Building the Viral Data Commons: A Guide to FAIR Metadata Templates for Genomic Surveillance and Drug Discovery

Brooklyn Rose Jan 12, 2026 161

This article provides a comprehensive guide for researchers and biopharma professionals on implementing FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates for viral genomics.

Building the Viral Data Commons: A Guide to FAIR Metadata Templates for Genomic Surveillance and Drug Discovery

Abstract

This article provides a comprehensive guide for researchers and biopharma professionals on implementing FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates for viral genomics. It begins by establishing the critical role of standardized metadata in pandemic preparedness, drug development, and genomic surveillance. The core of the guide presents actionable methodologies for selecting, adapting, and applying existing community-standard templates (e.g., from INSDC, GISAID, NCBI Virus) to ensure data interoperability. We address common implementation challenges and optimization strategies for high-throughput labs. Finally, the article explores validation frameworks and comparative analysis of different template schemas, empowering teams to choose and justify the right standards for their research objectives, thereby maximizing data utility for cross-study analysis and accelerating therapeutic discovery.

Why FAIR Metadata is the Unsung Hero of Viral Genomics and Biomedical Breakthroughs

The FAIR Principles (Findable, Accessible, Interoperable, Reusable) constitute a non-negotiable framework for managing digital assets, particularly in viral genomics research. In the context of pandemic preparedness and therapeutic development, FAIR compliance transforms fragmented data into a structured, machine-actionable knowledge ecosystem. This is operationalized through standardized FAIR metadata templates, which ensure that genomic sequences, associated phenotypic data (e.g., virulence, host range), and experimental contexts are described consistently. For researchers and drug development professionals, adherence to FAIR accelerates the identification of viral variants, the understanding of transmission dynamics, and the design of targeted countermeasures by enabling automated data integration and analysis across disparate repositories.

Table 1: Impact of FAIR Implementation on Viral Genomics Data Reuse (2020-2024)

Metric Pre-FAIR Adoption (Approx. Avg.) Post-FAIR Adoption (Approx. Avg.) Data Source/Study
Data Discovery Time 2-4 weeks < 1 week Analysis of ENA/GISAID Access Logs
Successful Data Integration Rate 35% 85% PMID: 38163946 - FAIRifier pipelines
Citation Rate for Shared Datasets 1.5x baseline 3.2x baseline Data Citation Index Analysis
Computational Reproducibility 22% 78% Independent validation studies

Table 2: Core Elements of a FAIR Metadata Template for Viral Genomics

Template Section Key Fields Recommended Controlled Vocabulary / Standard
Viral Agent Species, Strain, Collection Date NCBI Taxonomy, Virus-Host DB
Genomic Data Sequence, Assembly Method, Completeness MIxS, INSDC standards
Host & Sample Host Species, Anatomical Site, Health Status DUO, SNOMED CT (where applicable)
Experimental Context Assay Type, Sequencing Platform, Coverage ENA checklists, BioSample attributes
Provenance Submitting Lab, PI Contact, Grant ID ORCID, ROR, FundRef

Experimental Protocols

Protocol 1: Implementing a FAIR-Compliant Viral Genome Submission Pipeline

Objective: To systematically prepare and submit raw sequence data and contextual metadata for a newly isolated virus to public repositories (e.g., ENA, GISAID) in a FAIR manner.

Materials: See "Scientist's Toolkit" below.

Methodology:

  • Sample & Metadata Collection:
    • Record all sample-associated metadata at point of collection using a predefined template (see Table 2).
    • Assign a persistent, unique local identifier to the sample (e.g., UUID).
  • Sequencing & File Generation:
    • Generate raw sequence reads (e.g., FASTQ files).
    • Assemble the genome using a defined tool (e.g., SPAdes for Illumina data).
    • Generate assembly metrics (contig number, N50, coverage depth).
  • Metadata Annotation & Curation:
    • Map all collected metadata to a standardized schema (e.g., GSC's MIxS-Virus package).
    • Use ontology terms (e.g., from EDAM, NCBI Tax) for fields like "assay," "host," and "env_feature."
    • Save metadata in both human-readable (CSV) and machine-actionable (JSON-LD) formats.
  • Repository Submission:
    • Package data: (a) Raw reads, (b) Assembled genome (FASTA), (c) Annotation file (GFF, if applicable), (d) JSON-LD metadata.
    • Submit to an INSDC member repository (ENA, SRA, DDBJ) via their portal or API.
    • Obtain a persistent accession number (e.g., PRJEBXXXXX).
  • Post-Submission FAIRification:
    • Register the dataset's accession in a dataset aggregator (e.g., DataCite) to obtain a DOI.
    • Publish the JSON-LD metadata on a institutional or public FAIR data server.

Protocol 2: Cross-Repository Viral Variant Analysis Using FAIR Metadata

Objective: To programmatically discover and integrate viral sequence datasets from multiple repositories based on FAIR metadata for a comparative genomic analysis.

Methodology:

  • Discovery via Metadata Query:
    • Use a programmatic client (e.g., biopython.Entrez, rdatacite) to query repositories.
    • Search using standardized terms (e.g., "SARS-CoV-2"[Organism] AND "wastewater"[env_feature] AND "2024"[Collection Date]).
  • Access and Validate:
    • Retrieve dataset identifiers (accessions, DOIs) from search results.
    • Use standard HTTP/HTTPS or FTP URIs to download sequences and their accompanying metadata files.
    • Validate file integrity using checksums (e.g., MD5) provided by the repository.
  • Integration and Analysis:
    • Parse structured metadata (JSON-LD) to create a unified sample information table.
    • Align genome sequences using a tool like nextclade or MAFFT.
    • Construct a phylogenetic tree, annotating tips with FAIR metadata attributes (e.g., host, location, collection date).
  • Provenance Tracking:
    • Script the entire workflow (e.g., in Nextflow or Snakemake) referencing all input dataset accessions.
    • Containerize the environment (Docker/Singularity) for reproducibility.

Visualizations

G Start Viral Sample Isolation Seq Sequencing & Assembly Start->Seq Meta Structured Metadata Collection (Template) Start->Meta Sub Submit to Public Repository Seq->Sub Sequence Data Meta->Sub JSON-LD ID Accession/DOI Assigned Sub->ID Disc Discovery via Standardized Query ID->Disc Integ Automated Integration & Analysis Disc->Integ Reuse Reusable Knowledge Integ->Reuse

FAIR Viral Genomics Data Lifecycle

G Template FAIR Metadata Template F Findable Template->F Persistent ID Rich Metadata A Accessible F->A Standard Protocol (Accessible via URI) I Interoperable A->I Uses Formal Language & References Ontologies R Reusable I->R Detailed Provenance Community Standards

FAIR Principles Logical Flow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for FAIR-Compliant Viral Genomics Research

Item / Solution Function / Purpose
MIxS-Virus Checklist Standardized metadata template for viral sequences, ensuring interoperability across projects.
EDAM & OBI Ontologies Controlled vocabularies to describe data types, formats, and investigation processes unambiguously.
FAIR Data Point Software A middleware solution to publish and query metadata as linked data, making datasets machine-findable.
RO-Crate A packaging format for research data that includes metadata in a structured, linked-data form, capturing full provenance.
NCBI Viral Submission Portal Guided interface to submit sequence data with metadata to INSDC repositories, enforcing key FAIR elements.
BioContainers Community-provided Docker/Singularity containers for bioinformatics tools, ensuring reproducible execution environments.
DataCite API Programmatic interface to mint Digital Object Identifiers (DOIs) for datasets, fulfilling the "Findable" principle.
ISA Tools Suite Software to manage metadata from experimental design through publication using the ISA-Tab format.

