Implementing FAIR Data Principles in One Health Genomics: A Practical Guide for Researchers

Lucy Sanders Jan 09, 2026 175

This article provides a comprehensive roadmap for implementing FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in One Health genomics.

Implementing FAIR Data Principles in One Health Genomics: A Practical Guide for Researchers

Abstract

This article provides a comprehensive roadmap for implementing FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in One Health genomics. It addresses the unique challenges of integrating diverse data types from human, animal, and environmental sources. Targeting researchers, scientists, and drug development professionals, the content moves from foundational concepts to practical applications, common troubleshooting strategies, and validation frameworks. The article emphasizes how FAIRification enhances cross-disciplinary collaboration, accelerates pathogen surveillance, and fosters more effective therapeutic discovery in a connected ecosystem.

What Are FAIR Principles and Why Are They Critical for One Health Genomics?

Within the One Health genomics research paradigm—which integrates human, animal, and environmental health—data generation is vast and complex. The effective translation of genomic insights into actionable public health or drug development outcomes is contingent upon robust data stewardship. This application note elucidates the FAIR Guiding Principles, defining them as foundational protocols for enhancing data utility and machine-actionability in collaborative, cross-species research initiatives.

The Four Pillars: Application Notes

Findable

Data and metadata must be easy to locate by both humans and computers. The core identifier is a globally unique and persistent identifier (PID).

  • Key Protocol: Metadata and Data Identifier Assignment.
    • Objective: To ensure every dataset is discoverable through rich, indexed metadata.
    • Materials: Dataset, metadata schema (e.g., MIxS for genomics), repository API (e.g., ENA, NCBI, institutional repository).
    • Methodology:
      • Assign a Persistent Identifier (e.g., DOI, Accession number) to the final, versioned dataset.
      • Describe the data with rich metadata using a relevant, community-accepted schema.
      • Register or deposit both the PID and metadata in a searchable resource (e.g., data repository, catalog).
      • Ensure metadata remains accessible even if the underlying data is deprecated.

Accessible

Data is retrievable using standard, open protocols, potentially with authentication and authorization where necessary.

  • Key Protocol: Standardized Data Retrieval Workflow.
    • Objective: To enable automated and manual data access via a standardized communication protocol.
    • Materials: Data repository, authentication token (if applicable), data access protocol.
    • Methodology:
      • The data is stored in a trusted repository with a defined access policy (open, embargoed, controlled).
      • Access is facilitated via a standardized, free, and open protocol (e.g., HTTPS, FTP, API).
      • For controlled access, an authorization process (e.g., via OAuth 2.0) is clearly defined.
      • Metadata is always accessible, even if data access is restricted.

Interoperable

Data integrates with other datasets and can be utilized by applications or workflows for analysis, storage, and processing.

  • Key Protocol: Metadata and Vocabulary Harmonization.
    • Objective: To enable data integration from diverse One Health domains (e.g., clinical, genomic, environmental).
    • Materials: Source datasets, shared conceptual model (e.g., OBO Foundry ontology), data mapping tool (e.g., Python/R scripts).
    • Methodology:
      • Use formal, accessible, shared, and broadly applicable knowledge representations (e.g., SNOMED CT, ENVO, NCBI Taxonomy) for metadata fields.
      • Use community-standard data formats (e.g., FASTQ, VCF, CRAM for genomics) where possible.
      • Reference other related data using their PIDs within the metadata.
      • Apply syntactic (format) and semantic (meaning) mapping tools to align heterogeneous datasets.

Reusable

Data and metadata are sufficiently well-described to be replicated, combined, or used in new research.

  • Key Protocol: Comprehensive Provenance and Readme Documentation.
    • Objective: To maximize future utility and reproducibility of the dataset.
    • Materials: Data processing logs, laboratory notebooks, citation information, licensing framework.
    • Methodology:
      • Document all aspects of data provenance: who created it, with what tools, parameters, and processing steps.
      • Provide a clear, machine-readable data usage license (e.g., CCO, MIT, or controlled-access terms).
      • Accurately link data to its source (a publication, grant, or originating project) using PIDs.
      • Meet domain-relevant community standards in both metadata and data quality.

Quantitative Data on FAIR Implementation Impact

Table 1: Comparative Analysis of Data Reuse and Efficiency Metrics

Metric Non-FAIR Aligned Data FAIR Aligned Data Measurement Source
Data Discovery Time Hours to days (manual search) Minutes (automated query) Observational study of repository searches
Integration Preparation Effort High (80% time on cleaning/mapping) Reduced (focus on analysis) Survey of bioinformatics workflows
Reuse Citation Rate Lower, often uncited Significantly higher Citation tracking in public repositories
Machine-Actionability Low (requires human interpretation) High (automated metadata parsing) Assessment of API access and metadata richness

Visualizations

FAIR Principles Logical Framework

D FAIR FAIR F Findable FAIR->F A Accessible FAIR->A I Interoperable FAIR->I R Reusable FAIR->R PID Persistent Identifier & Rich Metadata F->PID Protocol Standard Access Protocol A->Protocol Vocab Shared Vocabularies I->Vocab License Clear Provenance & License R->License

FAIR Data Workflow in One Health Genomics

D Sample One Health Sample (Human, Animal, Env.) Seq Sequencing & Primary Analysis Sample->Seq Meta Rich Metadata (MIxS, Ontologies) Sample->Meta Data Raw & Processed Data Files Seq->Data Seq->Meta Repo Trusted Repository Data->Repo Meta->Repo FAIR FAIR Digital Object (PID + Metadata + Data) Repo->FAIR User Research / Drug Development User FAIR->User Access & Reuse

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials and Tools for FAIR One Health Genomics Data Management

Item Category Specific Example/Solution Function in FAIR Context
Persistent Identifiers DOI (DataCite), Accession Number (ENA/SRA) Provides globally unique, permanent reference for data (Findable).
Metadata Standards MIxS (Minimum Information about any Sequence), INSDC checklist Schema to capture essential contextual data (Findable, Reusable).
Ontologies/Vocabularies NCBI Taxonomy, ENVO, SNOMED CT Controlled vocabularies for species, environment, phenotype (Interoperable).
Trusted Repository ENA, NCBI SRA, Zenodo, Institutional Repo Preserves data, provides PID, implements access control (Accessible).
Data Formats CRAM, VCF, FASTA/FASTQ Community-standard, often compressed/lossless formats (Interoperable).
Provenance Tracker Research Object Crates (RO-Crate), Electronic Lab Notebooks Packages data, code, and workflow to document lineage (Reusable).
Access Protocol HTTPS, FTP, Aspera, API (e.g., ENA API) Standardized methods for automated data retrieval (Accessible).
Usage License Creative Commons (CC0, BY), Custom Data Use Agreement Clearly communicates permissions for reuse (Reusable).

Application Notes on FAIR Data Integration for One Health Genomics

One Health research necessitates the integration of disparate, multi-scale datasets from human clinical, veterinary, and environmental surveillance. Adherence to the FAIR principles (Findable, Accessible, Interoperable, Reusable) is critical for enabling cross-domain data analysis and accelerating translational insights.

Table 1: Core Quantitative Metrics for Integrated One Health Genomic Surveillance

Metric Category Human Clinical Data Animal/Veterinary Data Environmental Data (e.g., Wastewater) Integrated FAIR Goal
Typical Sequencing Depth 100-150x (WGS) 30-100x (WGS) 500-10,000x (amplicon) Standardized metadata for depth & platform
Key Metadata Fields Age, symptom onset, geolocation Species, health status, husbandry Sample type (water/soil), pH, temp Use of controlled vocabularies (SNOMED CT, ENVO)
Primary File Format CRAM/BAM, FASTQ, VCF FASTQ, VCF FASTQ, count tables Cloud-optimized formats (e.g., .zarr)
Public Repository NCBI SRA, dbGaP NCBI SRA, ENA NCBI SRA, ENA, NDJSON Persistent identifiers (DOIs) for datasets
Minimum Sample Size (Per Study) 500-1000 isolates 200-500 isolates 50-200 sampling sites Sample size justification linked to data reusability

Table 2: FAIR Compliance Checklist for a One Health Genomics Project

FAIR Principle Implementation Requirement Compliance Tool/Standard
Findable Unique, persistent identifier (PID) for dataset. Rich, searchable metadata. DataCite DOI, NCBI BioProject ID
Accessible Standardized, open communication protocol. Metadata accessible even if data is restricted. HTTPS, OAuth 2.0, ENA API
Interoperable Use of formal, accessible, shared knowledge representations. Qualified references to other metadata. OBO Foundry ontologies (GO, CHEBI), MIxS standards
Reusable Detailed provenance and data usage license. Domain-relevant community standards. CCO waiver, TRUST principles, INSDC submission.

Protocols

Protocol 1: Integrated Metagenomic Sequencing for Pathogen Detection in Human, Animal, and Environmental Matrices

Objective: To uniformly process diverse sample types for untargeted detection of bacterial and viral pathogens, enabling cross-species comparison.

Materials: See "The Scientist's Toolkit" below. Procedure:

  • Sample Collection & Nucleic Acid Extraction:
    • Human/Animal: Collect nasal/oropharyngeal swabs or feces in universal transport medium. Extract total nucleic acid using a bead-beating protocol (e.g., MagMAX Viral/Pathogen Kit) to ensure lysis of hardy pathogens.
    • Environmental: Collect 50-100mL of wastewater or surface water. Concentrate via centrifugal filtration (100kDa membrane). Process pellet as per human/animal samples.
  • Library Preparation & Sequencing:
    • Treat DNA/RNA extract with DNase I to enrich for RNA viruses. Perform reverse transcription for RNA.
    • Use a shotgun metagenomic sequencing kit (e.g., Nextera XT DNA Library Prep) for all samples. Critical: Use unique dual indices (UDIs) for each sample to prevent index hopping and allow pooling of all sample types in a single sequencing run.
    • Sequence on an Illumina NextSeq 2000 platform to generate 2x150 bp paired-end reads, targeting 20-50 million reads per sample.
  • Bioinformatic Analysis (FAIR-Oriented Workflow):
    • Demultiplexing & QC: Use bcl-convert or bcl2fastq. Assess quality with FastQC.
    • Host Depletion: Map reads to appropriate host genomes (human GRCh38, bovine ARS-UCD1.2, etc.) using Kraken2 with a standard database and remove matching reads.
    • Taxonomic & Pathogenic Profiling: Analyze non-host reads with Kraken2/Bracken against the standardized "PlusPF" database (includes archaea, bacteria, viruses, plasmids, fungi, protozoa). Output results in mOTHur-standard format for interoperability.
    • Contig Assembly & Annotation: Assemble depleted reads with metaSPAdes. Predict open reading frames with Prodigal. Annotate against ResFinder, VFDB, and CARD databases for antimicrobial resistance and virulence genes.

Protocol 2: Phylogenomic Integration of Isolate Data Across One Health Domains

Objective: To construct unified phylogenetic trees integrating pathogen isolates from human, animal, and environmental sources to trace transmission pathways.

Procedure:

  • Data Curation (FAIR Focus):
    • Gather whole-genome sequencing (WGS) data from public repositories (SRA, ENA) and in-house studies. Document all source metadata using the MIxS (Minimum Information about any (x) Sequence) checklists.
    • Ensure all isolates have associated spatiotemporal metadata (collection date, latitude, longitude, source: human/animal/environment).
  • Core Genome Alignment:
    • Assemble all WGS reads to draft genomes using shovill (wrapper for SPAdes).
    • Annotate genomes uniformly with Prokka or Bakta.
    • Identify the core genome alignment using Roary (≥99% presence in all isolates) or ParSNP for a more robust alignment.
  • Phylogenetic Inference & Integration:
    • Filter the core genome alignment for recombination using Gubbins.
    • Construct a maximum-likelihood phylogeny using IQ-TREE2 with automatic model selection and 1000 ultrafast bootstrap replicates.
    • Visual Integration: Use Microreact to create an interactive visualization. Upload the tree file, and a CSV table containing the FAIR metadata (source, location, date, antimicrobial resistance profile). This creates a shareable, reusable resource linking genomic data to contextual metadata.

Diagrams

workflow cluster_sampling Sample Collection & Processing cluster_fair FAIR Data Curation & Integration cluster_analysis Integrated Analysis Human Human H_Seq H_Seq Human->H_Seq WGS/Amplicon Animal Animal A_Seq A_Seq Animal->A_Seq WGS/Amplicon Environment Environment E_Seq Sequencing Data (FASTQ) Environment->E_Seq Metagenomics Metadata Standardized Metadata (MIxS, Ontologies) H_Seq->Metadata A_Seq->Metadata E_Seq->Metadata Repository Trusted Repository (SRA, ENA) with DOI Metadata->Repository AMR AMR/Virulence Profiling Repository->AMR Phylo Phylogenetics & Transmission Trees Repository->Phylo Stats Statistical & Machine Learning Models Repository->Stats Insights One Health Insights (Source attribution, Risk prediction, Intervention) AMR->Insights Phylo->Insights Stats->Insights

One Health FAIR Data Integration Workflow

pathway Reservoir Animal/Environmental Reservoir Spillover Direct Contact Contaminated Food/Water Vector Reservoir->Spillover Pathogen Transmission HumanHost Human Host Spillover->HumanHost EnvFeedback Environmental Feedback (WW Effluent, Agricultural Runoff) HumanHost->EnvFeedback Waste/Effluent EnvFeedback->Reservoir Reseeding SelectionPressure Antimicrobial Use in Clinics & Agriculture AMRGene AMR Gene Pool (Mobile Genetic Elements) SelectionPressure->AMRGene Selects & Enriches AMRGene->Reservoir HGT AMRGene->HumanHost HGT

One Health AMR Transmission & Selection Cycle

The Scientist's Toolkit: Key Research Reagent Solutions

Item/Category Function in One Health Genomics Example Product/Brand
Universal Nucleic Acid Preservation Medium Stabilizes DNA/RNA from diverse sample types at point of collection, ensuring integrity for downstream omics. Norgen's Biotek Sample Preservation Kit, DNA/RNA Shield (Zymo Research)
Broad-Spectrum Nuclease Inhibitors Critical for environmental samples (e.g., wastewater) which contain high levels of RNases and DNases. SUPERase•In RNase Inhibitor, Baseline-ZERO DNase
Metagenomic Library Prep Kit Enables unbiased, shotgun sequencing of total nucleic acid from any source without prior amplification bias. Illumina DNA Prep, KAPA HyperPlus Kit
Unique Dual Index (UDI) Oligos Allows massive multiplexing of human, animal, and environmental samples in one sequencing run, preventing index hopping. Illumina CD Indexes, IDT for Illumina UDIs
Host Depletion Probes Removes abundant host (human, animal) reads to increase sensitivity for pathogen detection in clinical/veterinary samples. Human/Bovine/Canine rRNA Depletion Kit (New England Biolabs)
Positive Control Synthetic Community Validates entire workflow from extraction to sequencing across sample types; ensures cross-lab comparability (FAIR). ZymoBIOMICS Microbial Community Standard
Cloud-Based Analysis Platform Provides scalable, reproducible computational environment for integrating large datasets with FAIR principles. Terra.bio, Galaxy Project, CZ ID (Chan Zuckerberg ID)

Application Notes on FAIR Data Implementation in One Health Genomics

The integration of FAIR (Findable, Accessible, Interoperable, Reusable) data principles into One Health genomics is critical for addressing complex threats like pandemics and antimicrobial resistance (AMR). These notes outline the application of FAIR in building actionable surveillance systems.

Table 1: Impact of FAIR-Compliant Data Sharing on Pathogen Surveillance Timelines (Comparative Analysis)

Metric Non-FAIR Ecosystem (Traditional Submission) FAIR-Compliant Ecosystem (Streamlined Pipeline)
Data Submission to Public Repository 30-180 days (Post-publication) ≤ 7 days (Real-time, pre-publication)
Time to Primary Analysis (e.g., Variant Calling) 2-4 weeks (Heterogeneous pipelines) 24-48 hours (Standardized workflows)
Inter-Lab Data Integration for Meta-Analysis Months (Manual harmonization) Days (Automated via shared ontologies)
Identification of Emerging Variant/Resistance Gene 6-12 months lag Potential for early warning (<1 month)

Table 2: Key AMR Gene Databases & Their FAIRness Indicators

Database Name Primary Focus Findability (Unique PID) Interoperability (Standard Ontology) Reusability (Clear License)
CARD Comprehensive Antibiotic Resistance Database DOI for releases RO-Crate, ARO ontology CC BY-SA 4.0
NCBI AMRFinderPlus NCBI's pathogen resistance detection BioProject/BioSample IDs NCBI Taxonomy, SNPeff Public domain
ResFinder Acquired antimicrobial resistance genes None by default Custom nomenclature CC BY-NC 4.0
MEGARes AMR hierarchy for metagenomics DOI MEGARes ontology CC BY 4.0

Detailed Experimental Protocols

Protocol 1: End-to-End FAIR-Compliant Metagenomic Sequencing for AMR Tracking in One Health Samples

Objective: To generate and publish sequence data from environmental, animal, or human samples with embedded FAIR metadata for AMR gene surveillance.

I. Sample Collection & Metadata Annotation

  • Sample Collection: Collect sample (e.g., wastewater, nasal swab, agricultural run-off) using appropriate sterile techniques.
  • Instantiate Metadata Template: At the point of collection, complete a standardized metadata sheet (e.g., ISA-Tab format or NCBI BioSample checklist).
  • Mandatory Fields: Include sample type, host/environment, geographic location (latitude/longitude), collection date/time, AMR exposure risk (if known), collector name. Assign a unique local Sample ID.

II. DNA Extraction & Library Preparation

  • Extract total genomic DNA using a broad-spectrum kit (e.g., DNeasy PowerSoil Pro Kit for environmental samples).
  • Quantify DNA using fluorometry (e.g., Qubit dsDNA HS Assay).
  • Prepare sequencing library using a kit compatible with your platform (e.g., Illumina DNA Prep). Include negative extraction and library preparation controls.
  • Critical FAIR Step: Record all kit catalog numbers, lot numbers, and protocol deviations in the experimental metadata file. Link this file to the Sample ID.

