

  • To develop a domain-agnostic specification (ClarID) for creating unified, human- and machine-readable identifiers for subjects and biospecimens.
  • To embed key contextual metadata directly into these identifiers, improving data traceability and accessibility.
  • To provide an open-source tool (ClarID-Tools) for generating, decoding, and validating these structured identifiers.

Main Methods:

  • Developed ClarID, a specification supporting human-readable and compact 'stub' identifier formats.
  • Implemented ClarID-Tools, an open-source command-line tool processing tabular metadata (CSV/TSV) with a YAML codebook.
  • The tool generates, decodes, and validates identifiers, including QR code functionality, supporting bulk and single-sample processing.

Main Results:

  • Applied ClarID to Genomic Data Commons (GDC) datasets, generating interpretable identifiers for over 113,000 clinical records and 4,255 biospecimen records.
  • Demonstrated the utility of ClarID in creating structured, interpretable identifiers from existing datasets.
  • All materials, including scripts and data, are publicly available and reproducible via GitHub and Google Colab.

Conclusions:

  • ClarID bridges the gap between opaque accession numbers and rich metadata schemas by embedding context into structured identifiers.
  • The ClarID framework enhances data traceability, simplifies downstream analysis, and offers adaptability through a configurable codebook.
  • The open-source ClarID-Tools software is freely available with comprehensive documentation and reproducible pipelines.