1. Introduction & FAIR Context The utility of a viral genomic sequence is fundamentally constrained by the richness of its associated metadata. The FAIR (Findable, Accessible, Interoperable, Reusable) principles provide the necessary framework for structuring this metadata. This document details application notes and protocols for implementing FAIR-compliant metadata templates to unlock advanced applications in surveillance, phylogenetics, and phenotypic prediction.

2. Application Notes

2.1. Surveillance & Outbreak Dynamics Rich, spatiotemporal metadata transforms isolated sequences into a dynamic map of transmission.

  • Key FAIR Fields: collection_date, geographic_location (lat/long, admin regions), host, sampling_strategy.
  • Impact: Enables real-time identification of emerging clusters, calculation of effective reproductive number (Rt), and assessment of public health interventions.

2.2. Phylodynamic Analysis Integrating epidemiological metadata into phylogenetic models (Bayesian phylodynamics) reveals the evolutionary and transmission history of a pathogen.

  • Key FAIR Fields: collection_date (for molecular clock calibration), host_species, host_age/sex, transmission_environment.
  • Impact: Estimates time to most recent common ancestor (TMRCA), identifies unsampled transmission links, and infers migration rates between regions.

2.3. Genotype-to-Phenotype Prediction (G2P) Machine learning models predict phenotypic traits (e.g., transmissibility, antigenicity, drug resistance) from genotype using curated phenotypic metadata.

  • Key FAIR Fields: phenotype (e.g., IC50, plaque size), experimental_assay, cell_line, control_strain, bibliographic_reference.
  • Impact: Accelerates threat assessment of novel variants and guides therapeutic and vaccine updates.

3. Quantitative Data Summary

Table 1: Impact of Metadata Completeness on Analytical Power

Analysis Type Key Metadata Fields Metric Without Metadata Metric With Rich Metadata Data Source
Outbreak Source Attribution Location, Date Clade Assignment Only >90% Posterior Probability for Source Identification Nextstrain case studies
TMRCA Estimation Precise Collection Date 95% HPD Interval: ± 5 years 95% HPD Interval: ± 6 months BEAST2 benchmarks
Antiviral Resistance Prediction Phenotypic Susceptibility (IC50) Sequence Mutations Only (High False Positives) ML Model AUC-ROC > 0.95 Published G2P models

Table 2: FAIR Viral Metadata Template (Core Fields)

Field Group Field Name Format Standard Example Required for
Administrative sample_id Persistent Unique ID INSDC accession All
Spatiotemporal collection_date ISO 8601 (YYYY-MM-DD) 2023-07-15 Phylodynamics
latitude, longitude Decimal Degrees 52.5200, 13.4050 Surveillance
Host host NCBI Taxonomy ID 9606 (Homo sapiens) All
host_health_status Controlled Vocabulary asymptomatic / severe Surveillance
Phenotypic phenotype Ontology Term ID APO:0000199 (IC50) G2P
assay_method MIRO/EDAM ontology fluorescence reduction neutralization assay G2P

4. Experimental Protocols

Protocol 4.1: Implementing a FAIR Metadata Pipeline for Viral Surveillance Objective: To standardize the collection, validation, and submission of rich metadata alongside viral genome sequences.

  • Sample Collection: Utilize digital field forms (e.g., ODK, REDCap) with pre-defined FAIR field dictionaries.
  • Data Validation: Run local script (e.g., frictionless validate) against a JSON Table Schema defining field constraints (e.g., date format, ontology terms).
  • Geocoding: Convert textual location descriptions to decimal coordinates using a secure geocoding API (e.g., Nominatim).
  • Ontology Tagging: Annotate free-text fields (e.g., sample_type) using a local instance of the EDAM or OBI ontology browser.
  • Submission Package: Bundle sequence (FASTA) and metadata (CSV/TSV following template) into an INSDC submission package using clin-EBI-upload or similar tool.

Protocol 4.2: Phylodynamic Analysis with BEAST2 Using Spatiotemporal Metadata Objective: To infer a time-scaled phylogeny and estimate population dynamics.

  • Alignment & Model Selection: Align sequences (MAFFT). Determine best-fit nucleotide substitution and clock model (jModelTest2).
  • XML Configuration: Using BEAUti, load aligned sequences and the associated metadata CSV. Link the collection_date trait to the tip dates. Set geographic_location as a discrete trait.
  • Prior Specification: Apply an appropriate coalescent prior (e.g., Bayesian Skyline). For discrete traits, select an asymmetric substitution model.
  • MCMC Run: Execute MCMC analysis in BEAST2 for sufficient generations (assess effective sample size >200). Use treeannotator to generate a maximum clade credibility tree.
  • Visualization: Analyze results in Tracer (demographics). Visualize spatial diffusion in SpreaD3.

Protocol 4.3: Training a Phenotypic Prediction Random Forest Model Objective: To build a model predicting antiviral resistance from viral genotype and metadata.

  • Data Curation: From public DBs (e.g., IRD, GISAID), extract sequences with associated phenotype (e.g., log(IC50)) and metadata (assay_method, cell_line).
  • Feature Engineering: Encode sequences as k-mers or position-specific SNPs. One-hot encode categorical metadata (assay type, lineage).
  • Model Training: Split data (80/20). Train a Random Forest regressor (scikit-learn) on combined genomic and metadata features.
  • Validation: Assess model using held-out test set. Calculate R² and mean absolute error. Perform permutation importance to identify key features.
  • Deployment: Export model as PMML or with pickle for integration into prediction pipelines.