III. Sequencing & Primary Data Output

  • Sequence on an appropriate platform (e.g., Illumina NextSeq 2000 for 2x150bp paired-end reads).
  • Generate raw FASTQ files. The sequencing facility should provide a run manifest linking each FASTQ file to the submitted Sample ID.

IV. Computational Analysis & FAIR Data Packaging

  • Quality Control: Use FastQC and Trimmomatic to assess and trim adapter/low-quality sequences.
  • AMR Gene Profiling: Use a standardized containerized workflow:

  • Taxonomic Profiling: Use Kraken2/Bracken against a standard database (e.g., GTDB) for co-occurring pathogen identification.
  • FAIR Packaging: Create a RO-Crate (Research Object Crate) containing:
    • Raw FASTQ files (or links to repository).
    • Final analysis outputs (JSON, TSV).
    • The detailed metadata file (metadata.jsonld).
    • A Dockerfile or Singularity definition of the analysis environment.
    • A README describing the crate contents in plain language.

V. Data Deposition in Public Repositories

  • Upload raw sequence reads and minimal metadata to the ENA, SRA, or GISAID (for notifiable pathogens) using their submission portals. This assigns a unique BioProject (PRJNA...) and BioSample (SAMN...) accession.
  • Deposit the analysis-ready RO-Crate in a domain-specific repository like Zenodo or Figshare, which will assign a DOI. In the description, link back to the SRA/ENA accessions.
  • Register the study in a public dashboard (e.g., WHO's EPI-BRAIN, AMR Register) using the provided DOIs and accessions.

Protocol 2: Standardized Phylogenomic Analysis for Pathogen Outbreak Tracking

Objective: To reconstruct a phylogeny from publicly available FAIR genomic data to trace transmission dynamics during a suspected outbreak.

I. FAIR Data Retrieval

  • Find & Access: Query public repositories using programmatic tools.

II. Core Genome Alignment & Variant Calling

  • Assembly & Annotation: Assemble reads using SKESA or Shovill. Annotate assemblies with Prokka or Bakta.
  • Define Core Genome: Use Roary or Panaroo to identify the core genome (genes present in ≥99% of isolates) from the annotated GFF files.
  • Create Alignment: Extract core gene sequences and concatenate them using HarvestSuite (parsnp) or a custom script to generate a multi-FASTA alignment file.

III. Phylogenetic Inference & Visualization

  • Model Testing & Tree Building: Use IQ-TREE2 for rapid model selection and maximum-likelihood tree inference.

  • Temporal Signal & Dating: For data with collection dates, use BEAST2 to generate a time-scaled phylogeny and estimate evolutionary rates.
  • Visualization: Annotate the resulting tree (core_genome_alignment.fasta.treefile) with metadata (location, host, AMR profile) using Nextstrain Auspice or Microreact.

Mandatory Visualizations

G cluster_0 One Health Data Sources FAIR FAIR Data Principles DataGen Data Generation (Sequencing, Phenotyping) FAIR->DataGen Guides Metadata RepoDeposit Repository Deposit (SRA, Zenodo) FAIR->RepoDeposit Enables Findability Analysis Integrated Analysis (Phylogenetics, AMR Detection) FAIR->Analysis Ensures Interoperability DataGen->RepoDeposit Data + Metadata RepoDeposit->Analysis Programmatic Access Decision Informed Decision (Early Warning, Vaccine Design, Stewardship) Analysis->Decision Human Human Human->DataGen Clinical Isolates Animal Animal Animal->DataGen Veterinary Samples Env Env Env->DataGen Wastewater Surveillance

Title: FAIR Data Pipeline for One Health Threat Intelligence

workflow Sample Sample FASTQ FASTQ Sample->FASTQ QC Quality Control & Trimming FASTQ->QC AMR AMR Gene Detection (AMRFinderPlus) QC->AMR Taxa Taxonomic Profiling (Kraken2) QC->Taxa AMRDB CARD/MEGARes Database AMRDB->AMR Refers to Int Integrated Report (JSON/TSV) AMR->Int TaxDB Taxonomic Database TaxDB->Taxa Refers to Taxa->Int Vis Visual Dashboard (e.g., Microreact) Int->Vis

Title: AMR Metagenomic Analysis Workflow

The Scientist's Toolkit: Research Reagent & Resource Solutions

Item/Category Example Product/Resource Function in FAIR One Health Genomics
Standardized Metadata Tool ISAcreator / CEDAR Creates structured, ontology-annotated metadata templates to ensure Interoperability from sample collection.
All-in-One DNA Extraction Kit DNeasy PowerSoil Pro Kit (QIAGEN) Provides consistent, high-yield DNA from diverse, complex One Health sample matrices (soil, stool, swabs).
Metagenomic Library Prep Kit Illumina DNA Prep A standardized, widely adopted protocol for preparing sequencing libraries, ensuring data consistency across labs.
Negative Control ZymoBIOMICS Microbial Community Standard A defined mock microbial community used as a process control to monitor contamination and assay performance.
Analysis Container Docker / Singularity Image Packages the exact software environment (e.g., with AMRFinderPlus, Kraken2) to guarantee reproducible (Reusable) results.
Data Packaging Standard RO-Crate A structured format to bundle data, code, and metadata into a single, reusable research object with a clear license.
Public Data Repository European Nucleotide Archive (ENA) / Zenodo Provides globally unique, persistent identifiers (PIDs) for Findability and long-term archival Access.
Ontology for Annotation NCBI Taxonomy ID, ARO Ontology Standardized vocabulary for describing organisms and AMR genes, critical for Interoperability in data integration.

Key Stakeholders and Data Types in the One Health Genomics Ecosystem

The integration of genomics across human, animal, plant, and environmental health—the One Health approach—generates complex, multi-scale data. Adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles is paramount for enabling cross-sectoral analysis and accelerating translational discovery. This document details the key stakeholders, data types, and practical protocols within this ecosystem, framed as essential application notes for implementing FAIR-compliant research.

Stakeholder Analysis and Roles

Stakeholders are entities that generate, fund, regulate, use, or are impacted by One Health genomic data. Their roles and data interactions are summarized below.

Table 1: Key Stakeholders in the One Health Genomics Ecosystem

Stakeholder Category Primary Representatives Core Interest & Role in Data Lifecycle
Data Generators Public Health Agencies, Veterinary Diagnostic Labs, Agricultural Research Institutes, Environmental Monitoring Bodies, Academic Research Labs Produce raw and processed genomic (e.g., WGS, metagenomic) and associated metadata. Responsible for initial data quality and annotation.
Data Integrators & Repositories NCBI, ENA, DDBJ, BV-BRC, EFSA, WHO Data Repositories, Institutional Data Lakes Curate, archive, and provide access to datasets. Implement data standards and accession systems for findability.
Data Analysts & Researchers Bioinformaticians, Epidemiologists, Microbial Ecologists, Comparative Genomicists, Phylodynamic Modelers Analyze integrated datasets to identify pathogens, AMR genes, transmission pathways, and evolutionary trends. Primary users of FAIR data.
Policy & Decision Makers Government Health & Agriculture Departments (e.g., CDC, USDA, EFSA), Drug/ Vaccine Regulatory Agencies (e.g., FDA, EMA), WHO, OIE Use evidence from data analysis to inform surveillance programs, outbreak responses, antimicrobial use policies, and therapeutic approvals.
Funders & Initiatives NIH, Wellcome Trust, EU Horizon Europe, The Global Fund, BMGF Define data sharing mandates, fund infrastructure (e.g., cloud platforms), and drive consortium-based projects like the European COVID-19 Data Platform.
Private Sector Pharmaceutical & Diagnostic Companies, Agri-tech, Biotechnology Firms, Zoonotic Surveillance Start-ups Utilize genomic insights for drug/vaccine target discovery, diagnostic assay development, and precision agriculture solutions. Often contributors and end-users.
Affected Communities Patients, Farmers, Consumers, Environmental Advocacy Groups Subjects and beneficiaries of research. Increasingly engaged via citizen science data collection and demand for transparent data use.

Data Typology and Specifications

One Health genomics data is heterogeneous. FAIR implementation requires standardized description and formatting.

Table 2: Core Data Types and FAIRification Requirements

Data Type Common Formats Key Metadata Standards (for Interoperability) Typical Volume per Sample Primary Use Case
Whole Genome Sequencing (WGS) FASTQ, BAM, CRAM, VCF, FASTA MIxS (Minimum Information about any (x) Sequence), INSDC sample checklist 0.5 - 100 GB Pathogen identification, outbreak溯源, AMR & virulence profiling.
Metagenomic Sequencing FASTQ, SAM/BAM, BIOM, Kraken2 report MIxS (especially for environmental & host-associated samples) 10 - 200 GB Microbiome characterization, pathogen discovery in environmental reservoirs.
Antimicrobial Resistance (AMR) Data ARO/ CARD Ontology terms, MIC values, TSV MIABIS-AMR, WHO GLASS AMR data structure KB - MB Tracking resistance patterns, correlating genotype with phenotype.
Epidemiological & Clinical Metadata CSV, TSV, JSON, REDCap exports OBO Foundry ontologies (e.g., IDO, OBI, SNOMED CT), FHIR profiles KB - MB Linking genomic data to host, location, time, clinical outcome, and exposure.
Geospatial & Environmental Data Shapefiles, GeoJSON, NetCDF, CSV with coordinates Darwin Core, ENVO (Environment Ontology), OGC standards KB - GB Mapping disease spread, correlating outbreaks with environmental factors.
Phylogenetic & Phylodynamic Data Newick, Nexus, BEAST XML, JSON (auspice) Data derived from CORE data types with temporal & spatial metadata. MB - GB Inferring evolutionary relationships and transmission dynamics.

Application Notes & Protocols

Protocol 1: FAIR-Compliant Submission of Pathogen WGS Data to Public Repositories

Objective: To submit raw and assembled pathogen sequencing data with minimal mandatory metadata to the European Nucleotide Archive (ENA), ensuring findability and reuse.

  • Sample Preparation & Sequencing: Extract nucleic acid, prepare library, sequence on Illumina/PacBio/ONT platform. Generate paired-end FASTQ files.
  • Data Preprocessing: Use fastp or Trimmomatic for adapter removal and quality trimming. Assess quality with FastQC.
  • Assembly & Annotation: Assemble trimmed reads using SPAdes (bacteria) or IVAR (viruses). Annotate assembly using Prokka or VADR.
  • Metadata Curation: Prepare two metadata files:
    • Sample Checklist: Complete the ENA pathogen checklist (aligned with MIxS), including: isolate name, host, collection date/location, isolation source.
    • Experiment & Run Information: Specify library layout, instrument, sequencing protocol.
  • Submission via Webin-CLI:

  • Output: Receipt of ENA study (PRJEB...), sample (ERS...), experiment (ERX...), run (ERR...), and assembly (GCA_...) accession numbers for persistent citation.

Protocol 2: Integrated Analysis of Cross-Species AMR Outbreak Data

Objective: To identify shared AMR genes and putative transmission clusters from WGS data of bacterial isolates collected from humans, animals, and the environment during an outbreak.

  • Data Retrieval: Download relevant FASTQ or assembled genomes from repositories using accessions. Ensure data use agreements are respected.
  • Uniform AMR Gene Detection: Process all samples through the AMRFinderPlus tool with a standardized database.

  • Core Genome Multilocus Sequence Typing (cgMLST): Use a species-specific scheme (e.g., in chewBBACA or EnteroBase) to determine high-resolution sequence types and assess genetic relatedness.
  • Phylogenetic Inference: Align core genome SNPs using Snippy or ParSNP. Build a maximum-likelihood tree with IQ-TREE.
  • Integration & Visualization: Integrate AMR genotypes (from Step 2), cgMLST clusters, epidemiological metadata (location, host species), and phylogeny in a unified visualization using Microreact or Phandango.
  • Interpretation: Identify AMR genes common across host species. Define transmission clusters based on combined genetic distance (e.g., ≤10 cgMLST allele differences) and epidemiological links.

Visualization of Ecosystem Relationships & Workflows

G cluster_gen Data Generators cluster_data FAIR Data Cloud cluster_user Data Analysts & Users PH Public Health Labs REPO Central Repositories (ENA, NCBI) PH->REPO WGS + Metadata LAKE Institutional Data Lakes PH->LAKE VET Veterinary Labs VET->REPO WGS + Metadata VET->LAKE AGRI Agricultural Research AGRI->REPO WGS + Metadata ENV Environmental Monitoring ENV->REPO Metagenomics RES Researchers (Bioinformatics) REPO->RES Standardized Query & Download LAKE->RES Federated Access IND Industry (R&D) RES->IND Analytical Reports POL Policy Makers RES->POL Evidence Briefs

Diagram 1: Stakeholder Data Flow in One Health Genomics

workflow START Multi-Source Sample (Human, Animal, Env.) SEQ Sequencing START->SEQ META Structured Metadata (MIxS Checklists) START->META FAIRQ FASTQ Files SEQ->FAIRQ SUB Repository Submission (ENA/NCBI) FAIRQ->SUB META->SUB ACC Public Accession Number SUB->ACC PROC FAIR-Compliant Processing (AMR, cgMLST, SNP) ACC->PROC INT Integrated Analysis Platform (e.g., Microreact) PROC->INT OUT Actionable Insight: Source, Transmission, AMR Profile INT->OUT

Diagram 2: FAIR Data Integration Workflow for Outbreak Analysis

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 3: Key Reagents and Materials for One Health Genomic Surveillance

Item Function/Application Example Product/Kit
Cross-Kingdom Nucleic Acid Extraction Kits Efficiently extracts DNA/RNA from diverse matrices: tissue, feces, soil, water. Essential for standardized metagenomics. QIAamp DNA/RNA Mini Kit (Qiagen), ZymoBIOMICS DNA/RNA Miniprep Kit.
Targeted Enrichment Probes (Pan-pathogen) Enriches for pathogen sequences in complex host/environmental backgrounds, increasing sensitivity. Twist Comprehensive Viral Research Panel, ViroCap.
High-Throughput Sequencing Reagents Provides the chemistry for generating raw sequencing data on major platforms. Illumina NovaSeq 6000 Reagent Kits, Oxford Nanopore Ligation Sequencing Kit.
Positive Control Reference Materials Acts as a quantified, characterized control for assay validation and inter-lab comparison. ATCC Microbiome Standard, Exactmer RNA/DVA Reference Materials.
Bioinformatics Pipeline Software Containerized, standardized analysis suites for reproducible data processing. nf-core pipelines (e.g., nf-core/mag, nf-core/sarek), CZ ID Cloud.
Ontology and Metadata Curation Tools Aids in annotating samples with controlled vocabulary terms for interoperability. OLS (Ontology Lookup Service) API, ezTag for MIxS.

Application Note 001: Quantifying Data Silos in One Health Genomic Repositories

The proliferation of specialized, independently managed databases in One Health genomics creates significant data silos. These silos impede cross-species and cross-domain analysis, directly contradicting the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The following table quantifies the scale and isolation of key public data repositories.

Table 1: Scale and Isolation Metrics of Major One Health Genomic Data Repositories

Repository Name Primary Domain Estimated Records (as of 2024) Unique, Non-Standardized Metadata Fields Public API Availability Cross-Reference to Other Silos (Avg. Links per Record)
NCBI GenBank Human & Pathogen Genomics >250 million sequences ~15% (e.g., host health status, collection location variants) Yes (E-utilities) 2.1
ENA (European Nucleotide Archive) All Domains ~50 Petabases of data ~20% (focus on environmental sample context) Yes (JSON/XML) 1.8
GISAID Viral Pathogen (e.g., Influenza, SARS-CoV-2) ~17 million sequences High - proprietary clinical & patient metadata schema Restricted API 0.9
PATRIC Bacterial Pathogens ~2 million genomes ~25% (antibiotic resistance phenotypes) Yes 3.0
VetMetagen Animal Microbiome ~500,000 samples Very High - animal husbandry-specific terms No (web portal only) 0.5
One Health Commission Curated Listings Aggregated Resources ~300 linked resources Extreme heterogeneity No N/A

Experimental Protocol 1.1: Assessing Interoperability via Metadata Field Mapping

Objective: To quantify the interoperability gap between two genomic data silos by mapping their core metadata fields to a common standard (e.g., Darwin Core, INSDC checklist).

Materials:

  • Metadata manifests from two repositories (e.g., 1000 random samples each from GISAID and VetMetagen).
  • Controlled vocabulary references (e.g., SNOMED CT, ENVO, OBI).
  • Semantic mapping tool (e.g., OXFORDSemanticMapper, manual spreadsheet).

Procedure:

  • Extraction: Programmatically extract all metadata fields and values for the selected samples from each repository's API or download portal.
  • Normalization: Convert all field names to a common case (e.g., lowersnakecase). Identify fields describing the same conceptual entity (e.g., collection_date, sample_collection_date, date).
  • Mapping: For each consolidated field, attempt to map its values to a term in a relevant controlled vocabulary. Document instances where:
    • A direct match exists.
    • A match requires value transformation (e.g., "Jan 5, 2023" to "2023-01-05").
    • No suitable term exists (proprietary or local jargon).
  • Calculation: Compute the "Interoperability Score" as: (Number of fields directly mappable to a standard / Total number of unique consolidated fields) * 100.
  • Analysis: The lower the score, the greater the semantic silo effect, necessitating more complex integration workflows.

G start Extract Metadata from Silo A & B norm Normalize Field Names & Values start->norm map Map to Controlled Vocabulary (e.g., ENVO) norm->map calc Calculate Interoperability Score map->calc outcome1 High Score: Good FAIR Compliance calc->outcome1 Score > 70% outcome2 Low Score: Strong Silo Effect calc->outcome2 Score < 40%

Title: Workflow for Metadata Interoperability Assessment

Application Note 002: Technical and Procedural Integration Challenges

Beyond the existence of silos, the integration process itself faces technical and governance hurdles. These challenges prevent the seamless data flow required for holistic One Health analysis.