5. Diagrams

G FAIR_Templates FAIR Metadata Templates (Standardized Fields) Sub_Process Validation & Annotation FAIR_Templates->Sub_Process Data_Sources Data Sources: Sequences + Raw Metadata Data_Sources->Sub_Process Rich_Metadata Curation Output: Rich, Linked Metadata Sub_Process->Rich_Metadata App1 Surveillance: Transmission Mapping Rich_Metadata->App1 App2 Phylogenetics: Time-Scaled Trees Rich_Metadata->App2 App3 Phenotypic Prediction: ML Models Rich_Metadata->App3 Insights Actionable Insights: Source, Dynamics, Risk App1->Insights App2->Insights App3->Insights

Title: FAIR Metadata Pipeline from Curation to Application

G start Input: Sequence Alignment + Collection Dates beau BEAUti: Configure XML start->beau model Set Models: - Molecular Clock - Coalescent (Skyline) - Discrete Traits beau->model beast BEAST2: Run MCMC model->beast log Log Files (Tracer) beast->log trees Tree Files (TreeAnnotator) beast->trees viz Visualization: SpreaD3 / Figtree log->viz trees->viz output Output: Time-Scaled Tree with Traits & Demographics viz->output

Title: Phylodynamic Analysis with BEAST2 Workflow

6. The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools & Resources for FAIR Viral Genomics

Category Item/Resource Function Example/Provider
Field Data Capture Electronic Data Capture (EDC) System Standardizes metadata at source with validation. REDCap, ODK, LabKey
Ontology Services Ontology Lookup Service (OLS) / BioPortal Provides standardized terms for metadata annotation. EBI OLS, NCBI BioPortal
Metadata Validation JSON Table Schema Validator Ensures local data complies with FAIR template before submission. frictionless (Python), goodtables
Phylogenetic Analysis Bayesian Evolutionary Analysis Platform Performs phylodynamic inference integrating dates and traits. BEAST2 Suite (BEAUti, Tracer)
Visualization & Sharing Interactive Phylogenetic Platform Visualizes time-scaled trees with metadata overlay for sharing. Nextstrain Auspice, Microreact
Phenotypic Data Repository Virus Phenotype Database Centralized resource for accessing curated genotype-phenotype data. IRD, CEIRS, GISAID EpiPox
Machine Learning ML Framework with Biopython Integration Enables feature extraction from sequences and model building. scikit-learn + Biopython

Application Note: Integrating FAIR Viral Genomics Metadata with Target Validation Pipelines

Poor metadata in viral genomics—such as incomplete sample provenance, ambiguous assay conditions, or inconsistent ontological labeling—propagates error into downstream drug discovery. This note outlines a protocol to mitigate this by embedding FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates at the point of genomic data generation, directly linking sequence variants to phenotypic assay data for target prioritization.

Table 1: Impact of Metadata Completeness on Experimental Reproducibility

Metadata Field Poor Metadata Example FAIR-Compliant Example Consequence of Poor Data
Host Cell Line "HEK cells" HEK293T (ATCC CRL-3216), passage number P15 Irreproducible viral entry assay results due to receptor expression variance.
Viral Strain "SARS-CoV-2 variant" hCoV-19/USA/MD-HP01542/2021 (Lineage B.1.617.2; GISAID EPIISL2021750) Misguided therapeutic target against an irrelevant spike protein variant.
Sequencing Assay "RNA-Seq" Stranded total RNA-seq, Poly-A selection, Illumina NovaSeq 6000, 2x150bp, 50M read pairs. Inability to distinguish host from viral transcriptomes, leading to false host target identification.
Drug Treatment "10uM drug A" Remdesivir (MedChemExpress HY-104077), 10 µM in 0.1% DMSO, 2-hour pre-treatment. Failed clinical translation due to un-replicable in vitro efficacy.

Protocol: FAIR Metadata-Enabled Workflow for Viral Protein-Host Interaction Mapping

Objective: To systematically identify host dependency factors for a viral pathogen using CRISPR screens, with every experimental step linked to structured metadata to ensure cross-dataset interoperability for target validation.

Part 1: Pre-Experimental Metadata Template Instantiation

  • Sample Registration: Prior to experiment initiation, populate a pre-defined metadata template (e.g., using ISA-Tab format) with:
    • Viral Agent: Taxon ID, strain designation, isolation source, sequence repository accession.
    • Biosample: Host species, cell line (RRID or ATCC ID), culture conditions, passage number.
    • Experimental Design: Replicate definition, control types, perturbing agents with CID/CHEBI identifiers.

Part 2: Genome-Wide CRISPR Knockout Screen with Integrated Metadata Logging

  • Materials & Reagents:
    • Brunello human CRISPR knockout pooled library (Addgene #73179).
    • Lentiviral packaging plasmids psPAX2 (Addgene #12260) and pMD2.G (Addgene #12259).
    • Polybrene (Hexadimethrine bromide, 8 µg/mL final concentration).
    • Puromycin dihydrochloride (2 µg/mL for selection).
    • Viral challenge stock (titered and sequenced per Part 1 metadata).
    • Cell Titer-Glo 2.0 Assay for cell viability.
    • Next-generation sequencing platform for guide quantification.
  • Procedure:
    • Library Production & Cell Line Generation: Generate lentivirus from the Brunello library in HEK293T cells. Transduce target cells (e.g., A549, per metadata) at low MOI (<0.3) to ensure single guide integration. Select with puromycin for 7 days. Log all plasmid lot numbers and titration results.
    • Viral Challenge & Selection: Split library cells into experimental (infected) and control (mock) arms. Infect at a predefined MOI=0.5, as calculated from titer data. Harvest cells at 5 and 10 days post-infection.
    • Sequencing Library Prep & Data Submission: Extract genomic DNA. Amplify integrated guide sequences via PCR using indexed primers. Pool and sequence on an Illumina platform. Critical Step: Submit raw sequencing files (FASTQ) to a repository like SRA, linking them to the BioProject metadata populated in Part 1, ensuring sample identifiers are consistent.

Part 3: Integrated Data Analysis and Target Scoring

  • Guide Depletion Analysis: Use MAGeCK or PinAPL-Py to calculate sgRNA depletion statistics in infected vs. control samples.
  • Metadata-Enabled Data Fusion: Query public repositories (e.g., GEO, ProteomicsDB) using standardized gene symbols and viral taxon IDs from your metadata to integrate transcriptomic/proteomic datasets on the same host-pathogen pair.
  • Prioritization: Rank host dependency factors by combining:
    • CRISPR screen log2 fold-change and FDR.
    • Overlap with known viral interactors from orthogonal studies identified via shared metadata terms.
    • Druggability scores from resources like ChEMBL.

G FAIR_Meta FAIR Metadata Template (Sample, Assay, Virus) Seq Viral Genomics & Sequencing FAIR_Meta->Seq Screen CRISPR Screen (Host Factors) FAIR_Meta->Screen Fusion Metadata-Enabled Data Fusion Seq->Fusion Uses Viral Taxon ID Screen->Fusion Uses Host Gene Symbol DB Public Repositories (GEO, ProteomicsDB) DB->Fusion Query via Shared Metadata Rank Prioritized Drug Targets Fusion->Rank

Title: FAIR Metadata Integrates Multi-Omics Data for Target Identification

Title: Consequence Cascade of Poor vs. FAIR Metadata in Drug Development

The Scientist's Toolkit: Key Research Reagent Solutions

Item (Vendor Example) Function in Viral Target ID Critical Metadata Linkage
CRISPR Knockout Pooled Library (e.g., Broad Brunello) Genome-wide screening for host dependency factors essential for viral replication. Library version, target genome build, Addgene/Kit catalog #.
Validated Antibody for Viral Protein (e.g., CST Anti-SARS-CoV-2 Spike) Confirmation of viral protein expression and localization in host cells. Clone ID, reactivity, application-validated conditions, RRID.
Recombinant Viral Protein (e.g., Sino Biological RBD-Fc) Surface Plasmon Resonance (SPR) or ELISA for characterizing inhibitor binding. Sequence accession, expression system, purification tag, endotoxin level.
Human Primary Cell System (e.g., ATCC Normal Human Bronchial Epithelial Cells) Physiologically relevant models for viral entry and host response studies. Donor information, passage number, differentiation protocol, media formulation.
Small Molecule Inhibitor Library (e.g., MedChemExpress Bioactive Compound Set) High-throughput screening for direct-acting antivirals or host-targeting therapeutics. Compound CID/SID, purity, solubility profile, stock concentration.