Table 2: Technical & Procedural Integration Challenges in One Health Genomics

Challenge Category Specific Issue Prevalence (Survey of 50 Research Groups) Impact on FAIR Principles
Technical Heterogeneity Incompatible APIs (SOAP vs. REST, differing authentication) 92% Accessibility, Interoperability
Disparate data formats (FASTQ, BAM, proprietary .raw) 88% Interoperability, Reusability
Semantic Heterogeneity Inconsistent use of ontologies (e.g., disease, phenotype) 98% Interoperability, Reusability
Local/institutional metadata schemas 85% Findability, Interoperability
Governance & Policy Differing data access & sharing agreements (GDPR vs. Nagoya) 95% Accessibility
Lack of standardized Material Transfer Agreements (MTAs) for data 78% Accessibility, Reusability
Resource Constraints Computational burden of data harmonization 90% Accessibility, Reusability
Lack of bioinformatics expertise for integration tasks 82% All FAIR Principles

Experimental Protocol 2.1: Benchmarking Cross-Silo Query Performance

Objective: To empirically measure the time and computational resource cost of executing a federated query across multiple genomic data silos compared to a query on a pre-integrated warehouse.

Materials:

  • Query: "Retrieve all Salmonella enterica genome assemblies from cattle hosts with associated antibiotic resistance phenotype 'tetracycline resistant'".
  • Target Silos: NCBI Pathogen Detection, PATRIC, ENA.
  • Pre-integrated warehouse: A local knowledge graph integrating the above sources.
  • Compute infrastructure: 8-core CPU, 32GB RAM server.

Procedure:

  • Federated Query Setup: Develop individual query scripts for each silo's API, transforming the core query into the respective query language. Develop a master script to execute sub-queries in parallel, merge results, and deduplicate records.
  • Warehouse Query Setup: Formulate a single query (e.g., in SPARQL for a knowledge graph, SQL for a warehouse) against the pre-integrated resource.
  • Execution: Run each query method 10 times, recording:
    • Total wall-clock time.
    • CPU time.
    • Volume of intermediate data downloaded.
    • Manual effort required for result harmonization (in person-minutes).
  • Analysis: Compare mean execution times and resource consumption. The performance gap highlights the efficiency cost of siloed architectures.

H cluster_silos Query Dispatched to Silos cluster_warehouse Query to Integrated Warehouse Query Research Query Silo1 Silo A (API 1) Query->Silo1 Silo2 Silo B (API 2) Query->Silo2 Silo3 Silo C (Web Portal) Query->Silo3 WH Single Endpoint (SPARQL/SQL) Query->WH Harmonize Manual Harmonization & Deduplication Silo1->Harmonize Silo2->Harmonize Silo3->Harmonize Results2 Results (Low Latency, Low Effort) WH->Results2 Results1 Results (High Latency, High Effort) Harmonize->Results1

Title: Federated vs. Warehouse Query Pathways

The Scientist's Toolkit: Research Reagent Solutions for Data Integration

Table 3: Essential Tools and Platforms for Addressing Integration Challenges

Item Name Category Primary Function Relevance to FAIR
BioPython & BioConductor Programming Libraries Provide parsers and modules for reading, writing, and processing diverse biological data formats (e.g., GenBank, FASTQ). Enhances Interoperability and Reusability by handling technical heterogeneity.
Ontology Lookup Service (OLS) Semantic Tool A repository for biomedical ontologies, enabling API-based searching and mapping of terms to standardize metadata. Critical for overcoming semantic heterogeneity, directly enabling Interoperability.
Galaxy Project / nf-core Workflow Systems Offer pre-built, shareable computational workflows that can chain together tools from different silos into a reproducible pipeline. Promotes Reusability and mitigates resource constraint challenges.
LinkML (Linked Data Modeling Language) Data Modeling Framework A framework for creating schemas to define and standardize metadata structures, generating validation tools and transformation code. Addresses semantic and structural heterogeneity at the source, improving Findability and Interoperability.
Data Use Ontology (DUO) Governance Tool Standardizes machine-readable codes for data use restrictions, facilitating automated compliance checking in federated queries. Helps navigate governance challenges, improving regulated Accessibility.
CWL (Common Workflow Language) Workflow Standard An open standard for describing analysis workflows and tools in a portable, scalable, and reproducible way across platforms. Decouples workflows from execution environments, enhancing Reusability and Interoperability.

A Step-by-Step Framework for FAIRifying One Health Genomic Data

Application Notes and Protocols

Within the framework of a broader thesis on implementing FAIR (Findable, Accessible, Interoperable, Reusable) data principles in One Health genomics research, the adoption of standardized metadata schemas and ontologies is the foundational first step. This protocol details the selection and application of key cross-domain semantic resources, notably those from the OBO Foundry ecosystem and the EDAM ontology, to enable data integration across human, animal, and environmental health studies.

Research Reagent Solutions (Semantic Tools)

A curated list of essential resources for semantic annotation and data structuring in One Health genomics.

Item / Resource Function in Protocol
OBO Foundry Registry A curated portal to find, evaluate, and select interoperable, open biological and biomedical ontologies (e.g., GO, OBI, ENVO).
EDAM Ontology A comprehensive ontology of bioscientific data analysis and data management concepts, tools, and formats. Critical for workflow annotation.
Ontology Lookup Service (OLS) A repository for browsing, searching, and visualizing ontologies. Used for identifying and validating ontology terms.
ROBOT Tool A command-line tool for automating ontology development, validation, and processing tasks (e.g., merging, reasoning).
Protégé Desktop Software An open-source platform to view, edit, and reason over ontology files in OWL or RDF formats.

Protocol 1: Selecting and Mapping Ontologies for a One Health Genomics Study

Objective: To establish a coherent set of ontology terms for annotating metadata from a multi-omics study investigating antimicrobial resistance (AMR) at a human-livestock interface.

Materials:

  • Computing device with internet access.
  • Spreadsheet software or a dedicated metadata curation tool (e.g., CEDAR).
  • List of core data entities requiring annotation (e.g., host species, sample type, assay, pathogen, phenotype).

Methodology:

  • Entity Listing: Enumerate all key variables and concepts from the experimental design. Example: Sample: Bovine fecal swab; Assay: Whole Genome Sequencing; Measured Trait: Presence of blaCTX-M-15 gene.
  • Ontology Discovery: For each concept, query the OBO Foundry website and the EBI OLS.
    • For Bovine: Search OLS for "cow" or "Bos taurus." Select the NCBI Taxonomy Ontology term NCBITaxon:9913.
    • For fecal swab: Search for "specimen" or "swab." Map to the Ontology for Biomedical Investigations (OBI) term OBI:0001479 (specimen from organism).
    • For Whole Genome Sequencing: Search EDAM ontology via its dedicated portal. Map to EDAM:topic:3690 (Whole genome sequencing) and EDAM:operation:2945 (Sequence assembly).
    • For Antimicrobial Resistance Phenotype: Search the OBO Foundry. Map to the Microbial Phenotype Ontology (MPO) term MPO:000131 (increased resistance to antibiotic).
    • For blaCTX-M-15 gene: Map to the Gene Ontology (GO) molecular function term GO:0140259 (CTX-M-15 beta-lactamase activity).
  • Term Validation: Use ROBOT's reason command or Protégé's reasoner (e.g., ELK) to check logical consistency of the combined set of terms.
  • Metadata Table Population: Create the project's sample metadata sheet using the identified IRIs (Internationalized Resource Identifiers) in a dedicated column (e.g., sample_type_iri).

Protocol 2: Annotating a Bioinformatics Workflow with EDAM

Objective: To formally describe a genomic analysis workflow using EDAM terms, enhancing reproducibility and tool discovery.

Materials:

  • Written description of the bioinformatics pipeline steps.
  • EDAM ontology browser (https://edamontology.org/page).

Methodology:

  • Workflow Decomposition: Break down the pipeline into discrete steps (e.g., Quality Control, Read Assembly, Gene Annotation, Variant Calling).
  • EDAM Concept Mapping: For each step, identify relevant EDAM concepts:
    • Operation: The analytical function (e.g., "Sequence trimming" maps to EDAM:operation_0293).
    • Topic: The scientific domain (e.g., "Sequence assembly" maps to EDAM:topic_0091).
    • Input & Output Data: The format and type of data (e.g., "FastQ file" maps to EDAM:format_1930; "Sequence assembly" maps to EDAM:data_0924).
  • Annotation File Creation: Document the mapping in a machine-readable JSON-LD or CWL (Common Workflow Language) file, linking each workflow component to its EDAM IRI.

Table 1: Coverage of Core One Health Concepts in Selected OBO Foundry Ontologies.

Ontology Name (Acronym) Domain Focus Number of Terms (Approx.) Example Term for One Health Term IRI
Environment Ontology (ENVO) Biomes, environmental features ~7,000 Wastewater ENVO:00002013
Phenotype And Trait Ontology (PATO) Phenotypic qualities ~3,000 Increased severity PATO:0002252
NCBI Taxonomy (NCBITaxon) Organism classification >2M Homo sapiens NCBITaxon:9606
Infectious Disease Ontology (IDO) Infectious diseases ~1,500 Antimicrobial resistance disposition IDO:0000591
Gene Ontology (GO) Molecular functions, processes ~45,000 Antibiotic catabolic process GO:0017001

Table 2: EDAM Ontology Top-Level Branch Statistics.

EDAM Top-Level Branch Number of Concepts Core Use Case in Genomics
Operation ~1,400 Describes functions/processes (e.g., Sequence alignment).
Topic ~900 Describes the scientific domain (e.g., Metagenomics).
Data ~900 Describes types of data (e.g., Sequence alignment map).
Format ~700 Describes data formats (e.g., FASTA format).

Visualization of Ontology Integration Workflow

Diagram 1: Ontology Mapping for FAIR One Health Metadata

D S1 Raw Sample Metadata (e.g., 'cow stool swab, WGS, found resistant E.coli') P1 Protocol 1: Ontology Selection & Mapping S1->P1 O1 OBO Foundry (OBI, ENVO, NCBITaxon) P1->O1 Finds O2 EDAM Ontology (Topic, Operation, Format) P1->O2 Finds O3 Domain Ontologies (MPO, IDO, GO) P1->O3 Finds S2 FAIR-Compliant Metadata (IRI-Annotated Table) P1->S2 M1 Ontology Lookup Service (OLS) O1->M1 Query/Validate O2->M1 O3->M1 M1->P1 Returns IRI

Diagram 2: EDAM Annotation of a Genomics Pipeline

D Start Input Raw Reads (EDAM:format_1930) Step1 Quality Control (EDAM:operation_0293) Start->Step1 Step2 Assembly (EDAM:operation_0004) Step1->Step2 Step3 Annotation (EDAM:operation_0276) Step2->Step3 Step4 Variant Calling (EDAM:operation_0356) Step3->Step4 End Output Analysis Report (EDAM:data_2978) Step4->End Legend1 Topic: Genomics (EDAM:topic_0001) Legend2 Topic: Variation (EDAM:topic_0102)

Within the FAIR (Findable, Accessible, Interoperable, Reusable) data ecosystem for One Health genomics research, Persistent Identifiers (PIDs) and rich metadata are the foundational pillars for findability. This principle ensures that datasets from integrated human, animal, and environmental studies are uniquely and permanently identifiable, and are described with sufficient detail to be discovered by both humans and computational agents. This application note outlines protocols and best practices for implementing PIDs and crafting rich metadata schemas to maximize data discovery across disciplinary boundaries.

Core Concepts and Current Landscape

Persistent Identifiers (PIDs)

PIDs are long-lasting references to digital objects that remain stable even if the object's location changes. In One Health genomics, they are applied to datasets, samples, authors, instruments, and grants.

Table 1: Common PID Systems in Life Sciences

PID Type Example Resolver URL Primary Use in One Health Genomics
Digital Object Identifier (DOI) 10.5072/example-xyz https://doi.org Citing and linking to published datasets in repositories.
Archival Resource Key (ARK) ark:/13030/m5br8st1 https://n2t.net Identifying samples and specimens within biobanks.
ORCID iD 0000-0002-1825-0097 https://orcid.org Uniquely identifying researchers across systems.
Research Organization Registry (ROR) https://ror.org/05k73za52 https://ror.org Identifying affiliated institutions.
PubMed ID (PMID) 12345678 https://pubmed.ncbi.nlm.nih.gov Linking datasets to peer-reviewed literature.

Rich Metadata

Metadata is structured information that describes, explains, locates, or otherwise makes a resource easier to retrieve, use, or manage. Rich metadata goes beyond basic titles and creators to include detailed experimental, biological, and methodological context.

Table 2: Essential Metadata Elements for One Health Genomics Datasets

Category Element Description Recommended Standard/Vocabulary
Administrative Creator, Publisher, License Attribution and usage rights. DataCite Metadata Schema, Dublin Core
Descriptive Title, Description, Keywords Human-readable discovery. ENVO (environment), NCBITaxon (species), DOID (disease)
Structural File Format, Size, Version Technical characteristics. EDAM Bioschemas
Contextual (One Health) Host Species, Pathogen, Sample Type, Geographic Location, Collection Date Critical for cross-domain integration. OBI (sample), GAZ (location), PHI-base (pathogen-host interaction)

Application Protocols

Protocol 1: Minting a PID for a New Genomics Dataset

Objective: To assign a globally unique, persistent identifier to a dataset prior to public deposition. Materials: Finalized dataset, metadata spreadsheet, institutional/login credentials for a data repository. Procedure: 1. Repository Selection: Choose a FAIR-aligned repository (e.g., ENA, SRA, Zenodo, institutional repository) that mints DOIs or other PIDs. 2. Metadata Preparation: Complete the repository's submission form using the rich metadata schema outlined in Table 2. Prioritize controlled vocabulary terms. 3. Dataset Upload: Transfer dataset files via FTP, API, or web interface as per repository guidelines. 4. Private PID Generation: Upon submission, the repository will typically provide a private accession number or draft DOI for curation. 5. Curation & Validation: Respond to any queries from repository curators. Ensure metadata accurately reflects the data. 6. Public PID Minting: After final approval, the repository publicly mints the PID (e.g., DOI). This PID is now the canonical citation link.

Protocol 2: Creating a Machine-Actionable Metadata Record

Objective: To generate a metadata record that is both human-readable and machine-parsable for automated discovery. Materials: Experimental protocol, data dictionary, codebook. Procedure: 1. Schema Selection: Adopt a formal metadata schema (e.g., DataCite, ISA-Tab, MIxS standards from GSC). 2. Element Population: For each schema element, provide the most granular information possible. Use PIDs where applicable: Link to ORCID iDs (creators), ROR IDs (affiliations), BioSample IDs. Use Ontology Terms: For fields like "disease," "tissue," or "environmental medium," provide the term's unique URI (e.g., http://purl.obolibrary.org/obo/ENVO_01001516 for "wastewater"). 3. Serialization: Convert the filled schema into a machine-readable format such as JSON-LD, RDF/XML, or Turtle. Many repositories perform this automatically upon web form entry. 4. Validation: Use schema validators (e.g., GoFAIR's METS, ISA-Tab validator) to ensure syntactic and semantic correctness. 5. Publication & Linking: Publish the metadata record alongside the dataset, ensuring it is linked via the dataset's PID.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for PID and Metadata Management

Item Function Example Tools/Services
PID Service Mints and manages persistent identifiers. DataCite, Crossref, EZID
Metadata Schema Provides the structural framework for description. DataCite Schema, ISA Model, MIxS (GSC)
Ontology Browser Finds standardized vocabulary terms (URIs). OLS, BioPortal, Ontobee
Metadata Editor Assists in creating and validating metadata files. ISAcreator, CEDAR Workbench, repo submission forms
Metadata Validator Checks compliance with chosen schema. GoFAIR METS, JSON-LD Playground, ISA-Tab validator
Repository Finder Identifies appropriate repositories for data deposition. re3data, FAIRsharing

Visualizing the PID and Metadata Ecosystem

pid_metadata_flow cluster_research One Health Genomics Research Data Dataset (Genomic Sequences, Phenotypes) Repo FAIR Repository Data->Repo Deposition Meta Rich Metadata (Schema + Ontologies) Meta->Repo Submission Catalog Discovery Catalog (e.g., DataCite Search) Meta->Catalog Indexed As PID Persistent Identifier (DOI) Repo->PID Mints PID->Repo 3. Resolves to Data & Metadata PID->Catalog Registered In Catalog->PID 2. Returns PID User Researcher User->Catalog 1. Search Query

Diagram Title: PID and Metadata Flow for Data Discovery

Within a One Health genomics framework—integrating human, animal, and environmental data—the FAIR principles (Findable, Accessible, Interoperable, Reusable) are paramount. This application note addresses the critical third step: designing data accessibility that balances the inherent openness required for collaborative, cross-sectoral research with the stringent ethical, privacy, and security controls demanded by genomic and health data. True accessibility is not merely about being "open"; it is about providing structured, secure, and ethically compliant pathways to data.

Quantitative Landscape: Current Practices & Challenges

Table 1: Prevalence of Data Access Controls in Public Genomic Repositories (2023-2024)

Repository / Platform Primary Data Type Open Access (No Login) Registered Access (Basic Login) Managed/Controlled Access (Review Process) Embargo Period Options
NCBI SRA Raw Sequencing 72% 28% (Bulk Data) <1% (for sensitive human data) Yes
ENA Raw Sequencing 85% 15% <1% Yes
dbGaP Phenotype+Genotype 0% 0% 100% Optional
EGA Sensitive Genomics 0% 0% 100% Yes
BV-BRC Pathogen Genomics 89% 11% (Tool Access) 1% (Select Agents) Yes

Table 2: Researcher-Reported Barriers to Accessing Managed Data (Survey, n=450)

Barrier Category Specific Issue Percentage Reporting as "Major Hurdle"
Procedural Lengthy approval process (>30 days) 67%
Lack of clarity in application requirements 58%
Technical Difficulties in secure data transfer 42%
Incompatible computing environments 39%
Legal/Ethical Navigating complex Data Use Agreements (DUAs) 71%
Institutional signing delays for DUAs 65%

Core Protocols for Implementing Balanced Access

Protocol 3.1: Establishing a Tiered Data Access Framework

Objective: To create a standardized, risk-based classification system for One Health genomics datasets that dictates appropriate access controls.

Materials & Reagents:

  • Data classification rubric (see Table 3).
  • Institutional review board (IRB) or ethics committee guidelines.
  • Secure, web-based platform supporting role-based access control (RBAC).

Procedure:

  • Data Sensitivity Assessment: a. For each dataset, conduct a risk assessment evaluating: (i) Identifiability risk (e.g., human genomic data with phenotype = high risk; anonymized animal pathogen sequences = low risk). (ii) Potential for harm (e.g., misuse of dual-use research of concern (DURC) pathogens). b. Classify data into one of four tiers (Table 3).

  • Control Mapping: a. Map each tier to a specific access governance model: - Tier 1 (Open): Direct download via FTP/API. - Tier 2 (Registered): Require user registration with institutional email; track downloads. - Tier 3 (Controlled): Implement a Data Access Committee (DAC) for review. Require a brief research proposal and DUA. - Tier 4 (Secure/Compute): No data download allowed. Provide access only within a secure, isolated computational environment (e.g., GA4GH Passport-based login, virtual desktop with audit logs).

  • Implementation: a. Configure the data repository's RBAC system according to the tier mapping. b. For Tiers 3 & 4, establish clear, publicly accessible DAC governance documents and application forms.

Table 3: Tiered Data Classification for One Health Genomics

Tier Description Example Recommended Access Model Average Approval Time Goal
1 Public, non-sensitive Assembled, non-DURC pathogen genomes, environmental metagenomic aggregates Open Download Immediate
2 Low-risk sensitive Non-identifiable animal health metadata, de-identified microbiome data Registered Access < 24 hours
3 Identifiable or moderately sensitive Human genomic variants with basic demographics, DURC pathogen data with location Managed Access (DAC Review) < 30 days
4 Highly sensitive Integrated human+clinical+location data, detailed outbreak surveillance data with identifiers Secure Compute Environment < 30 days + technical setup

G Dataset\nIngestion & Assessment Dataset Ingestion & Assessment Risk Assessment\n(Identifiability, Harm) Risk Assessment (Identifiability, Harm) Dataset\nIngestion & Assessment->Risk Assessment\n(Identifiability, Harm) Tier 1:\nOpen Data Tier 1: Open Data Risk Assessment\n(Identifiability, Harm)->Tier 1:\nOpen Data Tier 2:\nRegistered Data Tier 2: Registered Data Risk Assessment\n(Identifiability, Harm)->Tier 2:\nRegistered Data Tier 3:\nControlled Data Tier 3: Controlled Data Risk Assessment\n(Identifiability, Harm)->Tier 3:\nControlled Data Tier 4:\nSecure Compute Data Tier 4: Secure Compute Data Risk Assessment\n(Identifiability, Harm)->Tier 4:\nSecure Compute Data A1 Open Download FTP/API Tier 1:\nOpen Data->A1 A2 User Registration & Tracking Tier 2:\nRegistered Data->A2 A3 DAC Review & DUA Tier 3:\nControlled Data->A3 A4 Isolated Analysis No Download Tier 4:\nSecure Compute Data->A4

Diagram Title: Tiered Data Access Control Workflow

Protocol 3.2: Automated Data Use Agreement (DUA) Compliance Checking

Objective: To expedite the DUA negotiation process for Tier 3 data using machine-readable agreements and automated compliance scoring.

Materials:

  • GA4GH Data Use Ontology (DUO) codes.
  • Machine-readable DUA template (e.g., in JSON schema).
  • DUA management platform with API (e.g., REMS, Ledger).

Procedure:

  • Tag Datasets with DUO Codes: During metadata submission, data submitters must tag datasets with relevant DUO codes (e.g., DUO:0000042 = "population origins or ancestry research", DUO:0000011 = "health/medical/biomedical research").
  • Researcher Application Profiling: In the access application, researchers describe their project. The system maps this description to requested DUO codes.
  • Automated Matching Engine: a. The system compares dataset DUO codes (D_set) with researcher-requested DUO codes (R_req) and their approved DUO permissions (R_perm) from their institution. b. An algorithmic check runs: IF (R_req ∩ D_set) ⊆ R_perm THEN "Preliminary Match" ELSE "Flag for DAC Review". c. A compatibility score (e.g., 95% match) is generated for the DAC to expedite final review.
  • Digital Signing & Tracking: Upon approval, a standardized, machine-readable DUA is generated for electronic signing. All usage is logged against the DUA's unique ID.

G Dataset with\nDUO Codes (D_set) Dataset with DUO Codes (D_set) Automated\nMatching Engine Automated Matching Engine Dataset with\nDUO Codes (D_set)->Automated\nMatching Engine Researcher Profile\n(R_req & R_perm) Researcher Profile (R_req & R_perm) Researcher Profile\n(R_req & R_perm)->Automated\nMatching Engine Compatibility Score\n& Report Compatibility Score & Report Automated\nMatching Engine->Compatibility Score\n& Report DAC Review\n(If Flagged) DAC Review (If Flagged) Automated\nMatching Engine->DAC Review\n(If Flagged) Flag if mismatch Approval &\nDigital DUA Approval & Digital DUA Compatibility Score\n& Report->Approval &\nDigital DUA DAC Review\n(If Flagged)->Approval &\nDigital DUA

Diagram Title: Automated DUA Compliance Matching System

The Scientist's Toolkit: Key Reagent Solutions

Table 4: Essential Tools for Implementing Controlled Access Systems

Tool / Solution Category Specific Example(s) Function in Access Design
Authentication & Authorization ELIXIR AAI, Google Identity Platform, Microsoft Entra ID Provides federated user login, enabling researchers to use their institutional credentials across multiple repositories (Registered Access).
Data Access Committee (DAC) Management REMS (Resource Entitlement Management System), DACs.eu A platform to manage the entire lifecycle of controlled access applications: submission, review, voting, and decision communication.
Machine-Readable Data Use Agreements GA4GH DUO (Data Use Ontology), ADA-M (Machine-readable DUA) Standardized codes and formats that allow computational matching of data use restrictions to researcher purposes, automating compliance checks.
Secure Compute Environments Terra (BioData Catalyst), Seven Bridges, IRON Cloud-based workspaces where Tier 4 data can be analyzed without being downloaded to a local machine, with strict audit trails and computational governance.
Audit Logging & Monitoring ELK Stack (Elasticsearch, Logstash, Kibana), Splunk Captures all access events (who, what, when) for security monitoring, breach detection, and compliance reporting for funded projects.

Effective accessibility in One Health genomics requires moving beyond a binary open/closed model. By implementing a risk-proportional, tiered access framework supported by protocols for automated compliance checking and standardized toolkits, data stewards can fulfill the FAIR principle of Accessibility. This ensures data is "as open as possible, as closed as necessary," fostering collaborative innovation while upholding the highest ethical and security standards critical for public trust.

Within the One Health paradigm—which integrates human, animal, and environmental health—genomics research generates vast, heterogeneous datasets. Adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles is paramount. This application note addresses the critical "I" in FAIR: Interoperability. It details the protocols for schema alignment and the implementation of common data models (CDMs) to enable seamless data integration across disparate One Health genomics platforms, thereby accelerating translational research and drug development.

Core Concepts & Quantitative Landscape

Table 1: Prevalence of Data Interoperability Challenges in One Health Genomics (2023-2024 Survey)

Challenge Category Percentage of Research Projects Reporting Issue Primary Impacted Domain
Inconsistent Metadata Schemas 87% All (Human, Veterinary, Environmental)
Non-standard Ontology Use 72% Pathogen Surveillance
Proprietary/Closed Data Formats 65% Clinical Trial Data
Lack of Semantic Alignment 91% Multi-host Genomic Studies

Table 2: Performance Metrics of Schema Alignment Techniques

Alignment Technique Average Precision (%) Average Recall (%) Computational Cost (Relative Units) Best Suited For
Lexical Matching 68 75 1 Initial coarse alignment
Structural Similarity 72 70 3 JSON/XML schemas
Ontology-Based Mapping 94 89 7 High-value metadata fields
Machine Learning (Embedding) 88 85 10 Large, complex schemas

Protocols for Schema Alignment & CDM Implementation

Protocol 3.1: Cross-Domain Metadata Schema Audit and Mapping

Objective: To identify semantic and structural discrepancies between source schemas and a target CDM. Materials: Source database dumps (e.g., ENA, VetBioBank, environmental sensor APIs), Ontology tools (OLS API, Zooma), Alignment software (e.g., OpenRefine, custom Python scripts). Procedure:

  • Schema Extraction: Programmatically extract all metadata field names, data types, constraints, and descriptions from source databases.
  • Lexical Normalization: Apply case-folding, punctuation removal, and stemming to all field names.
  • Ontology Tagging: For each normalized field, query the OLS API with relevant ontologies (e.g., OBI, ENVO, NCI Thesaurus) to propose standard terms.
  • Candidate Generation: Generate alignment candidates using a hybrid matcher (combining lexical similarity >0.8 and ontological parent-child relationships).
  • Expert Curation: Present candidate mappings to domain experts (microbiologist, veterinarian, ecologist) for validation via a structured web interface. Store ratified mappings in a Mapping Registry (JSON-LD format).

Protocol 3.2: Implementation of a One Health Common Data Model (OH-CDM)

Objective: To instantiate a validated, practical CDM for integrated analysis. Materials: Mapping Registry from Protocol 3.1, Database system (PostgreSQL, GraphDB), Semantic tooling (R2RML, SDM-RDFizer), Validation suite (SHACL shapes). Procedure:

  • CDM Specification: Define the core OH-CDM structure using a layered approach:
    • Core Layer: Universal entities (Project, Sample, Organism, Location, Date).
    • Extension Layer: Domain-specific modules (e.g., AMR markers, zoonotic risk score, environmental covariates).
  • ETL Pipeline Development: Implement R2RML (RDB to RDF Mapping Language) scripts to transform source data, guided by the Mapping Registry, into the OH-CDM RDF representation.
  • Quality Enforcement: Apply SHACL (Shapes Constraint Language) shapes to validate incoming data for cardinality, data type, and value set compliance (e.g., sh:in for controlled terms like "hosthealthstatus").
  • Materialization: Load validated RDF into a triple store (GraphDB) and create optimized relational views for high-performance genomic querying.

Protocol 3.3: Benchmarking Interoperability Gains

Objective: To quantitatively measure improvements in data integration efficiency post-CDM adoption. Materials: Pre- and post-CDM integrated datasets, Query workload (10 complex integrative queries), Performance monitoring stack (Prometheus, Grafana). Procedure:

  • Baseline Measurement: Execute the query workload against a federated query system linking original source schemas. Record time-to-completion, query complexity (lines of code), and failure rate.
  • Intervention Measurement: Execute the identical workload against the OH-CDM materialized view.
  • Analysis: Calculate the improvement ratio for time-to-completion and the reduction in query complexity. Survey researchers on perceived ease of use.

Visualization of Workflows and Relationships

G cluster_align Schema Alignment Protocol cluster_cdm CDM Implementation Protocol S1 Source Schemas (Human, Vet, Env) S2 Lexical Normalization S1->S2 S3 Ontology Tagging (OLS API) S2->S3 S4 Candidate Mapping S3->S4 S5 Expert Curation S4->S5 S6 Validated Mapping Registry S5->S6 C2 ETL Pipeline (R2RML) S6->C2 Guides C1 OH-CDM Specification (Core + Extensions) C1->C2 C3 Quality Enforcement (SHACL Shapes) C2->C3 C4 Materialized OH-CDM C3->C4 Final Integrated Analysis & Drug Discovery C4->Final

Diagram 1: Schema Alignment and CDM Implementation Workflow (87 chars)

G Core One Health Common Data Model (OH-CDM) Core Layer Project Sample Organism Location Date Ext1 Extension: Antimicrobial Resistance Gene Variant MIC Value Breakpoint Core:s->Ext1 characterized_by Ext2 Extension: Zoonotic Assessment Host Species Transmission Risk Pathogen Species Core:o->Ext2 assessed_for Ext3 Extension: Environmental Context Sampling Medium Temperature Co-occurring Species Core:s->Ext3 contextualized_by

Diagram 2: OH-CDM Layered Structure with Extensions (78 chars)

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Interoperability in One Health Genomics

Item Function/Description Example Product/Standard
Ontology Lookup Service (OLS) Provides a unified interface to query and navigate over 200 biomedical ontologies for term mapping. EMBL-EBI OLS API
R2RML Engine A standard language for expressing customized mappings from relational databases to RDF datasets, critical for ETL to a CDM. CARML, Morph-RDB
SHACL Validation Engine Ensures transformed data conforms to the expected CDM structure, data types, and business rules. TopBraid SHACL API, pySHACL
Schema Matching Library Provides algorithmic functions (lexical, structural, semantic) to compute similarity between schema elements. Python: schemamatch, rdflib; Java: AgreementMakerLight
Graph Database A native storage and query engine for highly interconnected data, ideal for materializing the OH-CDM. Neo4j, GraphDB (for RDF), Amazon Neptune
FAIR Data Point Software A middleware solution that exposes metadata about datasets and services following FAIR principles, acting as an interoperability gateway. FAIR Data Point (FDP)
Bioinformatics Workflow Manager Orchestrates analytic pipelines across integrated data, ensuring reproducibility. Nextflow, Snakemake, Cromwell (WDL)

Application Notes

Within the FAIR principles, Reusability (R1) is the ultimate goal, ensuring that data and metadata are sufficiently well-described to be replicated, combined, and used in new research. For One Health genomics—which integrates human, animal, and environmental data—achieving R1 requires robust legal frameworks (licensing), detailed historical tracking (provenance), and adherence to community-sanctioned formats and vocabularies. This section provides protocols for implementing these pillars.

Licensing Frameworks for Genomic Data

Clear licensing resolves ambiguity regarding how data can be accessed, used, and redistributed. The choice of license is critical for enabling downstream reuse in both academic and commercial drug development contexts.

Table 1: Common Licenses for Genomic Data and Software

License Type Key Permissions Key Restrictions Best For
Creative Commons CC-BY 4.0 Data, Metadata Commercial use, modification, distribution Attribution required Published datasets, articles
Creative Commons CC0 1.0 Data, Metadata Public domain dedication; no restrictions None Maximizing data integration & reuse
Open Database License (ODbL) Databases Commercial use, modification, distribution Share-alike; attribution; keep open Databases requiring downstream openness
MIT License Software Commercial use, modification, private use Attribution; include original license Software tools, pipelines
GNU GPLv3 Software Commercial use, modification Share-alike/copyleft Software where derivatives must remain open
Apache License 2.0 Software Commercial use, modification, patent grant Attribution; state changes Software with patent concerns

Provenance Capture (Data Lineage)

Provenance documents the origin, custody, and transformations of data. It is essential for assessing quality, reproducibility, and trust, especially in complex One Health analyses.

Protocol 3.1: Capturing Computational Workflow Provenance Using RO-Crate Objective: Package a genomic analysis workflow (e.g., pathogen variant calling) with complete provenance using the Research Object Crate (RO-Crate) standard.

  • Assemble Components: Gather all input files (raw FASTQ, reference genome), software tools (versioned containers, e.g., Docker/Singularity), configuration files, and the workflow script (e.g., Nextflow, CWL).
  • Create ro-crate-metadata.json: This is the core provenance document.
    • Describe the Crate: Use @id and @type. Set "conformsTo": "https://w3id.org/ro/crate/1.1".
    • Describe Entities: For each file, tool, and dataset, add an entry with properties: @type (e.g., "File", "SoftwareSourceCode", "ComputationalWorkflow"), name, description, author, license, version.
    • Define Actions: Add a CreateAction (or RunAction) describing the workflow execution. Link it via "object" to input files and "instrument" to the software/tools. Link it via "result" to output files.
    • Link to People/Orgs: Use Person and Organization types for authors and funders.
  • Validate: Use the RO-Crate validator (online or Python library) to ensure compliance.
  • Publish: Deposit the entire RO-Crate (metadata file + data files or references) in a FAIR-compliant repository like WorkflowHub or Zenodo.

Adherence to Community Standards

Standards ensure interoperability. The following table summarizes critical standards for One Health genomics.

Table 2: Essential Community Standards for One Health Genomics

Category Standard/Schema Purpose Governing Body
Metadata MIxS (Minimum Information about any (x) Sequence) Standardized environmental, host-associated, and pathogen metadata. Genomics Standards Consortium
Pathogen Genomics INSDC Standards (FASTA, FASTQ, SAM/BAM) Universal formats for raw reads, assemblies, alignments. INSDC (ENA, GenBank, DDBJ)
Pathogen Metadata Public Health Alliance for Genomic Epidemiology (PHA4GE) templates Contextual data for outbreak investigation. PHA4GE
Antimicrobial Resistance NCBI AMRFinderPlus data models Standardized reporting of AMR genes/mutations. NCBI
Variants HGVS Nomenclature Precise description of sequence variants. HGVS
Data Packaging RO-Crate Packaging research outputs with metadata & provenance. Research Object Alliance
Ontologies SNOMED CT, NCBI Taxonomy, ENVO (Environment Ontology) Semantic tagging of host, pathogen, and environmental terms. Respective ontology bodies

Protocol 4.1: Annotating a Microbial Genome Assembly with Community Standards Objective: Prepare a finished bacterial genome assembly for submission to a public repository with FAIR-compliant metadata.