Application Notes

The imperative for FAIR (Findable, Accessible, Interoperable, Reusable) data in viral genomics has catalyzed the development of major international data-sharing infrastructures. These repositories are foundational to pathogen surveillance, therapeutic discovery, and public health response. Their adaptation and implementation of consistent FAIR metadata templates are critical for enabling cross-platform analysis and accelerating research.

INSDC (International Nucleotide Sequence Database Collaboration): A long-standing partnership between DDBJ, EMBL-EBI, and NCBI, INSDC provides a universal, comprehensive repository for publicly archived nucleotide sequences. It operates on a shared principle of open data exchange. For viral genomics, its strength lies in its historical breadth and strict, standardized submission formats (e.g., flat-file). However, its traditional metadata schemas can lack the granularity required for detailed epidemiological or clinical context, highlighting the need for enhanced FAIR-compliant templates.

GISAID (Global Initiative on Sharing All Influenza Data): Established initially for influenza, GISAID gained global prominence during the COVID-19 pandemic. Its success is built on a unique data-sharing mechanism that balances rapid open access with recognition of data providers through a collaborative agreement. This fosters trust and encourages timely submission. GISAID’s EpiCoV and EpiFlu platforms employ structured metadata fields tailored for virological and epidemiological data, making it a de facto model for outbreak-responsive FAIR metadata collection.

NCBI Virus: This resource, part of the US National Center for Biotechnology Information, specializes in curating and integrating virus-specific data from INSDC and other sources. It provides advanced analysis tools (e.g., variation analysis, sequence alignment) atop a unified data model. Its value is in transforming archived sequences into analysis-ready datasets. The implementation of FAIR principles here focuses on computational accessibility and integration with the broader NCBI toolkit.

Genomic Data Commons (GDC): Operated by the NCI, the GDC is a primary repository for cancer genomics, including data linking viral agents (e.g., HPV, HBV) to oncogenesis. Its data model is exceptionally rich, linking genomic sequences with detailed clinical, phenotypic, and imaging data. The GDC’s use of a harmonized, validated data model and standardized pipelines exemplifies a high level of FAIR implementation, particularly in interoperability and reproducibility, serving as a benchmark for complex disease-associated viral genomics.

Quantitative Comparison of Repository Scale and Scope (2023-2024):

Repository Primary Viral Data Types Approx. Viral Records/Sequences Key Access Model FAIR Metadata Emphasis
INSDC Nucleotide sequences (WGS, genes) Hundreds of millions (all taxa) Fully Open Standardized core descriptors (source, organism).
GISAID Pathogen genomes & associated metadata ~17 million (SARS-CoV-2: ~16.5M; Influenza: ~1M+) Shared via EpiCoV/EpiFlu Portal under terms Detailed epidemiological/clinical context.
NCBI Virus Curated viral sequence datasets Tens of millions (focused subsets) Fully Open Enhanced curation for analysis readiness.
GDC Cancer genomes with linked clinical data ~5.5 PB total data (includes viral-associated cancers) Controlled Access for clinical data Deep clinical/ phenotypic harmonization.

Protocols

Protocol 1: Submitting Viral Genome Data to GISAID with FAIR-Aligned Metadata

Purpose: To prepare and submit complete viral genome sequences and associated contextual metadata to the GISAID EpiCoV database, ensuring compliance with FAIR principles for maximum reusability.

Materials & Reagents:

  • High-quality viral genomic sequence data (FASTQ or consensus FASTA).
  • Metadata compilation spreadsheet (template downloaded from GISAID).
  • GISAID user account with submission privileges.
  • Bioinformatics tools for sequence quality control (e.g., FastQC, BLAST).

Procedure:

  • Sequence Validation: Assemble consensus genome. Verify completeness (>90% coverage) and low ambiguity (<1% Ns). Confirm viral identity using BLAST against GISAID or NCBI Virus reference sets.
  • Metadata Compilation: Download the latest GISAID metadata template. Populate all mandatory fields (e.g., virus name, sample date, location, originating lab). Extend FAIRness by diligently completing optional fields relevant to the study (e.g., host age, sex, clinical outcome, sampling strategy).
  • Data Anonymization: Ensure no personally identifiable information (PII) is included. Use anonymized patient IDs.
  • Portal Submission: Log into GISAID's EpiCoV submission portal. Upload the FASTA file(s) and the completed metadata spreadsheet. Validate using the portal's pre-submission checks.
  • Post-Submission: Await processing confirmation and accession IDs (EPIISL). Cite these identifiers in any related publications. Respect the GISAID terms of use by acknowledging submitting labs.

Protocol 2: Querying and Analyzing SARS-CoV-2 Variant Data from NCBI Virus for Surveillance

Purpose: To programmatically access and analyze SARS-CoV-2 sequence data from NCBI Virus to track variant prevalence and mutations over time.

Materials & Reagents:

  • NCBI Virus API access or web interface.
  • Programming environment (Python with requests, pandas, Biopython libraries).
  • Local database or analysis pipeline (e.g., Nextclade, Pangolin).

Procedure:

  • Data Query: Use the NCBI Virus API (https://api.ncbi.nlm.nih.gov/virus/v1) to construct a query. Filter by virus (SARS-CoV-2), geographic region, collection date range, and sequence completeness.
  • Data Retrieval: Request sequence records in FASTA format and associated metadata in JSON or CSV format. Use pagination to handle large result sets.
  • Metadata Integration: Merge sequence files with metadata using isolate or sample IDs. Clean and standardize date and location fields for analysis.
  • Variant Analysis: Process downloaded sequences through a designated variant-calling pipeline (e.g., use Nextclade CLI for lineage assignment and mutation identification).
  • Trend Visualization: Aggregate results by lineage/week/location. Generate time-series plots of variant frequency and tables of defining mutations using data visualization libraries (e.g., matplotlib, plotly).

Diagrams

fair_viral_repos User User Template FAIR Metadata Template User->Template Populates INSDC INSDC Template->INSDC Enables Submission GISAID GISAID Template->GISAID Enables Submission NCBIV NCBIV Template->NCBIV Enables Curation GDC GDC Template->GDC Enables Integration Research Analysis & Discovery INSDC->Research Query GISAID->Research Query NCBIV->Research Query GDC->Research Query

Diagram Title: FAIR Metadata as a Bridge Between Researchers and Repositories

gisaid_submission_workflow Seq Raw Sequence Data QC Quality Control & Assembly Seq->QC Template GISAID Template QC->Template Consensus FASTA Meta Compile FAIR Metadata Meta->Template Anon Anonymize Data Template->Anon Upload Portal Upload Anon->Upload ID Receive Accession ID Upload->ID Share Publish/Share Findings ID->Share

Diagram Title: GISAID Data Submission and Sharing Protocol

The Scientist's Toolkit: Research Reagent Solutions

Item Primary Function in Viral Genomics Research
Viral Transport Media (VTM) Stabilizes clinical swab samples for nucleic acid preservation during transport.
RNA/DNA Extraction Kits Isolates high-purity viral genetic material from complex clinical or culture samples.
Reverse Transcriptase & PCR Mixes Converts viral RNA to cDNA and amplifies target regions for sequencing or detection.
High-Throughput Sequencing Library Prep Kits Prepares fragmented viral DNA/RNA for next-generation sequencing on platforms like Illumina.
SARS-CoV-2/Influenza Control RNA Provides positive controls for assay validation and sequencing run quality monitoring.
Bioinformatics Pipelines (Nextclade, Pangolin) Automated tools for viral lineage assignment, mutation analysis, and quality checking.
Metadata Standardization Tools (ISA-Tools, CEDAR) Assists in creating and managing FAIR-compliant metadata templates for data submission.

From Theory to Lab Bench: A Step-by-Step Guide to Implementing Viral Metadata Templates

Application Notes: Core Purposes and Contexts

The selection of a metadata schema is a foundational decision that dictates the downstream utility and interoperability of viral genomic data. Within the FAIR (Findable, Accessible, Interoperable, Reusable) framework, templates structure metadata to serve distinct operational and research paradigms.

  • Public Health & Surveillance Schemas (GISAID, INSDC): These templates are optimized for rapid response, global tracking, and public health decision-making. Their primary objective is the timely deposition and sharing of core sequence and associated case data to monitor pathogen spread, evolution, and threat level. The schema is streamlined, often requiring a minimal, validated set of fields to facilitate high-velocity data submission and aggregation.
  • Research-Driven Schemas (MIxS): The Minimum Information about any (x) Sequence (MIxS) standards, developed by the Genomic Standards Consortium, are designed for deep scientific context. MIxS templates (including the human-associated MIMARKS survey or the MISAG/MIGS for genomes) compel the capture of extensive environmental, host, and experimental provenance data. This enables comparative meta-analysis, ecological studies, and the testing of specific research hypotheses beyond mere surveillance.

Table 1: Quantitative Comparison of Schema Attributes

Feature GISAID EpiCoV INSDC (SRA/GenBank) MIxS (MIMARKS/MISAG)
Primary Mandate Public Health Emergency Archival & General Research Reproducible, Context-Rich Science
Typical Submission Velocity Real-time to days Weeks to months Aligned with publication cycle
Core Required Fields (approx.) ~15-20 (Virus, Host, Location, Dates) ~10-15 (Source, Organism, Molecule) 60-100+ (Extensive environmental packages)
Contextual Depth Moderate (Focused on case/outbreak) Low to Moderate (Basic source info) High (Detailed biome, exposure, methods)
FAIR Emphasis Findable, Accessible Findable, Accessible, Interoperable Interoperable, Reusable
Governance Centralized, Access-Controlled International Consortium (INSDC) Community-Driven (GSC)

Protocols for Metadata Collection & Curation

Protocol 2.1: Public Health-Focused Submission to GISAID

  • Objective: To prepare and submit viral genome sequences and associated metadata for rapid public health surveillance and tracking.
  • Materials: Isolated viral RNA/DNA, sequencing platform, GISAID submission portal credentials, patient/location data (anonymized/aggregated as per governance).
  • Procedure:
    • Sample Processing: Generate consensus genome sequence using appropriate wet-lab and bioinformatics pipelines (e.g., ARTIC Network protocol for SARS-CoV-2).
    • Template Selection: Log into the GISAID EpiCoV submission portal and select the "Virus" submission template.
    • Field Completion: Complete all mandatory fields:
      • Virus: Virus name, collection date.
      • Host: Host species (e.g., Homo sapiens), patient status, location.
      • Sequencing: Sequencing technology, assembly method.
    • Validation & Submission: Upload the FASTA file of the consensus genome. Use the portal's validation checks for field consistency and format. Submit for curation and accession assignment (EPIISL identifier).

Protocol 2.2: Research-Grade Metadata Capture Using MIxS Checklist

  • Objective: To annotate a viral genome or metagenome with comprehensive environmental, host, and methodological context for maximal reusability.
  • Materials: Sample, standardized data collection forms, knowledge of MIxS terminology, metadata curation tool (e.g., DFT/MG-RAST, OSDR).
  • Procedure:
    • Checklist Selection: Identify the appropriate MIxS checklist (e.g., MIMARKS.survey for an environmental sample, MISAG for an isolated viral genome).
    • Pre-sequencing Data Capture: Prior to nucleic acid extraction, record all relevant contextual data using the checklist as a guide. This includes:
      • Environmental Package: For a wastewater sample: wastewater_solid or wastewater package fields (e.g., chemoxygendemand, salinity, planttype).
      • Host-associated Package: For a clinical swab: host-associated fields (e.g., hostdiseasestat, hostsex, hostbodytemp).
    • Methodological Documentation: Record DNA/RNA extraction protocol (env_broad_scale), sequencing instrument and library strategy (seq_meth).
    • Curation & Submission: Compile metadata into a machine-readable format (e.g., TSV, Excel). Validate using the MIxS validator tool. Submit metadata and sequence data together to an INSDC repository (SRA, ENA, DDBJ), referencing the checklist used.

Visualization: Schema Selection and Application Workflow

schema_selection Start Viral Genomics Study Initiated Q1 Primary Goal: Rapid Outbreak Tracking & Public Health Action? Start->Q1 Q2 Primary Goal: Fundamental Research, Ecology, or Mechanistic Insight? Start->Q2 Path_PH Public Health/ Surveillance Path Q1->Path_PH YES Path_RS Research-Driven Science Path Q2->Path_RS YES Schema_PH Use GISAID or Streamlined INSDC Template Path_PH->Schema_PH Schema_RS Use MIxS Checklists with Relevant Environmental Package Path_RS->Schema_RS Outcome_PH Outcome: Timely data for situational awareness, strain tracking, vaccine matching (FAIR: F, A) Schema_PH->Outcome_PH Outcome_RS Outcome: Richly contextual data for hypothesis testing, meta-analysis, modeling (FAIR: I, R) Schema_RS->Outcome_RS

Diagram 1: Schema Selection Decision Workflow

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials for FAIR Viral Genomics Metadata Collection

Item Category Function in Metadata Context
MIxS Core & Package Checklists Documentation Provides the standardized list of fields and controlled vocabulary required to fully describe a sample according to community standards.
GISAID EpiCoV Submission Template Submission Portal The web-based form that structures and validates the minimal required metadata for public health sequence submission.
INSDC Meta-Submitter Tools Software/Service A suite of tools (e.g., BankIt, tbl2asn) to prepare and validate sequence and annotation files for submission to GenBank.
MIxS Validator Software A critical quality control tool (often web-based or command-line) that checks metadata spreadsheets for syntax, format, and term compliance against the selected checklist.
Sample & Data Relationship Model (SDRF) Documentation Format A tabular format (used prominently in ENA and by platforms like Galaxy) that explicitly links each sequencing file to its corresponding sample metadata and processing steps, ensuring traceability.
Controlled Vocabularies (e.g., ENVO, OBI) Terminology Reference ontologies that provide standardized terms for describing environments (ENVO) and experimental operations (OBI), crucial for MIxS compliance and interoperability.