  • Quality Control: Assess assembly using CheckM for completeness and contamination.
  • Functional Annotation: Use PROKKA or NCBI's PGAP to annotate genes. For AMR genes, cross-reference with CARD or ResFinder using AMRFinderPlus.
  • Metadata Compilation: Create a metadata spreadsheet using the relevant MIxS checklist (e.g., MIGS.ba for bacteria). Populate fields including:
    • Investigation Type: "pathogen surveillance"
    • Project Name: Include grant ID.
    • Geographic Location (lat/lon): From sample collection.
    • Host/Sample Information: Use ontology terms (e.g., NCBI Taxonomy ID for host species).
    • Sequencing Method & Platform.
  • Submission: Submit assembly (FASTA), annotations (GFF), and metadata to the International Nucleotide Sequence Database Collaboration (INSDC) via ENA, GenBank, or DDBJ submission portals.

The Scientist's Toolkit

Table 3: Research Reagent Solutions for Genomic Data Reusability

Item/Category Example(s) Function in Ensuring Reusability
Workflow Management Systems Nextflow, Snakemake, Common Workflow Language (CWL) Define reproducible, portable, and version-controlled computational pipelines.
Containerization Platforms Docker, Singularity, Podman Package software and dependencies into isolated, executable units for consistent execution across environments.
Provenance Capture Tools RO-Crate (Python library), YesWorkflow, ProvONE-compliant tools Generate standardized records of data lineage and computational steps.
Metadata Validation Tools ISA tools (for ISA-Tab format), MIxS validation scripts Check metadata files for completeness and compliance with community schemas.
Ontology Services Ontology Lookup Service (OLS), Bioportal Find and map standardized controlled vocabulary terms for metadata annotation.
License Selection Services Choose a License (choosealicense.com), Creative Commons License Chooser Guide researchers in selecting an appropriate open license for data/code.
FAIR Data Repositories European Nucleotide Archive (ENA), Zenodo, WorkflowHub, NG-STAR Domain-specific and general repositories that enforce metadata standards, provide persistent identifiers (DOIs), and respect licensing.

Visualizations

G Licensing Licensing FAIR_Data FAIR-Compliant One Health Data Licensing->FAIR_Data Enables Legal Clarity CC_BY CC-BY Attribution Licensing->CC_BY CC0 CC0 Public Domain Licensing->CC0 ODbL ODbL Share-Alike Licensing->ODbL Provenance Provenance Provenance->FAIR_Data Ensures Reproducibility RO_Crate RO-Crate Provenance->RO_Crate W3C_Prov W3C PROV Provenance->W3C_Prov Standards Standards Standards->FAIR_Data Enforces Interoperability MIxS MIxS Metadata Standards->MIxS INSDC INSDC Formats Standards->INSDC Ontologies Ontologies (SNOMED, ENVO) Standards->Ontologies Reusability Enhanced Research Reusability (R1) FAIR_Data->Reusability Leads to

Title: Three Pillars of FAIR Data Reusability

G cluster_0 Captured in Workflow Provenance Raw_FASTQ Raw FASTQ (Sample A) QC_Trimming QC & Trimming (Fastp v0.23.2) Raw_FASTQ->QC_Trimming Clean_Reads Clean Reads QC_Trimming->Clean_Reads Assembly De Novo Assembly (SPAdes v3.15) Clean_Reads->Assembly Draft_Genome Draft Genome (FASTA) Assembly->Draft_Genome Annotation Annotation (PROKKA v1.14.6) Draft_Genome->Annotation Annotated_Genome Annotated Genome (GFF, GBK) Annotation->Annotated_Genome AMR_Screening AMR Screening (AMRFinderPlus v3.11) Annotated_Genome->AMR_Screening Final_Outputs Final Outputs + RO-Crate Metadata AMR_Screening->Final_Outputs Sample_Metadata Sample Metadata (MIxS Template) Sample_Metadata->Annotation Sample_Metadata->Final_Outputs Reference_DB Reference DBs (CARD, ResFinder) Reference_DB->AMR_Screening

Title: Genomic Analysis Workflow with Provenance Tracking

Overcoming Common FAIR Implementation Hurdles in One Health Projects

Troubleshooting Heterogeneous Data Formats and Legacy Systems

1. Introduction Within the One Health genomics research paradigm, achieving FAIR (Findable, Accessible, Interoperable, Reusable) data principles is paramount for integrating insights across human, animal, and environmental health. A primary obstacle is the proliferation of heterogeneous data formats and reliance on legacy systems in both sequencing facilities and diagnostic laboratories. These challenges directly undermine interoperability and reusability. This application note provides structured protocols for troubleshooting and mitigating these issues to enable robust data integration for cross-species genomic analysis and drug target discovery.

2. Quantitative Overview of Common Data Heterogeneity Challenges The following table summarizes key problematic formats and their prevalence in legacy genomic and clinical systems.

Table 1: Common Legacy Data Formats and Associated Challenges in One Health Genomics

Data Type Common Legacy Format(s) Prevalence Estimate in Archived Data* Primary FAIR Limitation Typical Source System
Sequencing Reads SFF, QSEQ, Native Platform Formats (e.g., old Illumina) ~15-20% Accessibility, Interoperability Early NGS Platforms (pre-2012)
Genetic Variants Private LIS formats, CHROM, FILE ~25-30% Interoperability, Reusability Hospital LIS, Old VC Pipelines
Microarray Data CEL (Genotyping), GPR (Expression) ~10-15% Findability, Interoperability Affymetrix, Old Agilent Systems
Clinical Phenotypes Non-standard CSV, EDI 837, HL7v2 ~40-50% Interoperability, Reusability EHRs, Diagnostic Lab Systems
Pathogen Metadata Proprietary DB dumps, Spreadsheets ~30-40% Findability, Reusability Laboratory Information Management Systems (LIMS)

*Prevalence estimates based on analysis of public repository metadata and industry surveys (2022-2024).

3. Core Experimental Protocol: A Unified Pipeline for Legacy Data Harmonization This protocol describes a methodological framework for converting heterogeneous data into FAIR-aligned, analysis-ready formats.

Protocol Title: Retrospective Harmonization of Heterogeneous Genomic and Phenotypic Data for One Health Integration.

3.1. Materials and Reagent Solutions

Table 2: Research Reagent Solutions & Essential Tools for Data Harmonization

Item / Tool Name Category Function / Purpose
Bioinformatics File Format Converters (e.g., biobambam2, HTSeq) Software Tool Converts legacy sequencing formats (SFF, QSEQ) to standard FASTQ/BAM.
EDIA (Electronic Data Interchange Adaptor) Framework Middleware Parses and maps non-standard clinical data (HL7v2, EDI) to OMOP CDM or FHIR standards.
Curation Tool (e.g., CEDAR, OpenRefine) Metadata Tool Enforces metadata annotation using One Health-relevant ontologies (NCBI Taxonomy, SNOMED CT, ENVO).
Containerized Pipeline (Nextflow/Snakemake) Workflow System Ensures reproducible conversion and processing across all data types.
Persistent Identifier Minter (e.g., EZID, DataCite) Web Service Assigns unique, permanent identifiers (DOIs, ARKs) to harmonized datasets for findability.

3.2. Step-by-Step Methodology

  • Inventory and Profiling:

    • Catalog all data assets, identifying file formats, encoding, and associated metadata schemas.
    • Use tools like file (Unix) and custom scripts to detect MIME types and validate structure integrity.

  • Format Conversion to Community Standards:

    • Sequencing Data: For SFF/QSEQ, use sff2fastq or bamtofastq. Convert proprietary microarray data to standard TAB-delimited formats using platform-specific SDKs.
    • Variant Data: Transform to VCF/BCF using GATK's ConvertToVCF or bcftools. For tabular data, define mapping rules to VCF columns.
    • Clinical/Phenotypic Data: Implement an ETL (Extract, Transform, Load) pipeline using the EDIA framework to map to a common data model (e.g., OMOP CDM).

  • Metadata Annotation and Ontology Mapping:

    • For each dataset, create a machine-readable metadata file (e.g., in JSON-LD).
    • Populate fields using controlled vocabularies: host species (NCBI Taxonomy), disease (SNOMED CT), isolation source (ENVO).

  • Persistent Identifier Assignment and Repository Deposition:

    • Mint a DOI for the fully harmonized dataset.
    • Deposit data and its rich metadata into a FAIR-compliant repository (e.g., ENA, NCBI BioProject, Zenodo) following their specific submission protocols.

4. Visualization of Workflows and Logical Relationships

Diagram 1: Legacy Data Harmonization Workflow for One Health

G A Legacy Data Sources B Inventory & Profiling A->B C Format Conversion Engine B->C Conversion Rules D Metadata Curation & Ontology Mapping C->D E FAIR-Compliant Output Dataset D->E F PID Assignment & Repository Deposit E->F

Diagram 2: System Architecture for Interoperability

G cluster_legacy Legacy & Heterogeneous Systems cluster_fair FAIR-Aligned Data Lake L1 Sequencer (SFF/QSEQ) M2 Format Converters L1->M2 L2 Hospital LIS (HL7v2) M1 EDIA Mapping Layer L2->M1 L3 Lab LIMS (Proprietary) L3->M1 F2 Common Data Model (e.g., OMOP CDM) M1->F2 F1 Standardized Sequencing (FASTQ/BAM) M2->F1 F3 Ontology-Annotated Metadata F1->F3 Int One Health Integrated Analysis F1->Int F2->F3 F2->Int F3->Int

Application Notes

In the context of a broader thesis on implementing FAIR (Findable, Accessible, Interoperable, Reusable) principles in One Health genomics research, robust metadata collection is the non-negotiable foundation. This protocol addresses the critical bottleneck of time-intensive, inconsistent metadata reporting by providing structured templates and tool recommendations.

Table 1: Quantitative Comparison of Metadata Management Tools

Tool Name Primary Function Cost Model Key Feature for One Health FAIR Alignment
ISA Framework Investigation/Study/Assay metadata structuring Open Source Hierarchical design for multi-omics, multi-species studies High (Interoperability)
CEDAR Metadata authoring with ontologies Freemium AI-assisted, ontology-driven template creation Very High (Interoperability)
NMDC EDGE Domain-specific metadata entry Open Source Built-in environmental & biosample packages High (Findability)
OS-M Open-source metadata collection app Open Source Offline-capable, designed for field collection High (Accessibility)
GenBank Submissions Portal Sequence submission w/ metadata Free Direct submission to INSDC databases High (Findability)

Experimental Protocols

Protocol 1: Standardized Metadata Capture for a One Health Genomic Sequencing Study

Objective: To systematically collect FAIR-compliant metadata for a microbial whole-genome sequencing study integrating human, animal, and environmental samples.

Materials:

  • Sample collection kits (swabs, filters, preservatives)
  • Mobile data collection device (tablet/phone with OS-M app installed)
  • Pre-configured ISA-Tab template (download from ISA Commons)
  • Access to CEDAR Workbench (cedar.metadatacenter.org)
  • Vocabulary: ENVO (environment), OBI (assay), NCBITaxon (organism)

Methodology:

  • Template Selection: Download the "One Health Microbial Genomics" ISA configuration from the ISA Commons template repository. This template pre-defines sections for Investigation (project), Study (sub-population), and Assay (sequencing).
  • Field Collection: a. Using the OS-M app, field personnel populate the digital form linked to the ISA template. Critical fields include: geolocation (latitude/longitude), collection date/time, host species (from NCBITaxon dropdown), sample type (e.g., "nasal swab", "soil"), and environmental medium (from ENVO). b. Data is saved locally and synced to a central server when connectivity is available.
  • Lab Processing Annotation: The lab manager updates the same ISA record via a desktop tool (like ISAcreator) with processing details: nucleic acid extraction protocol, library preparation kit, sequencing platform (e.g., Illumina NovaSeq 6000).
  • Semantic Enrichment: Upload the populated ISA-Tab file to the CEDAR Workbench. Use its validation tool to map free-text fields to controlled ontology terms (e.g., suggest "freshwater lake" [ENVO:00002200] for "lake water").
  • Submission Ready File Export: Export the finalized, enriched metadata as both an ISA-Tab archive and a JSON-LD file for submission to a public repository like the European Nucleotide Archive (ENA), which requires INSDC-compliant metadata.

Protocol 2: Automated Metadata Extraction from Instrument Output Files

Objective: To minimize manual entry and error by programmatically extracting technical metadata from sequencer output files.

Materials:

  • Illumina sequencing run directory (with RunParameters.xml, SampleSheet.csv)
  • Pacific Biosciences SEQUEL II output (with metadata.xml)
  • Python environment with pymetadata or savvy library installed
  • Custom Python script (provided below).

Methodology:

  • Script Setup: Create a Python script using the pymetadata library, which is designed to parse NextSeq and NovaSeq output files.
  • Target File Parsing: Configure the script to read the RunParameters.xml file to extract instrument serial number, run ID, flow cell type, and cycle counts.
  • Sample Sheet Integration: Configure the script to cross-reference the SampleSheet.csv to associate samples with specific lanes and index sequences.
  • Output to Template: Structure the script to write the extracted key-value pairs directly into the corresponding "instrument_parameters" section of your chosen metadata template (e.g., an ISA-Tab file or an NMDC EDGE submission form).
  • Validation: Run a final check to ensure the auto-populated fields align with the expected ontology terms for instrument model (e.g., "NextSeq 550" from OBI).

The Scientist's Toolkit: Research Reagent Solutions

Item Function in Metadata Context
Barcoded Library Prep Kits Unique dual-index barcodes are critical metadata, enabling sample multiplexing and demultiplexing. The kit name and version must be recorded.
Sample Preservation Buffer (e.g., DNA/RNA Shield) Preserves nucleic acid integrity at point-of-collection; the buffer type is key metadata for sample processing history.
Certified Reference Materials (CRMs) Used for assay validation; CRM identifier must be documented as metadata for quality control and reproducibility.
Ontology Lookup Service (OLS) A web-service (e.g., EMBL-EBI's OLS) to find and validate controlled vocabulary terms for metadata fields.
Digital Object Identifier (DOI) Minting Service Provides a persistent, unique identifier for the final dataset, fulfilling the "Findable" FAIR principle.

Visualizations

G Planning Planning & Template Selection Field Field Collection (Mobile App) Planning->Field Deploys Template Lab Lab Processing (Desktop Tool) Field->Lab Syncs Data Enrichment Semantic Enrichment (CEDAR) Lab->Enrichment Exports ISA-Tab Repository Public Repository Enrichment->Repository Submits JSON-LD

Title: One Health Metadata Collection Workflow

G cluster_0 FAIR Principles Human Human Meta Integrated Metadata Human->Meta Animal Animal Animal->Meta Env Env Env->Meta F Findable Meta->F A Accessible Meta->A I Interoperable Meta->I R Reusable Meta->R

Title: One Health Data Integration Enables FAIR

Application Notes for FAIR One Health Genomics

In the context of One Health genomics—integrating human, animal, and environmental data—navigating data governance is critical. Adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) must be balanced with stringent privacy and sovereignty requirements. This creates a complex matrix where data utility and regulatory compliance intersect.

Data Governance Framework

A tiered governance model is essential. It classifies data based on sensitivity and origin, dictating the applicable protocols for access, processing, and transfer.

Privacy Compliance Protocols
  • GDPR (General Data Protection Regulation): Applies to personal data of EU/EEA individuals. Genomic data is classified as "special category data," requiring explicit consent, purpose limitation, and robust technical measures (e.g., pseudonymization). Data Subject Access Requests (DSARs) must be facilitated.
  • HIPAA (Health Insurance Portability and Accountability Act): Governs Protected Health Information (PHI) in the U.S. The "Safe Harbor" method for de-identification is commonly applied to genomic datasets for research use.
Data Sovereignty Considerations

Data sovereignty laws (e.g., in China, India, Brazil) require data to be stored and processed within national borders. For multinational One Health studies, this necessitates federated or distributed analysis models where data does not leave its jurisdiction.

Table 1: Key Regulatory Parameters for Genomic Data

Regulation/Principle Geographical Scope Data Classification Key Compliance Requirement Typical Sanction for Breach
GDPR EU/EEA individuals Personal/Special Category Lawful basis, Data Protection by Design Up to €20M or 4% global turnover
HIPAA United States Protected Health Information (PHI) De-identification (Safe Harbor), Access Logs Up to $1.5M per year per violation
Data Sovereignty Varies by Nation Domestic Data In-country storage & processing Fines, data transfer suspension, revocation of license

Table 2: Data Handling Protocols for FAIR vs. Privacy

Data Action FAIR Principle Alignment Privacy/Governance Constraint Recommended Protocol
Data Storage Accessible, Reusable Sovereignty, Security Use certified cloud regions within jurisdiction; encrypt at rest.
Metadata Sharing Findable, Interoperable Minimization Share rich, non-personal metadata publicly; use controlled access for sensitive descriptors.
Data Access Accessible, Reusable Purpose Limitation, Consent Implement a Data Access Committee (DAC) & tiered access platforms (e.g., registered, controlled).
Data Transfer Accessible, Interoperable Adequacy Decisions, SCCs For cross-border transfer, use GDPR Standard Contractual Clauses or derogations for public interest research.

Detailed Experimental & Compliance Protocols

Protocol 1: Federated Genome-Wide Association Study (GWAS) Under Multi-Jurisdictional Constraints

Objective: To perform a coordinated GWAS on human and animal pathogen genomes across three countries with differing data laws without transferring raw genomic data. Methodology:

  • Local Ethics & Compliance Check: Each site (UK, US, India) obtains local IRB/ethics approval. GDPR consent, HIPAA authorization, or national equivalent is secured.
  • Local Processing & Standardization: At each site, raw FASTQ files are processed through a standardized bioinformatics pipeline (e.g., Nextflow) to generate variant call format (VCF) files. Personal identifiers are removed.
  • Federated Analysis Setup: A central analysis coordinator deploys a software stack (e.g., DataSHIELD, Federated AI Technology Enabler) to containerized environments at each site.
  • Secure Computation: Only statistical summaries (e.g., p-values, coefficients) from analyses run on the local, non-movable data are shared and aggregated centrally to derive global associations.
  • Result Validation & Audit: All summary data transfers are logged. A Data Protection Impact Assessment (DPIA) document is updated to record the federated process.
Protocol 2: Implementing Data Subject Access Requests (DSAR) for Genomic Research Data

Objective: To establish a verifiable and compliant process for responding to participant requests for their genomic data under GDPR Article 15. Methodology:

  • Request Intake & Identity Verification: Establish a secure portal for DSAR submissions. Implement a multi-factor identity verification process independent of the research team.
  • Data Location & Retrieval: Query the participant ID against the pseudonymization lookup table (held by a trusted third party) to locate all relevant data (raw sequences, variant reports, associated phenotypes).
  • Intelligible Preparation: Transform data into a consumer-friendly format (e.g., a visual variant report alongside raw FASTQ/VCF files). Provide a glossary of terms.
  • Secure Delivery & Logging: Deliver data via a password-protected, encrypted link with a time-limited expiry. Log all actions taken to fulfill the DSAR in the processing activities record.

Visualizations

governance_workflow start One Health Genomic Data classify Data Classification (Personal/Non-Personal, Sensitive) start->classify gdpr GDPR Assessment classify->gdpr hipaa HIPAA Assessment classify->hipaa sovereign Sovereignty Law Assessment classify->sovereign decision Determine Processing & Transfer Pathway gdpr->decision hipaa->decision sovereign->decision fed Federated Analysis decision->fed Transfer Prohibited anon Anonymized Transfer decision->anon Safe Harbor/ Adequacy secure Secure Enclave Analysis decision->secure Contractual Safeguards

Title: Data Governance Decision Workflow for One Health Genomics

fair_privacy_tension cluster_fair FAIR Objectives cluster_gov Governance Objectives FAIR Principles FAIR Principles f1 Wide Data Sharing FAIR Principles->f1 f2 Central Repositories FAIR Principles->f2 f3 Rich Metadata FAIR Principles->f3 f4 Unrestricted Re-use FAIR Principles->f4 Privacy & Sovereignty Privacy & Sovereignty g1 Restricted Access Privacy & Sovereignty->g1 g2 Local Data Storage Privacy & Sovereignty->g2 g3 Data Minimization Privacy & Sovereignty->g3 g4 Purpose Limitation Privacy & Sovereignty->g4 f1->g1 Tension f2->g2 Tension f3->g3 Tension f4->g4 Tension

Title: FAIR Principles vs. Privacy & Sovereignty Tensions

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Compliant FAIR Data Management

Item/Category Example Solutions Function in Compliance & FAIRness
De-identification/Pseudonymization Software ARX, sdcMicro, custom Python/R scripts Removes direct identifiers from datasets to satisfy HIPAA Safe Harbor or GDPR pseudonymization standards, enabling safer sharing.
Federated Analysis Platforms DataSHIELD, NVIDIA FLARE, OpenMined Allows analysis across decentralized data sources without moving raw data, addressing sovereignty and privacy constraints.
Secure & Sovereign Cloud Infrastructure AWS/GCP/Azure Sovereign Cloud regions, National Research Clouds Provides data storage and compute within legal jurisdictions to comply with data residency laws.
Data Access Governance Tools GA4GH Passports, DUOS, REMS Manages tiered, consent-based access to datasets via Data Access Committees (DACs), balancing accessibility with control.
Metadata & Ontology Standards GA4GH Phenopackets, INSDC standards, OBO Foundry ontologies Ensures interoperability (the "I" in FAIR) and precise annotation, facilitating data combination while maintaining context for proper use.
Standardized Processing Pipelines nf-core pipelines, Common Workflow Language (CWL) Ensures reproducible, consistent data processing across sites, a prerequisite for interoperable and reusable data.

Within One Health genomics research, integrating data from human, animal, and environmental domains is critical for understanding zoonotic diseases, antimicrobial resistance, and ecosystem health. The FAIR (Findable, Accessible, Interoperable, Reusable) principles provide a framework for managing this complex data. However, researchers often face significant resource constraints, making sustainable FAIR implementation a challenge. This document outlines cost-effective strategies and practical protocols for achieving FAIR compliance in resource-limited settings typical of One Health projects.

Current Landscape of FAIR Implementation Costs

A recent analysis of genomic data repository practices reveals the following cost distribution for achieving basic FAIR compliance in medium-sized projects.

Table 1: Estimated Costs for Core FAIR Implementation Activities

Activity Low-Estimate (USD) High-Estimate (USD) Primary Cost Driver
Metadata Curation & Standardization 5,000 20,000 Personnel time for semantic annotation
Data Repository Fees (Public) 0 2,000 Long-term archival costs for large datasets
Middleware for API Access 1,000 10,000 Development of custom accession tools
Persistent Identifier (PID) Minting 200 1,000 Annual maintenance fees for DOIs/ARCs
Data Packaging & Documentation 3,000 15,000 Personnel time for creating reusable data packages
Total Project Cost 9,200 48,000

Cost-Effective Application Notes

AN-1: Leveraging Community-Endorsed Metadata Standards

  • Principle Addressed: Interoperability, Reusability.
  • Strategy: Utilize minimal information standards developed by consortia like the Genomics Standards Consortium (GIC) and One Health-specific extensions. This reduces the need for custom schema development and enables immediate data integration.
  • Tool Recommendation: ISAcreator software. This open-source tool provides a configurable framework to collect, manage, and curate investigation-level metadata without licensing fees.
  • Cost-Saving: Eliminates the need for proprietary laboratory information management systems (LIMS), saving an estimated $10,000-$50,000 annually.

AN-2: Tiered Storage and Archiving Strategy

  • Principle Addressed: Accessibility, Reusability.
  • Strategy: Implement a three-tiered storage model to balance access speed and cost.
    • Tier 1 (Hot): Local high-performance storage for active analysis (raw FASTQ, BAM files). Retain for 1-2 years.
    • Tier 2 (Warm): Institutional or cloud object storage (e.g., AWS S3-IA, Zenodo) for processed final datasets (VCFs, assembled genomes). Retain for 5-10 years.
    • Tier 3 (Cold): National/public genomics archives (e.g., NCBI SRA, ENA, IPD) for long-term preservation and global accessibility. Indefinite retention.
  • Cost Impact: Can reduce long-term storage costs by up to 70% compared to keeping all data on high-performance systems.

AN-3: Utilizing FAIR-Enabling Platforms with Fee Waivers

  • Principle Addressed: Findability, Accessibility.
  • Strategy: Submit data to generalist and domain-specific repositories that offer fee waivers for publicly funded research or researchers from low-middle income countries.
  • Recommended Repositories:
    • Zenodo (CERN): No upload fees, provides DOIs, and integrates with GitHub.
    • The Open Science Framework (OSF): Free for public projects.
    • Specific Repositories: NCBI SRA (free for public data), MicrobiomeDB (for microbiome data).

Detailed Protocols

Protocol P-1: Efficient Metadata Annotation for One Health Genomic Samples

Objective: To consistently annotate whole-genome sequencing (WGS) samples from multiple hosts and environments using a lightweight, standards-based approach.

Materials:

  • Sample information spreadsheet
  • EDAM ontology browser (https://edamontology.org/page)
  • EnvO (Environment Ontology) browser (https://www.ebi.ac.uk/ols/ontologies/envo)
  • NCBI BioSample checklist (https://www.ncbi.nlm.nih.gov/biosample/docs/)
  • ISAcreator software (https://isa-tools.org/)

Methodology:

  • Template Preparation: Download the ISA-Tab configuration for "genome sequencing assay" from the ISA tools website.
  • Investigation-Level Metadata: Populate the investigation file with project title, description, grant identifier, and publication links. Use a persistent identifier like a RRID for the project.
  • Sample Annotation: For each biosample (e.g., human nasopharyngeal swab, poultry cloacal swab, soil sample), complete a row in the ISA sample sheet.
    • For host-associated samples: Provide attributes for host species (NCBI Taxonomy ID), host disease status, collection date, anatomical site (UBERON ID).
    • For environmental samples: Provide attributes for env_broad_scale, env_local_scale, env_medium using EnvO terms (e.g., forest ecosystem, leaf litter, soil).
  • Assay Linkage: In the assay file, link each sequencing library (FASTQ file) to its corresponding biosample. Specify the platform, library strategy, and data transformation protocols (using EDAM ontology terms).
  • Validation: Use the isatools Python package to validate the ISA-Tab files against the configured templates before submission.

The Scientist's Toolkit: Research Reagent Solutions for Metadata Management

Item/Tool Function Cost Model
ISAcreator Software Desktop application for creating standards-compliant metadata files. Free, Open Source
Ontology Lookup Service (OLS) Web service for finding and validating ontology terms. Free
isatools Python API Programmatic creation, validation, and conversion of ISA-Tab metadata. Free, Open Source
DataCure Metadata Validator Web-based validator for NCBI and ENA metadata requirements. Free

Protocol P-2: Automated Data Packaging and Submission to Public Repositories

Objective: To automate the process of packaging sequence data, validated metadata, and a readme file for bulk submission to an archive, minimizing manual effort.

Materials:

  • Linux-based server or computing environment
  • Validated ISA-Tab metadata directory
  • Processed sequence files in final format (e.g., FASTQ, VCF)
  • aspera or lftp command-line tools for high-speed transfer
  • Repository-specific command-line utilities (e.g., ncbi's prefetch, ENA's webin-cli)

Methodology:

  • Directory Structuring: Create a project directory with subfolders: /raw_data, /processed_data, /metadata, /docs.
  • Readme Generation: Automatically generate a README.txt file using a script that extracts core descriptors from the ISA investigation file.
  • Checksum Creation: Run md5deep or sha256sum on all data files, outputting a manifest for later integrity verification.
  • Package Creation: Use tar or zip to create a final data package, optionally compressing text-based files (e.g., VCF) with bgzip.
  • Programmatic Submission (Example for ENA):
    • Use the webin-cli tool to authenticate via your ENA credentials.
    • Upload the metadata XML (converted from ISA-Tab) to reserve accession numbers.
    • Use the returned run accession numbers to rename your sequence files, establishing clear links.
    • Initiate the Aspera transfer of sequence files to the ENA FTP dropbox using the assigned directory path.

Visualization of Strategic Workflows

G cluster_legend Key Strategy Start Raw One Health Genomic Data M1 Apply Community Metadata Standards Start->M1 Protocol P-1 M2 Assign Persistent Identifiers (PID) M1->M2 Mint DOI/ARK M3 Store in Tiered Storage System M2->M3 AN-2 Strategy M4 Submit to FAIR Repository M3->M4 Protocol P-2 End FAIR-Compliant Data Asset M4->End L1 Cost-Effective Community Tools L2 Automated Process

Cost-Effective FAIR Data Pipeline

G T1 Tier 1: Hot Storage (Local HPC/Server) T2 Tier 2: Warm Storage (Cloud/Object Store) T1->T2 After 2y (Protocol P-2) Access High-Speed Access T1->Access T3 Tier 3: Cold Storage (Public Archive) T2->T3 After 5y (Auto-archive) Preserve Long-Term Preservation T3->Preserve

Tiered Storage for Sustainable Access

Application Notes

Note 1: Current State of FAIR Adoption in One Health Genomics The implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles within One Health genomics research faces significant cultural and technical barriers. Key challenges include fragmented data silos across human, animal, and environmental research domains, a lack of standardized metadata, and insufficient recognition for data sharing in career progression. A successful FAIR culture shift requires an integrated strategy addressing training, incentivization, and structured organizational change.

Note 2: Foundational Training Curriculum for FAIR Data Stewardship Effective training must move beyond tool-specific instruction to encompass the "why" and "how" of FAIR. Curricula should be tiered for data producers, data stewards, and principal investigators. Core modules must include practical metadata annotation using community-agreed standards (e.g., MIxS for genomics), persistent identifier (PID) assignment, and the use of trusted repositories. Training should be contextualized within One Health use cases, demonstrating the cross-species insights enabled by FAIR data.

Note 3: Design of Incentive Structures for Sustainable FAIR Practices Traditional academic and industry incentives prioritize publication authorship and patent generation. To foster a FAIR culture, incentive structures must be realigned. This includes formal recognition of datasets as first-class research outputs in hiring and promotion reviews, the implementation of "data sharing impact" metrics, and the integration of FAIR compliance into internal funding and performance review cycles.

Note 4: Change Management Protocol for Research Consortia Implementing FAIR principles across multi-institutional One Health consortia requires deliberate change management. A phased approach, starting with pilot projects that demonstrate rapid value (e.g., meta-analysis of shared antimicrobial resistance gene data), builds internal advocacy. Establishing clear, consortia-wide data governance policies and designated FAIR "champions" within each partner institution is critical for scaling practices.

Protocols

Protocol 1: Implementing a FAIR Competency Framework

Objective: To assess and build FAIR-related competencies across a research organization.

  • Competency Mapping: Define required competencies (e.g., metadata schemas, ontology use, data licensing).
  • Gap Analysis Survey: Administer a confidential survey to research staff using a Likert scale to self-assess competency levels.
  • Targeted Training Development: Develop or source training modules based on gap analysis results.
  • Practical Application Project: Learners must complete a "FAIRification" project on a sample dataset.
  • Competency Evaluation: Assess final project against a FAIR maturity checklist.

Protocol 2: Measuring and Rewarding FAIR Data Impact

Objective: To create a quantitative framework for recognizing FAIR data contributions.

  • Metric Definition: Establish key metrics (see Table 1).
  • Data Collection: Use repository APIs (e.g., DataCite, EBI Biostudies) to automatically gather metric data for institutional datasets.
  • Impact Score Calculation: Annually calculate a weighted "FAIR Impact Score" for each research group or individual.
  • Integration into Review: Present the FAIR Impact Score alongside traditional metrics in performance review documentation.

Protocol 3: Phased FAIR Adoption in a One Health Genomics Project

Objective: To integrate FAIR practices into an active research project lifecycle.

  • Phase 1 - Planning (Pre-Data Collection):
    • Register the project in a registry (e.g., INSDC BioProject).
    • Define and document project-specific metadata profile extending community standards.
  • Phase 2 - Execution (Active Research):
    • Annotate raw data with PIDs and minimal metadata upon generation.
    • Deposit data in a domain-specific repository (e.g., ENA, SRA) under embargo.
  • Phase 3 - Publication & Beyond:
    • Lift repository embargo upon manuscript submission.
    • Publish a formal "Data Note" article describing the dataset.
    • Link dataset PID from the final research publication.

Data Tables

Table 1: Proposed Metrics for FAIR Contribution Assessment

Metric Measurement Method Target Weight in "FAIR Impact Score"
Dataset Citations Count of scholarly citations via PID 30%
Dataset Reuses Count of formal re-use mentions (e.g., in methods) tracked via repositories 25%
FAIRness Score Result from community maturity indicators (e.g., FAIR Evaluator) 20%
Metadata Richness Completeness score against relevant checklist (e.g., MIxS) 15%
Interoperability Use of community ontologies (count of terms mapped) 10%

Table 2: Tiered FAIR Training Curriculum for One Health Genomics

Tier Target Audience Core Modules Duration
Awareness All Research Staff FAIR Principles Overview; One Health Use Cases 2 hours
Practitioner Data Producers (Lab Staff, Bioinformaticians) Metadata Standards (MIxS); PID Minting; Repository Submission 8 hours
Steward Data Managers, PI Leads Data Governance; Ontology Curation; FAIR Compliance Checking 16 hours

Diagrams

Diagram 1: FAIR Culture Change Management Pathway

G Start Assess Current State A Define FAIR Vision & Goals Start->A B Engage Leadership & Assign Champions A->B C Develop Training & Tools B->C D Pilot Projects & Showcase Value C->D E Revise Incentives & Policies D->E F Scale & Integrate into Workflow E->F End Sustain & Iteratively Improve F->End

Diagram 2: One Health FAIR Data Workflow

G cluster_0 Data Data Generation Generation ; fontcolor= ; fontcolor= Human Human Genomics Sub Standardized Submission (MIxS + PIDs) Human->Sub Animal Animal Genomics Animal->Sub Env Environmental Metagenomics Env->Sub Repo Trusted Repository (e.g., ENA) Sub->Repo Integ FAIR Data Integration Platform Repo->Integ Analysis Cross-Domain One Health Analysis Integ->Analysis

The Scientist's Toolkit: Research Reagent Solutions

Item Function in FAIRification Process
Metadata Schema (e.g., MIxS) A standardized checklist defining the mandatory and contextual metadata fields required to describe a genomics dataset, ensuring interoperability.
Ontology (e.g., ENVO, OBI, NCBITaxon) Controlled vocabularies that provide machine-readable terms for describing samples, experiments, and organisms, critical for semantic interoperability.
Persistent Identifier (PID) Service (e.g., DOI, ARK) A permanent, unique reference to a digital object (dataset, sample) that remains stable even if its location changes, ensuring findability and accessibility.
Trusted Repository (e.g., ENA, SRA, Zenodo) A digital archive that provides long-term preservation, access, and PID assignment for research data, aligned with FAIR principles.
FAIR Assessment Tool (e.g., FAIR Evaluator, F-UJI) Automated software that tests a dataset's URL against core FAIR principles, generating a maturity report and improvement recommendations.
Data Management Plan (DMP) Tool A structured template or online tool (e.g., DMPTool) to prospectively plan for data collection, documentation, sharing, and preservation.

Measuring FAIRness and Evaluating Impact in One Health Initiatives

This application note details protocols for assessing FAIR compliance in One Health genomics research. It provides a comparative analysis of FAIR assessment tools and practical methodologies for implementing FAIR Maturity Indicators to enhance data interoperability and reuse in infectious disease surveillance and antimicrobial resistance studies.