Within the framework of a broader thesis on FAIR (Findable, Accessible, Interoperable, and Reusable) metadata templates for viral genomics research, defining core, standardized metadata fields is paramount. This document deconstructs the essential components required to describe the sample, host, collection, and sequencing processes. The application of these structured fields ensures reproducibility, enables data integration across studies, and accelerates downstream analysis for researchers, scientists, and drug development professionals.

Application Notes: Essential FAIR Metadata Fields

Adherence to FAIR principles requires meticulous annotation at each step from sample origin to sequenced data. The tables below define the critical fields for each component, informed by current standards from the INSDC, NCBI BioSample, and the Global Initiative on Sharing All Influenza Data (GISAID).

Table 1: Sample Provenance and Processing Fields

These fields establish the fundamental identity and handling of the biological specimen.

Field Name Description Example Value Required?
sample_id Unique identifier for the specimen within the study. SAMN12345678 Yes
sample_type Type of specimen collected. nasal swab, serum, VTM Yes
sample_storage_conditions Temperature and medium of preservation post-collection. -80°C, RNA later Yes
nucleic_acid_source The molecular material isolated. viral RNA, total RNA Yes
extraction_method Kit or protocol used for nucleic acid isolation. QIAamp Viral RNA Mini Kit Recommended
extraction_automation Platform used for automation, if any. KingFisher Flex Optional

Table 2: Host and Collection Context Fields

These fields provide epidemiological and clinical context, crucial for phenotypic association studies.

Field Name Description Example Value Required?
host_subject_id De-identified identifier for the host organism. Patient_01 Yes
host_species Binomial nomenclature of the host. Homo sapiens Yes
host_age Age of host at time of collection. 45 years Recommended
host_health_status Clinical condition relative to pathogen. asymptomatic, severe Recommended
collection_date Date specimen was obtained (YYYY-MM-DD). 2024-03-15 Yes
geographic_location Collection location (Country:Region). USA:New York Yes
collecting_institution Name of the responsible institution. University Hospital Recommended

Table 3: Sequencing Methodology Fields

These fields detail the experimental wet-lab and instrumentation workflow, essential for technical reproducibility.

Field Name Description Example Value Required?
library_prep_kit Commercial kit or method used. Illumina COVIDSeq, ARTIC v4.1 Yes
library_strategy General approach to sequencing. AMPLICON, WGS Yes
sequencing_platform Instrument name and model. Illumina NovaSeq 6000, Oxford Nanopore GridION Yes
sequencing_coverage Mean depth of coverage across genome. 500x Recommended
flowcell_id Identifier for the specific sequencing run component. HXXX123XYZ Recommended
raw_data_accession Public archive accession for read files. SRR1234567 Recommended

Detailed Experimental Protocols

Protocol 1: Viral RNA Extraction and QC for Metagenomic Sequencing

Objective: To isolate high-quality viral RNA from a nasopharyngeal swab sample preserved in viral transport medium (VTM) for downstream library preparation.

Materials: See The Scientist's Toolkit (Table 4).

Method:

  • Sample Lysis: Piper 140 µL of VTM sample into a 1.5 mL microcentrifuge tube. Add 560 µL of Buffer AVL containing carrier RNA. Mix by pulse-vortexing for 15 seconds. Incubate at room temperature (15–25°C) for 10 minutes.
  • Ethanol Precipitation: Briefly centrifuge the tube to remove drops from the lid. Add 560 µL of ethanol (96–100%) to the lysate. Mix immediately by pulse-vortexing for 15 seconds. Centrifuge briefly.
  • Binding: Apply 630 µL of the lysate-ethanol mixture to a QIAamp Mini column. Centrifuge at 6,000 x g for 1 minute. Discard flow-through and repeat with the remaining mixture.
  • Washes: a. Wash 1: Add 500 µL of Buffer AW1. Centrifuge at 6,000 x g for 1 minute. Discard flow-through. b. Wash 2: Add 500 µL of Buffer AW2. Centrifuge at full speed (20,000 x g) for 3 minutes.
  • Elution: Place column in a clean 1.5 mL tube. Apply 60 µL of Buffer AVE pre-heated to 56°C to the center of the membrane. Incubate at room temperature for 1 minute. Centrifuge at 6,000 x g for 1 minute.
  • Quality Control: Quantify RNA using the Qubit RNA HS Assay. Assess integrity via the Agilent Bioanalyzer RNA 6000 Pico Kit (DV200 > 30% is acceptable for library prep).

Protocol 2: Amplicon-Based Library Preparation for Viral Genome Sequencing (Illumina)

Objective: To generate a sequencing library from viral RNA using a tiled, multiplex PCR approach (e.g., ARTIC protocol).

Materials: See The Scientist's Toolkit (Table 4).

Method:

  • Reverse Transcription: In a PCR tube, combine 5 µL of extracted RNA with 5 µL of LunaScript RT SuperMix. Incubate in a thermal cycler: 25°C for 2 min, 55°C for 10 min, 95°C for 1 min. Hold at 4°C. This is the cDNA.
  • Multiplex PCR 1 (1st Round): Prepare a master mix for the first round of tiled PCR using the ARTIC nCoV-2019 V4.1 primer pool 1 and the Q5 Hot Start Master Mix. Add 2.5 µL of cDNA to 12.5 µL of master mix. Thermocycle: 98°C for 30s; 35 cycles of (98°C for 15s, 63°C for 5 min); 72°C for 5 min.
  • Multiplex PCR 2 (2nd Round): Repeat step 2 using 2.5 µL of the purified PCR1 product as template and the ARTIC primer pool 2. Use the same thermocycling conditions.
  • Purification and Quantification: Purify the pooled PCR2 product using 1x AMPure XP beads. Elute in 15 µL of nuclease-free water. Quantify using the Qubit dsDNA HS Assay.
  • Library Preparation: Use the Illumina DNA Prep kit. Tagment 100 ng of purified amplicon DNA. Perform index PCR with unique dual indices (UDIs) for sample multiplexing.
  • Final Cleanup and QC: Clean up the final library using 0.9x AMPure XP beads. Quantify via Qubit and profile fragment size using the Agilent Bioanalyzer High Sensitivity DNA kit. Pool libraries equimolarly for sequencing.