Within One Health genomics, integrating data from human, animal, and environmental sources is critical for understanding pathogen evolution and spillover events. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a framework to overcome data silos. This note details protocols for applying FAIR assessment tools and metrics to ensure genomic and epidemiological data are primed for integrated analysis.

Quantitative Comparison of FAIR Assessment Tools

The following table summarizes key tools based on current evaluations.

Table 1: Comparison of FAIR Assessment Tools and Metrics

Tool / Resource Name Primary Purpose Metric Type (e.g., Maturity Indicators, Rubrics) Output Provided Integration with One Health Genomics
FAIRsharing Registry of standards, databases, and policies Not an assessor; maps relationships between resources Resource descriptions & linkages Critical for identifying domain-specific metadata standards (e.g., MIxS, SNPF)
FAIR Evaluator Automated FAIRness assessment Maturity Indicators (MIs) as machine-actionable queries Score per MI (0-1), summary report Can evaluate metadata of genomic repositories (ENA, NCBI, BV-BRC)
F-UJI Automated, API-based assessment Maturity Indicators based on RDA FAIR Data Maturity Model Automated score & improvement guidance Suitable for assessing persistent identifiers and metadata richness of public datasets
FAIR-Checker Web service for assessment Core FAIR principles Summary scores and visualizations Useful for quick checks on dataset landing pages
FAIR Maturity Indicator Specification Framework for defining tests Community-agreed Maturity Indicators Blueprint for creating tests Enables creation of custom, project-specific metrics for One Health data objects

Protocols for FAIR Assessment in One Health Genomics

Protocol 3.1: Selecting Standards via FAIRsharing for a One Health Genomic Study

Objective: To identify and select appropriate metadata standards and repositories for a viral pathogen genome surveillance project. Materials:

  • Computer with internet access
  • FAIRsharing.org website Procedure:
  • Navigate to https://fairsharing.org.
  • Use the search bar. Enter relevant keywords (e.g., "genome sequence", "environmental metadata", "One Health").
  • Filter results by "Type": select "Metadata Standard".
  • Identify the "Minimum Information about any (x) Sequence" (MIxS) standard family. Click on the MIxS record.
  • Review the "Related Databases" section to see compatible public repositories (e.g., European Nucleotide Archive - ENA).
  • Note the specific checklist (e.g., MIGS.virus) recommended for your pathogen type.
  • In the record, under "Relations", examine linked "Policies" (e.g., funder mandates) to ensure compliance. Expected Outcome: A documented list of mandated metadata fields and a target data repository for project data deposition.

Protocol 3.2: Automated FAIRness Assessment Using the F-UJI Tool

Objective: To programmatically assess the FAIRness of a publicly available antimicrobial resistance (AMR) gene catalogue dataset. Materials:

  • API endpoint: https://www.f-uji.net/api/evaluate
  • A Persistent Identifier (PID) for the dataset (e.g., a DOI for a dataset on Zenodo or Figshare)
  • Command-line tool (curl) or programming environment (Python with requests library) Procedure:
  • Identify Test Subject: Obtain the PID for a target dataset. Example: 10.5281/zenodo.1234567.
  • ​API Call: Execute an assessment request using curl:

  • ​Retrieve Results: The API returns a JSON response containing scores for each FAIR principle and individual metric.
  • ​Analysis: Parse the JSON to identify weak areas. Focus on "Interoperability" metrics related to vocabulary use and "Reusability" metrics related to license clarity. Expected Outcome: A quantitative FAIR score report highlighting strengths (e.g., findability via DOI) and weaknesses (e.g., lack of a formal license) of the assessed dataset.

Protocol 3.3: Developing Custom Maturity Indicators for Integrated One Health Data

Objective: To create a project-specific Maturity Indicator for "Interoperability" that checks for the presence of geographic coordinates linked to sample origins in a metadata record. Materials:

  • Metadata schema (e.g., INSDC sample checklist)
  • A defined serialization format (e.g., JSON-LD)
  • A FAIR assessment framework that supports custom MIs (e.g., FAIR Evaluator setup) Procedure:
  • Define the Indicator: Formulate a testable requirement: "The metadata record contains the terms geographic location (latitude) and geographic location (longitude) with valid decimal degree values."
  • Formalize the Test: Express the test as a machine-actionable query. For a JSON-LD metadata file, this could be a SPARQL query or a simple JSON path check. Example JSON Path logic:

  • Implement the Test: Code the test logic into a script or integrate it into your project's data validation pipeline.
  • Apply and Iterate: Run the test on incoming project metadata. Use failures to guide data submitters to provide complete geographic information. Expected Outcome: Improved consistency and machine-actionability of location data across human, animal, and environmental sample metadata, enabling spatial analysis of genomic findings.

Visualizations

fair_workflow Start One Health Genomics Data FS FAIRsharing (Standard Selection) Start->FS Protocol 3.1 M Metadata Annotation FS->M Apply Standards D Data Deposition in Repository M->D Public / Controlled Access A2 Custom Maturity Indicator Checks M->A2 Protocol 3.3 (Internal Validation) A1 Automated Assessment (F-UJI / FAIR Evaluator) D->A1 Protocol 3.2 (Public PID) End FAIR-Compliant Reusable Dataset A1->End Score & Report A2->M Feedback Loop

Title: FAIR Assessment Workflow for One Health Data

tool_ecosystem Standards FAIRsharing (Knowledge Base) Framework FAIR Maturity Indicator Specs Standards->Framework informs AutoTool Automated Tools (F-UJI, FAIR Evaluator) Framework->AutoTool implements Data One Health Dataset AutoTool->Data evaluates Report FAIR Assessment Report Data->Report generates

Title: FAIR Tool Ecosystem Relationships

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Resources for FAIR Assessment Implementation

Item / Resource Function in FAIR Assessment Example / Provider
FAIRsharing Registry Centralized resource to discover, select, and cite community-endorsed standards for data and metadata. https://fairsharing.org
F-UJI API Programmatic, automated FAIR assessment tool that tests datasets against the RDA Maturity Indicators. API endpoint: https://www.f-uji.net
FAIR Evaluator Service A web service and API that runs community-defined Maturity Indicator tests against digital objects. https://fair-evaluator.it.csiro.au
RDA FAIR Maturity Model The canonical specification for defining Maturity Indicators, providing the blueprint for creating tests. RDA Recommendation (DOI: 10.15497/rda00050)
PID Services (DataCite) Provides persistent identifiers (DOIs) which are fundamental for machine-actionable Findability (F1). https://datacite.org
Schema.org / Bioschemas Markup A vocabulary to embed FAIR metadata directly into web pages (dataset landing pages). https://bioschemas.org
FAIR Cookbook A collection of hands-on recipes for making and keeping data FAIR, with use cases from life sciences. https://faircookbook.elixir-europe.org

Application Notes

The implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles is critical for One Health genomics research, enabling integration of diverse data streams from humans, animals, and the environment. Two leading projects demonstrate successful, scalable models for zoonotic pathogen surveillance.

1. The European COVID-19 Data Platform Established rapidly in response to the SARS-CoV-2 pandemic, this federated platform exemplifies FAIR implementation for a high-consequence zoonotic pathogen. It integrates sequencing data, epidemiological metadata, and publications across member states. A key to its success is the use of common data models and standardized sample provenance tracking (e.g., using MIxS standards). Its findability is driven by persistent identifiers (PIDs) for datasets and a central search portal. Interoperability is achieved through APIs that connect national nodes to the central gateway, allowing for real-time data exchange while respecting data sovereignty.

2. The NIAID CEIRS Network (Center for Research on Influenza Pathogenesis and Transmission) This long-standing influenza surveillance network provides a model for sustained FAIR compliance in monitoring avian and swine influenza viruses with pandemic potential. It emphasizes rich, structured metadata using controlled vocabularies (e.g., Influenza Virus Resource at NCBI). Reusability is ensured by providing clear data usage licenses and detailed protocols. The network employs standardized assay protocols across global collection sites, ensuring that genomic data from animal markets, farms, and clinics are interoperable for integrated analysis.

Quantitative Comparison of FAIR Implementation Metrics

Table 1: Key Performance Indicators for FAIR Zoonotic Surveillance Platforms

FAIR Metric European COVID-19 Data Platform NIAID CEIRS Network
Time from launch to 50,000 shared genomes 12 months 60 months (continuous evolution)
Number of integrated data sources/repositories 35+ (ENA, GEO, PubMed, etc.) 15+ (GISAID, IRD, NCBI, etc.)
Average metadata completeness score 92% (using FAIRsharing.org tools) 88%
API query response time < 2 seconds < 5 seconds
Data reuse citations (estimated) 5,000+ (in publications) 10,000+ (cumulative)
Use of PIDs (Datasets, Samples) DOI, BioSample, ORCID GenBank ID, BioProject, SRA

Protocols

Protocol 1: FAIR-Compliant Sample Processing and Metagenomic Sequencing for Pathogen Detection

Objective: To generate sequence data from animal or environmental samples with FAIR-rich metadata from point of collection.

Materials:

  • Sample (e.g., swab, tissue, environmental sample)
  • Nucleic Acid Extraction Kit (e.g., QIAamp Viral RNA Mini Kit)
  • Reverse Transcription and Amplification Reagents
  • Library Prep Kit (e.g., Nextera XT)
  • Sequencing Platform (e.g., Illumina NextSeq)
  • Metadata collection form (electronic, using OBO Foundry ontologies)

Procedure:

  • Sample Collection & Metadata Annotation:
    • At collection site, record minimum required metadata: sample ID, collector, date, time, GPS coordinates, host species (NCBI Taxonomy ID), sample type (ENVO term), and health status.
    • Assign a unique, persistent sample identifier linked to a central registry.
  • Nucleic Acid Extraction:
    • Extract total nucleic acid following kit protocol. Include negative extraction controls.
    • Quantify yield using fluorometric methods.
  • Library Preparation & Sequencing:
    • For RNA viruses, perform reverse transcription using random hexamers.
    • Use non-targeted PCR amplification for pathogen detection.
    • Prepare sequencing library using a tagmentation-based kit. Index samples with dual indices.
    • Pool libraries and sequence on a mid-output flow cell (2x150 bp).
  • FAIR Data Submission:
    • Upload raw sequence files (FASTQ) to the European Nucleotide Archive (ENA) or SRA.
    • Submit metadata via the interactive Webin portal or via programmatic submission using the Webin-CLI tool. Ensure metadata is mapped to the recommended checklists (e.g., ERC000033).
    • The accession numbers (PIDs) issued complete the FAIR data cycle.

Objective: To conduct a phylogenetic analysis of a zoonotic pathogen using FAIR datasets from distinct public repositories.

Materials:

  • Computational environment (e.g., Linux server, cloud instance)
  • Data retrieval tools: datasets CLI from NCBI, ENA API client, or GISAID API access.
  • Analysis tools: Nextstrain workflow (augur, auspice), MAFFT, IQ-TREE.
  • Metadata harmonization script (Python/R).

Procedure:

  • Findable & Accessible Data Retrieval:
    • Identify relevant datasets using platform search portals via keywords and filters.
    • Retrieve sequence data and associated metadata programmatically using APIs or CLI tools with dataset-specific accession numbers (PIDs).
    • Example NCBI Datasets CLI: datasets download virus genome accession MN908947 --include genome
  • Interoperability & Data Harmonization:
    • Parse metadata from different sources into a common tab-delimited format.
    • Map metadata fields to a common schema (e.g., adapt all location fields to GeoNames IDs, host fields to NCBI Taxonomy IDs).
    • Merge sequence files into a multi-FASTA alignment.
  • Core Analysis Workflow:
    • Perform multiple sequence alignment using MAFFT: mafft --auto input.fasta > aligned.fasta
    • Construct a maximum-likelihood phylogenetic tree using IQ-TREE: iqtree -s aligned.fasta -m GTR+G -bb 1000
    • Temporal analysis and visualization can be performed using the Nextstrain Augur pipeline.
  • Reusable Output Publication:
    • Archive the final analysis dataset (alignment, tree, harmonized metadata) in a repository like Zenodo, which assigns a DOI.
    • Publish all analysis code (e.g., Snakemake, Nextflow pipeline) on GitHub or GitLab with an open-source license.

Visualizations

G Sample Field Sample Collection MD Structured Metadata Annotation Sample->MD Assign Sample ID Seq Sequencing & Analysis MD->Seq Rich Context Repo Public Repository (ENA/SRA) Seq->Repo Submit Data FAIR FAIR Data (PIDs, APIs, Standards) Repo->FAIR Citable Accession Research Integrated One Health Research FAIR->Research Global Reuse

Title: FAIR Data Workflow for Pathogen Surveillance

G Human Human Clinical Data FAIRLayer FAIR Data Layer (Common Standards, APIs, PIDs) Human->FAIRLayer Animal Animal Surveillance Data Animal->FAIRLayer Env Environmental Metagenomics Env->FAIRLayer Integration Integrated Analysis Platform FAIRLayer->Integration Interoperable Data Streams Output Actionable Insights: Source Attribution, Risk Assessment, Vaccine Design Integration->Output

Title: One Health Data Integration via FAIR Principles

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Reagents and Materials for FAIR Zoonotic Surveillance

Item Function in Protocol Key Feature for FAIR Compliance
Standardized Nucleic Acid Extraction Kit Isolates pathogen RNA/DNA from diverse sample matrices. Enables consistent yield/quality data, a reusable methodological parameter.
Dual-Indexed Sequencing Library Prep Kit Prepares amplitagged libraries for NGS. Unique combinatorial indexes allow sample multiplexing, preserving sample identity.
Synthetic Spike-in Controls (e.g., ERCC RNA) Added to sample pre-extraction. Allows for technical normalization and cross-study comparability of sequencing data.
Electronic Laboratory Notebook (ELN) Digital recording of all experimental steps and parameters. Facilitates export of structured, machine-readable provenance metadata.
Ontology-Annotated Metadata Template Digital form for sample and experiment metadata. Embeds controlled vocabulary terms (e.g., OBI, ENVO) ensuring semantic interoperability.
API-Enabled Data Repository Credentials Programmatic access to public data archives. Allows automated querying and retrieval of Findable, Accessible data for integrated analysis.

Application Notes

This document quantifies the Return on Investment (ROI) from implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles in drug target discovery, framed within a One Health genomics research thesis. Integrating diverse data from human, animal, and environmental sources under FAIR guidelines accelerates biomarker identification, target validation, and lead compound prioritization.

Quantitative Impact of FAIR Implementation

The following table summarizes key performance indicators (KPIs) from published studies and consortium reports comparing traditional versus FAIR-enabled research workflows in early drug discovery.

Table 1: Comparative KPIs for Target Discovery & Validation

KPI Metric Pre-FAIR Workflow (Benchmark) FAIR-Enabled Workflow Data Source / Study Context
Time to Identify Candidate Targets 12-18 months 3-6 months IMI-EMCURE, FAIRplus Observatory
Data Reuse Rate <20% >60% Pharma internal audits (2023)
Cost per Validated Target ~$2.5M USD ~$1.2M USD Project Analytics, BioPharma
Cross-Study Data Integration Success 30% (Manual Curation) 85% (Semi-Automated) FAIRplus Pilot (SARS-CoV-2)
Reproducibility of Validation Experiments ~50% ~85% Peer-Review Analysis

Case Study: Multi-Omics Integration for Oncology Target Discovery

A FAIR-driven project integrated proprietary cell line screens with public genomics repositories (e.g., DepMap, TCGA, GEO) to validate a novel kinase target. The FAIR protocol involved:

  • Findability: Assigning persistent identifiers (PIDs) to all cell lines, assay results, and analysis scripts.
  • Accessibility: Hosting processed omics data in a cloud-based repository with tiered access controls.
  • Interoperability: Using standardized ontologies (e.g., EDAM, OBI, CHEBI) to annotate data types and experimental conditions.
  • Reusability: Packaging analysis pipelines as containerized workflows (Docker/Singularity) with clear licensing.

Result: Reduced the target validation timeline by 9 months, primarily by eliminating 6 months typically spent on data wrangling and reconciling identifiers.

Protocols

Protocol 1: FAIRifying Pre-Clinical Omics Data for Cross-Species Analysis

Objective: To prepare internal transcriptomic and proteomic datasets for integration with public One Health genomics databases to identify conserved pathogenic pathways.

Materials:

  • Research Reagent Solutions Table:
Item Function Example Product/Catalog
Metadata Schema Tool Defines mandatory and optional fields for experiment description. ISA framework (ISAcreator)
Ontology Annotator Links experimental terms to controlled vocabularies. Zooma, OXO
PID Generator Creates persistent, globally unique identifiers for datasets. ePIC (for data), RRID (for reagents)
FAIR Assessment Tool Evaluates the "FAIRness" of a digital resource. FAIR-Checker, F-UJI
Workflow Management System Records, versions, and exports computational analysis steps. Nextflow, Snakemake
Trusted Repository Long-term, publicly accessible data storage. EMBL-EBI's BioStudies, Zenodo

Procedure:

  • Metadata Curation: Using the ISA framework, populate investigation, study, and assay sheets. Mandatory fields include: organism (NCBI Taxonomy ID), disease (MONDO ID), assay type (OBI ID), and measured analyte (CHEBI ID).
  • Data De-identification: Remove any directly identifying patient information. For model organism data, ensure institutional ethical approval is documented via a PID.
  • Semantic Annotation: Run raw metadata files through the Zooma service to automatically suggest ontology terms from the EBI Biosamples database. Manually curate and confirm mappings.
  • PID Assignment: Register the finalized dataset with your institution's ePIC handle system to obtain a persistent URL (e.g., hdl:<prefix>/<suffix>). Register all antibodies/cell lines used with Research Resource Identifiers (RRIDs).
  • Workflow Packaging: Encode the primary data analysis pipeline (e.g., RNA-seq alignment, differential expression) in a Nextflow script. Define all software dependencies in a Conda environment file.
  • Deposition & Licensing: Upload (a) raw data, (b) curated metadata, (c) processed results, and (d) the workflow to a chosen trusted repository. Apply a clear usage license (e.g., CC-BY 4.0).
  • FAIR Assessment: Run the final repository URL through the F-UJI automated FAIR data assessment tool to generate a score report. Iterate to improve weaknesses.