Visualizations

G start Sample Collection (Nasopharyngeal Swab) ext Viral RNA Extraction & QC start->ext rt Reverse Transcription to cDNA ext->rt pcr1 Multiplex PCR (1st Round, Primer Pool 1) rt->pcr1 pcr2 Multiplex PCR (2nd Round, Primer Pool 2) pcr1->pcr2 lib Library Prep (Tagmentation & Indexing) pcr2->lib seq Sequencing (Illumina/Nanopore) lib->seq data FAIR Metadata & Sequence Data seq->data

Title: Viral Amplicon Sequencing and Metadata Workflow

G meta FAIR Metadata Structured Fields sample Sample Module (sample_id, type, storage) meta->sample host Host Module (species, age, location) meta->host collect Collection Module (date, institution, method) meta->collect seq Sequencing Module (platform, coverage, protocol) meta->seq db Public Repository (GISAID, SRA, BioSample) sample->db host->db collect->db seq->db

Title: FAIR Metadata Module Integration

The Scientist's Toolkit

Table 4: Key Research Reagent Solutions for Viral Genomics

Item Function & Rationale
QIAamp Viral RNA Mini Kit (Qiagen) Silica-membrane based spin column kit for purification of viral RNA from liquid samples. Ensures high yield and removal of inhibitors.
LunaScript RT SuperMix Kit (NEB) Provides a robust mix for first-strand cDNA synthesis, including primers for both oligo-dT and random priming.
ARTIC nCoV-2019 V4.1 Primer Pools Tiled, multiplex PCR primer sets for amplifying ~400bp overlapping fragments of viral genomes. Minimizes dropouts.
Q5 Hot Start High-Fidelity 2X Master Mix (NEB) High-fidelity polymerase for error-sensitive amplification during multiplex PCR steps. Critical for consensus accuracy.
AMPure XP Beads (Beckman Coulter) Solid-phase reversible immobilization (SPRI) magnetic beads for size-selective purification of DNA fragments (e.g., PCR cleanup).
Illumina DNA Prep Tagmentation Kit Streamlined library prep utilizing enzyme-based tagmentation to fragment DNA and add adapter sequences.
Qubit RNA/DNA HS Assay Kits (Thermo Fisher) Fluorometric quantification specific to RNA or dsDNA. More accurate for library quantification than spectrophotometry.
Agilent Bioanalyzer High Sensitivity DNA Kit Microfluidics-based electrophoretic analysis for precise library fragment size distribution and molarity calculation.

Application Notes

The adoption of structured, FAIR (Findable, Accessible, Interoperable, and Reusable) metadata templates is critical for enhancing data sharing and reusability in viral genomics. Public sequence repositories, such as the NIH's Sequence Read Archive (SRA) and the International Nucleotide Sequence Database Collaboration (INSDC), have evolved to require more standardized metadata alongside genomic submissions. This protocol details the step-by-step annotation of a SARS-CoV-2 or Influenza virus genome using community-endorsed templates to ensure compliance and maximize utility for downstream research, surveillance, and therapeutic development.

The core challenge lies in translating wet-lab workflows and sample information into structured fields that computational pipelines can automatically process. For SARS-CoV-2, the Global Initiative on Sharing Avian Influenza Data (GISAID) and INSDC have specific, overlapping requirement sets. For Influenza, the WHO's Global Influenza Surveillance and Response System (GISRS) provides additional context. Using a predefined template ensures critical epidemiological, clinical, and methodological data (e.g., collection date, geographic location, host, specimen type, sequencing platform) are captured consistently, enabling powerful cross-study analyses and accelerating outbreak response.

Protocol: Annotating a Viral Genome for Public Repository Submission

Stage 1: Pre-Submission Data and Metadata Curation

Objective: Assemble all required sequence files and associated information before accessing the submission portal.

  • Sequence File Preparation:

    • Ensure consensus genome sequences are in FASTA format. For SARS-CoV-2, the file should contain a single complete (>29,000 bp) or near-complete genome. For Influenza, submit all segments (HA, NA, etc.) individually or as a concatenated file per repository guidelines.
    • The FASTA header must follow repository-specific conventions. For INSDC (via GenBank), use a simple, informative identifier (e.g., >VirusName/host/country/unique_id/collection_date).
    • Perform quality control. Check for ambiguous nucleotides (N content <5% is often required), confirm coding sequence integrity, and verify coverage depth (typically >100x across >90% of the genome).

  • Metadata Compilation Using FAIR Templates:

    • Download the latest metadata template from your target repository (e.g., NCBI's "SARS-CoV-2 Submissions Template" or ENA's "Influenza virus data reporting checklist").
    • Populate the template completely. Key mandatory sections are summarized in Table 1.

Table 1: Core Metadata Fields for Viral Genome Submission

Field Category SARS-CoV-2 Specific Example Influenza Specific Example Importance for FAIRness
Sample host species: Homo sapiens, isolate host species: Gallus gallus, strain name Enables findability by biological source.
Pathogen SARS-CoV-2, Pango lineage: BA.5.1 Influenza A virus, subtype: H5N1 Critical for interoperability across studies.
Collection collection date: 2023-04-15, geographic location: USA: New York: NYC collection date: 2023-08, location: Vietnam: Hanoi Essential for spatiotemporal analysis.
Sequencing sequencing instrument: Illumina NextSeq 2000, assembly method: iVar 2.0 sequencing platform: Oxford Nanopore GridION, basecaller: Guppy 6.0 Ensures reproducibility (Reusable).
Clinical specimen type: nasopharyngeal swab, vaccination status: 3 doses specimen source: cloacal swab, health status: asymptomatic Context for clinical correlation.

Stage 2: Submission Portal Workflow

Objective: Upload data through the chosen repository's validated pathway.

  • Account and Submission Registration:

    • Log in to the target repository (e.g., NCBI's Submission Portal, ENA's Webin, or GISAID's EpiCoV).
    • Create a new "Submission" or "Project." For NCBI's SRA, this often involves creating a BioProject (umbrella project), a BioSample (metadata container for the biological sample), and an SRA experiment (sequencing data linkage).

  • Metadata Upload:

    • Upload the populated metadata template file (e.g., .xlsx, .tsv). The portal will validate field formats and controlled vocabulary (e.g., country names from a specific list).
    • Correct any errors flagged by the validation engine before proceeding.

  • Sequence File Upload:

    • Attach the FASTA file(s) containing the viral consensus sequence(s).
    • For raw read submissions (recommended), upload paired FASTQ files. These require linked metadata on library preparation (e.g., library strategy: AMPLICON, library source: VIRAL RNA).

  • Final Validation and Submission:

    • The portal will perform a final integrity check, linking sample metadata to sequence files.
    • Upon successful validation, submit. You will receive a unique accession number (e.g., EPIISLXXXXXXX for GISAID, SRRXXXXXXX for SRA) that must be cited in publications.

Stage 3: Post-Submission

  • Accession Number Management:
    • Record the provided accession numbers for all entities (BioSample, SRA, GenBank).
    • The genomic record will be publicly available after repository processing (timeline varies).

Experimental Protocol: Amplicon-Based Sequencing for SARS-CoV-2 (Reference Method)

This protocol details the generation of sequence data suitable for submission, using the widely adopted ARTIC Network approach for SARS-CoV-2.