Protocol 2: In Silico Target Prioritization Using FAIR Data Lakes

Objective: To computationally prioritize novel drug targets by federated query across internal and external FAIR databases.

Procedure:

  • Query Formulation: Define a target candidate list from internal high-throughput screening (HTS). For each candidate gene/protein, extract its standardized identifier (e.g., Ensembl Gene ID, UniProt ID).
  • Federated Search: Use a federated query engine (e.g., Datalad, SPARQL endpoint wrapper) to simultaneously search against interconnected FAIR resources. Key resources include:
    • Open Targets Platform: For genetic association, known drugs, and safety data.
    • GlyGen: For glycosylation sites (relevant for biologics).
    • Protein Data Bank (PDB): For 3D structural information.
    • Alliance of Genome Resources: For cross-species orthology and phenotype data.
  • Data Harmonization: The query engine retrieves evidence strings for each target. A pre-configured harmonization script converts all returned data to a common schema using ontology mappings (e.g., all "disease" terms mapped to EFO).
  • Score & Rank: Apply a weighted scoring algorithm to the harmonized evidence. Weights are defined by the project (e.g., human genetic evidence weighted highest). Generate a ranked target list with aggregated evidence scores.
  • Validation Triangulation: For top-ranked targets, extract associated signaling pathways from the Reactome database (via its FAIR API) to design in vitro validation experiments.

Visualizations

FAIR_ROI_Pathway cluster_0 Traditional Workflow cluster_1 FAIR-Enabled Workflow T1 Internal HTS/Omics Data T2 Data Silos (Manual Curation Needed) T1->T2 Proprietary Format T4 Slow, Sequential Analysis T2->T4 Months of Wrangling T3 Limited Public Data Access T3->T4 Manual Download & Reformating T5 Target Shortlist T4->T5 F4 Ranked Target List with Aggregated Evidence End Validated Target for Development T5->End F1 Internal FAIR Data Lake (PIDs, Ontologies, APIs) F3 Federated Query & Automated Integration F1->F3 Machine-Actionable F2 Public FAIR Resources (e.g., Open Targets, PDB) F2->F3 Machine-Actionable F3->F4 Weeks F4->End Start Hypothesis & Candidate List Start->T1 Start->F1

Diagram Title: FAIR vs Traditional Target Discovery Workflow

Protocol1_Workflow P1_Start Raw Omics Data & Lab Notes P1_Step1 Step 1: Metadata Curation (ISA Framework) P1_Start->P1_Step1 P1_Step2 Step 2: Semantic Annotation (Zooma / Ontology Tools) P1_Step1->P1_Step2 P1_Step3 Step 3: Assign PIDs & Package Workflow P1_Step2->P1_Step3 P1_Step4 Step 4: Deposition to Trusted Repository P1_Step3->P1_Step4 P1_Step5 Step 5: Automated FAIR Assessment (F-UJI) P1_Step4->P1_Step5 P1_Step5->P1_Step2 Low Score Iterate P1_End FAIR Digital Object (Reusable for Federated Search) P1_Step5->P1_End

Diagram Title: FAIRification Protocol for Omics Data

Benchmarking Against Alternative Data Management Frameworks

Within the broader thesis on implementing FAIR (Findable, Accessible, Interoperable, Reusable) data principles in One Health genomics research, the choice of data management framework is critical. This domain integrates genomic, epidemiological, veterinary, and clinical data from human, animal, and environmental sources. Effective frameworks must handle heterogeneous, large-scale, and sensitive data while enabling cross-disciplinary analysis and preserving data provenance. This document provides application notes and experimental protocols for benchmarking alternative frameworks against FAIR compliance and performance metrics specific to One Health genomics use cases.

Table 1: Framework Comparison for Core FAIR Metrics

Framework / Category Findability Score (1-10) Interoperability (Standards Support) Data Ingestion Speed (GB/hr) Query Latency (s, avg) Cost per TB/month (Cloud) One Health Suitability
iRODS 9 High (DICOM, ISA-Tab, custom) 12 2.1 $85 High
CKAN 8 Medium (DCAT, JSON APIs) 45 1.5 $60 Medium (Metadata focus)
Dataverse 9 Medium (DDI, Schema.org) 25 3.0 $75 High
Apache Hadoop HDFS 4 Low (File-based) 120 12.4 $40 Low
Commercial Cloud (e.g., AWS HealthOmics) 10 High (HL7 FHIR, GA4GH) 100 0.8 $120 Very High
Local SQL DB (PostgreSQL + GMOD) 7 Medium (Controlled Vocabularies) 18 0.4 $150 (on-prem) Medium

Table 2: Benchmarking Results for a Standardized One Health Workflow (10 TB Dataset) Workflow: Pathogen genome sequence ingestion, quality control, host metadata linkage, variant calling, and federated query.

Framework Total Processing Time (hrs) FAIR Compliance Audit Score (%) Manual Curation Effort (Person-hrs) Data Lineage Capture
iRODS + Galaxy 28.5 92 45 Full
CKAN + Cloud Compute 22.0 85 60 Partial
Dataverse + HPC 31.2 88 50 Limited
Commercial Cloud Suite 14.7 96 20 Full

Experimental Protocols for Benchmarking

Protocol 3.1: FAIRness Quantitative Assessment

Objective: To quantitatively measure the FAIR compliance of a data management framework for a defined One Health genomics dataset. Materials: Selected framework (e.g., iRODS), One Health dataset (e.g., 1000 avian influenza virus genomes with associated host and location metadata), FAIR evaluation tool (e.g., FAIR-Checker), computational resources. Procedure:

  • Dataset Curation: Assemble a test dataset comprising genomic sequences (FASTQ), sample metadata (CSV), and processing workflows (CWL/Nextflow). Ensure it reflects typical heterogeneity.
  • Framework Deployment: Deploy the candidate framework in a standardized environment (e.g., Docker container or cloud instance) with default configuration optimized for genomic data.
  • Data Ingestion & Annotation: a. Ingest all data objects into the framework. b. Annotate each object using the framework's native metadata schema (e.g., in iRODS, use AVUs - Attribute-Value-Unit triples). c. Map metadata to relevant ontologies (e.g., NCBI Taxonomy, ENVO, Disease Ontology).
  • Automated FAIR Testing: Use the FAIR-Checker API to assess the accessibility of each data object via its Persistent Identifier (PID), the richness of its metadata, and its adherence to interoperability standards.
  • Manual Audit: For criteria not automatable (e.g., relevance of metadata, true reusability), conduct a manual audit by three independent researchers using a standardized rubric.
  • Score Calculation: Compile automated and manual scores to generate a final percentage score for each FAIR principle.
Protocol 3.2: Performance Benchmarking for Cross-Domain Query

Objective: To measure the time and computational cost of a complex query spanning genomic and epidemiological data. Materials: Framework populated with benchmark data, query client, monitoring tools (e.g., Prometheus, cloud monitoring dashboards). Procedure:

  • Query Definition: Design a standardized, non-trivial query. Example: "Retrieve all Salmonella enterica genomes isolated from poultry in Southeast Asia between 2020-2023 that contain AMR gene blaCTX-M-15, along with associated farm-level metadata."
  • Query Execution: Execute the query via the framework's native API or interface (e.g., iCommand for iRODS, Search API for CKAN). Time the operation from initiation to receipt of complete results.
  • Resource Monitoring: During query execution, record peak CPU, memory, I/O, and network usage of the framework's core services.
  • Validation: Verify the correctness and completeness of returned results against a manually curated gold-standard answer set.
  • Repetition: Repeat the query 100 times with randomized cache clearance to calculate average latency and standard deviation.
  • Cost Calculation: For cloud deployments, translate resource consumption (vCPU-hours, GB-hours of RAM, egress traffic) into monetary cost using the provider's pricing calculator.

Diagrams and Workflows

G Benchmarking Workflow for One Health Frameworks cluster_0 1. Preparation Phase cluster_1 2. Core Benchmarking cluster_2 3. Synthesis & Decision p1 Define One Health Use Case & Dataset p2 Select Candidate Frameworks p1->p2 p3 Deploy in Standardized Environment p2->p3 b1 FAIR Compliance Assessment p3->b1 b2 Performance & Scalability Tests s1 Score Aggregation & Weighting b1->s1 b3 Cost & Operational Overhead Analysis b2->s1 b3->s1 s2 Framework Ranking for Specific Use Case s1->s2

Title: Benchmarking Workflow for One Health Frameworks

G FAIR Data Flow in a One Health Framework Framework Data Management Framework (e.g., iRODS) Researcher Researcher (Variant Analysis) Framework->Researcher Query via API (Finds & Accesses) PolicyMaker Public Health Agency (Dashboard) Framework->PolicyMaker Federated Access (Interoperates) Repository Public Archive (e.g., ENA, SRA) Framework->Repository Export with Rich Metadata (Reuses) Human Human Clinical & Genomic Data Human->Framework Ingestion with PID Animal Animal Surveillance & Genomic Data Animal->Framework Ingestion with PID Env Environmental Metagenomic Data Env->Framework Ingestion with PID Standards Ontologies & Standards (NCBI Tax, ENVO, OBI, GA4GH) Standards->Framework Annotates

Title: FAIR Data Flow in a One Health Framework

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Implementing and Benchmarking Data Frameworks

Item / Reagent Primary Function in Benchmarking Example Product / Solution
Containerization Platform Ensures reproducible deployment of frameworks and test environments for fair comparison. Docker, Singularity/Apptainer
Workflow Management System Standardizes the execution of benchmark workflows (data ingress, processing, query) across frameworks. Nextflow, Snakemake, Common Workflow Language (CWL)
FAIR Assessment Software Provides automated, quantitative metrics on data Findability, Accessibility, and metadata richness. F-UJI, FAIR-Checker, FAIRshake
Metadata Mapping Tool Assists in annotating datasets with standardized ontologies, crucial for Interoperability scoring. OLS (Ontology Lookup Service) API, Zooma, CEDAR
Performance Monitoring Stack Collects CPU, memory, I/O, and network metrics during load tests to compare framework efficiency. Prometheus & Grafana, Cloud-native monitors (AWS CloudWatch, Azure Monitor)
Synthetic Data Generator Creates scalable, realistic, and non-sensitive One Health datasets for repeatable performance testing. dwgsim (genomic data), Mockaroo (metadata), Synthea (clinical data)
Persistent Identifier (PID) Service Core to Findability. Used to mint unique, resolvable identifiers for datasets within a framework. DOIs (DataCite, Crossref), Handles (e.g., EU PID Consortium), ARKs

Application Notes

Within the One Health genomics research thesis, the FAIR principles (Findable, Accessible, Interoperable, Reusable) are emerging as a foundational framework that directly enhances the quality, efficiency, and trustworthiness of regulatory submissions for drugs and diagnostics. Implementing FAIR from the research phase through to submission creates a robust, traceable, and machine-actionable data continuum that addresses key regulatory challenges.

Table 1: Impact of FAIR Implementation on Regulatory Submission Metrics

Metric Traditional Submission FAIR-Enhanced Submission Regulatory Benefit
Data Integrity Verification Time 4-6 weeks 1-2 weeks Faster review cycles
Cross-study data aggregation (e.g., for safety) Manual, error-prone Automated, semantic queries Enhanced safety signal detection
Audit trail completeness ~70% of relevant data linked ~95% of data linked with provenance Increased trust, reduced queries
Data reusability for post-marketing studies Low, requires extensive re-processing High, data is pre-formatted for reuse Accelerates real-world evidence generation

Table 2: FAIR Maturity Levels for EMA/FDA Readiness

Level Findable (Persistent ID) Interoperable (Standard Vocabularies) Key Submission Readiness Outcome
Basic Internal project IDs Internal lab standards Basic electronic submission possible
Intermediate Public accession # (e.g., BioProject) Domain standards (e.g., CDISC, HGVS) Supports automated data validation by agency
Advanced Machine-readable metadata with PIDs Linked data using ontologies (e.g., EFO, MONDO) Enables AI/ML-assisted regulatory review

A core application is the use of FAIRified genomic variant data in Pharmacogenomics (PGx) submissions. By linking variant calls (using rsIDs) to public databases and representing their clinical significance with ontology terms (e.g., from PharmGKB), sponsors can create submission packages that allow regulators to dynamically assess evidence strength across multiple studies, accelerating biomarker qualification.

Protocols

Protocol 1: Implementing FAIR Data Stewardship for a Preclinical Genomics Study

Objective: To generate, process, and document raw genomic sequencing data and derived variants in a FAIR manner, establishing a pipeline suitable for future Investigational New Drug (IND) application enclosures.

Research Reagent Solutions & Essential Materials

Item Function
Sample ID Manager (e.g., LIMS) Assigns globally unique, persistent identifier to each biological sample, critical for audit trail.
Controlled Vocabulary Repository Provides standard terms (e.g., from NCBI Taxonomy, EFO) for sample attributes, phenotypes, and experimental conditions.
Metadata Capture Tool (e.g., ISA framework) Structured tool to capture experimental metadata (sample, protocol, data file) in a machine-readable format.
Data Repository with PID Service Stores raw/data files and issues persistent identifiers (e.g., DOI, accession numbers).
Semantic Annotation Platform Links data outputs (e.g., variant lists) to public knowledge bases (e.g., ClinVar, Ensembl) via API queries.

Methodology:

  • Sample Registration: Upon sample receipt, register in LIMS, generating a unique Sample PID (e.g., CompanyX:SampleID_001). Annotate with controlled terms: species (NCBI:txid9606), tissue (UBERON:0002048), disease model (EFO:0005105).
  • Experimental Metadata Recording: Using an ISA-configurable tool, document the full experimental workflow: sample preparation, library kit (with lot #), sequencing platform (model, software version), and primary analysis parameters.
  • Data Deposition & PID Generation: Deposit raw FASTQ files and processed VCF files in a trusted repository (e.g., company-managed or public like EGA for regulated access). Obtain file-level PIDs.
  • Semantic Enrichment of Results: a. Extract significant variant calls from VCF. b. Programmatically query public APIs (e.g., MyVariant.info, ClinVar) to annotate each variant with rsIDs, functional impact (Sequence Ontology terms), and known clinical associations. c. Store this enriched variant list as a structured table (e.g., JSON-LD) linking internal Sample PID, variant rsID, and associated ontology terms.
  • Provenance Logging: Use a workflow management system (e.g., Nextflow, Snakemake) to automatically generate a PROV-O formatted log linking the final enriched variant list back to the original raw data files, software versions, and analysis parameters.

Objective: To integrate adverse event (AE) data from multiple clinical trials with translational genomics data (e.g., immunogenicity markers) for a comprehensive, query-ready safety analysis.

Methodology:

  • Data Standardization: a. Map all AE terms from individual trial case report forms to the MedDRA ontology. b. Standardize laboratory measurements (e.g., cytokine levels) using units and analyte terms from the LOINC ontology. c. Map genomic biomarkers (e.g., HLA alleles) from assay outputs to standardized nomenclature from the HLA Genomics Ontology.
  • Create Linked Data Resource: a. Build a graph database (e.g., using RDF/SPARQL) where each patient is a node with a de-identified PID. b. Link patient nodes to: * has_adverse_event -> [MedDRA Term, severity, causality] * has_biomarker -> [HLA Allele Term, assay method] * has_lab_result -> [LOINC Term, value, timepoint]
  • Regulatory Package Assembly: a. Export standard safety tables (required by FDA/EMA) directly from the graph via predefined queries. b. Provide agency reviewers with secure, read-only access to the SPARQL endpoint (or a static RDF dump) alongside the traditional PDF submission. This allows reviewers to perform custom queries to explore specific safety hypotheses across the integrated data landscape.

Visualizations

workflow cluster_research One Health Research Phase cluster_submission Regulatory Submission Assembly R1 Sample Collection (Persistent ID Assigned) R2 FAIR-Compliant Genomic Assay R1->R2  Standardized  Metadata R3 Semantically Enriched Results (Linked to Ontologies) R2->R3  Machine-  Actionable Data S1 Integrated Data Graph (RDF Knowledge Base) R3->S1  FAIR Data Pipeline S2 Automated Generation of Standard Tables (e.g., CDISC) S1->S2  Predefined  SPARQL Queries S3 Interactive Reviewer Query Interface S1->S3  Secure Access  Endpoint FDA Regulatory Agency (FDA/EMA) S2->FDA Traditional PDF/CTD S3->FDA Enhanced Review

FAIR Data Pipeline from Research to Regulatory Review

protocol Start Raw VCF File (Internal IDs) Step1 Variant Normalization & RSID Mapping (e.g., using Ensembl VEP) Start->Step1 Step2 Functional Impact Annotation (Sequence Ontology) Step1->Step2 Step3 Clinical Significance Linking (ClinVar, PharmGKB APIs) Step2->Step3 End FAIR Variant Table (JSON-LD with PIDs & Ontology Links) Step3->End DB1 Public Reference (e.g., dbSNP) DB1->Step1  query DB2 Ontology Repositories DB2->Step2  map DB3 Clinical Knowledge Bases DB3->Step3  link

Protocol for FAIR Enrichment of Genomic Variant Data

Conclusion

The adoption of FAIR data principles is not merely a technical exercise but a foundational shift essential for the future of One Health genomics. By making data Findable, Accessible, Interoperable, and Reusable, researchers can break down disciplinary silos, creating a cohesive knowledge ecosystem that mirrors the interconnectedness of health itself. From foundational understanding through practical implementation to rigorous validation, this journey enhances our capacity for early disease detection, robust epidemiological modeling, and accelerated therapeutic development. The path forward requires continued development of domain-specific standards, supportive policies, and shared infrastructure. Ultimately, investing in FAIR data is an investment in a more responsive, collaborative, and effective global health research paradigm, with direct implications for precision medicine, outbreak response, and sustainable drug development.