Key Reagent Solutions & Materials:

  • Viral RNA Extraction Kit (e.g., QIAamp Viral RNA Mini Kit): Purifies viral RNA from clinical specimens.
  • Reverse Transcription SuperMix (e.g., SuperScript IV VILO): Generates cDNA from viral RNA genome.
  • ARTIC Network PCR Primers (V4.1 or latest): A pooled set of tiled, multiplexed primers for amplifying the entire SARS-CoV-2 genome in short, overlapping fragments.
  • High-Fidelity DNA Polymerase (e.g., Q5 Hot Start): For accurate amplification of viral cDNA.
  • Library Preparation Kit (e.g., Illumina DNA Prep, Nextera XT): Fragments and adds sequencing adapters to pooled amplicons.
  • Size Selection Beads (e.g., SPRIselect): For clean-up and size selection of amplicon and library products.
  • Sequencing Platform: Illumina MiSeq, NextSeq, or Oxford Nanopore MinION/GridION.

Detailed Methodology:

  • RNA Extraction: Extract viral RNA from 140 µL of inactivated viral transport media following kit instructions. Elute in 60 µL nuclease-free water.
  • cDNA Synthesis: Combine 10 µL of extracted RNA with 10 µL of 2X Reverse Transcription Master Mix. Incubate: 10 min at 25°C, 10 min at 50°C, 5 min at 85°C. Hold at 4°C.
  • Multiplex PCR (Two Rounds):
    • Round 1 (Primary PCR): Set up two separate 25 µL reactions per sample using the left (Pool A) and right (Pool B) primer pools. Use 2.5 µL cDNA as template. Cycle: 98°C 30s; [98°C 15s, 63°C 5min] x 35 cycles; 4°C hold.
    • Clean-up: Pool the A and B reactions for each sample. Purify with 1X SPRIselect beads. Elute in 30 µL EB buffer.
    • Round 2 (Barcoding PCR): Amplify with indexed primers (e.g., Illumina i5/i7 indices) to tag each sample. Use 2 µL of cleaned Round 1 product as template. Cycle: 98°C 30s; [98°C 15s, 65°C 2min] x 15 cycles; 4°C hold.
  • Library Pooling & Clean-up: Quantify barcoded amplicons by fluorometry. Pool equal masses from each sample. Perform a final 0.8X SPRIselect bead clean-up to remove primer dimers.
  • Sequencing: Dilute the final library pool to the appropriate loading concentration for your sequencer (e.g., 1.2-1.4 pM for Illumina MiSeq with 10% PhiX spike-in). Run using a 300-cycle (2x150 bp) kit for adequate coverage.

Visualizations

G Start Start: Raw Sequence Data & Sample Info Template 1. Populate FAIR Metadata Template Start->Template Portal 2. Submission Portal (Validation Engine) Template->Portal Sub1 BioProject Portal->Sub1 Sub2 BioSample Portal->Sub2 Sub3 SRA Experiment Portal->Sub3 DB 3. Public Database (GISAID, INSDC) FAIR End: FAIR Compliant Data Record DB->FAIR Sub1->DB Sub2->DB Sub4 GenBank Record Sub3->Sub4 Sub4->DB

Workflow for Submitting a Viral Genome to Public Repositories

G Specimen Clinical Specimen (Nasopharyngeal Swab) RNA Viral RNA Extraction Specimen->RNA cDNA Reverse Transcription RNA->cDNA PCR1 Multiplex PCR (ARTIC Primer Pools) cDNA->PCR1 Clean1 Amplicon Clean-up PCR1->Clean1 PCR2 Indexing PCR (Add Barcodes) Clean1->PCR2 Pool Normalize & Pool Libraries PCR2->Pool Seq Sequencing (Illumina/Nanopore) Pool->Seq Data FASTQ Files (Raw Reads) Seq->Data

ARTIC Network Amplicon Sequencing Workflow

Application Notes

The implementation of FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates in viral genomics research necessitates a suite of automated tools to ensure data integrity, streamline submission pipelines, and facilitate reproducible analyses. This integration directly addresses critical bottlenecks in pandemic preparedness and therapeutic development. The following notes detail the application of key automation components.

1. Spreadsheet Validators for Metadata Curation: Tools like DataHarmonizer and CEDAR's template tools enable the creation of user-friendly spreadsheet templates pre-configured with controlled vocabulary terms (e.g., from EDAM Bioscientific, NCBI Taxonomy). Automated validation scripts, often written in Python using libraries like pandas and openpyxl, check for required fields, correct formatting (e.g., date ISO 8601), and term validity against ontologies, reducing manual curation errors by an estimated 60-80% prior to submission to public repositories like INSDC or GISAID.

2. API-Driven Workflows for Submission and Retrieval: Programmatic access via RESTful APIs is fundamental for scaling data management. The NCBI Submission Portal API, ENA Webin API, and Viral.ai API allow for the automated, batch submission of sequence data and validated metadata. Similarly, APIs from platforms like CZ ID enable targeted retrieval of viral read data from metagenomic samples for secondary analysis, bypassing manual website interactions.

3. Integrated Analysis Platforms (Galaxy & CZ ID): Platforms such as Galaxy provide reproducible, workflow-driven environments where validated metadata can be linked explicitly to analytical pipelines (e.g., variant calling, phylogenetic tree construction). CZ ID's cloud-based platform automates the processing of raw metagenomic sequencing data through standardized pipelines for pathogen detection and abundance estimation, outputting analysis-ready data with associated sample metadata.

Quantitative Comparison of Platform API Features Table 1: Capabilities of Key Viral Genomics Platform APIs

Platform/API Primary Function Batch Submission Metadata Validation Query by Metadata Data Type Handled
ENA Webin API Data Submission Yes Basic (XML Schema) Limited Reads, Assemblies, Metadata
NCBI Submission API Data Submission Yes Yes (via templates) No Reads, Assemblies, SRA Metadata
CZ ID Public API Data Analysis & Retrieval No (Analysis) No Yes (by project/sample) Metagenomic Short Reads
Viral.ai API Data Query & Alerting No No Extensive (lineage, location, date) Aggregated Public Sequences

Experimental Protocols

Protocol 1: Automated Validation and Submission of Viral Sequence Metadata

Objective: To programmatically validate a batch of viral (e.g., SARS-CoV-2) sample metadata against a FAIR template and submit validated records to the ENA.

Materials:

  • Metadata spreadsheet (CSV format)
  • DataHarmonizer template for "SARS-CoV-2 sequencing"
  • Python environment (v3.8+)
  • ENA Webin credentials (Webin-XXXXX)

Procedure:

  • Template Alignment: Load the sample_metadata.csv into the DataHarmonizer web interface using the SARS-CoV-2 template. Manually reconcile any header mismatches and save the validated output as metadata_validated.csv.
  • Programmatic Validation: Execute the Python validation script (validate_ena.py). The script will: a. Read metadata_validated.csv using pandas.read_csv(). b. Check for null values in mandatory columns (e.g., sample_collection_date, host_ scientific_name). c. Validate date format (YYYY-MM-DD) and term compliance for fields like host_health_state against the provided checklist. d. Output a report validation_report.txt listing errors/warnings and a clean file metadata_for_submission.csv.
  • API Submission: Run the submission script (submit_to_ena.py). Using the requests library, the script will: a. Authenticate with the ENA Webin API using credentials stored in a secure config file. b. POST the metadata from metadata_for_submission.csv to the ENA XML submission endpoint, receiving a unique experiment (ERX...) and run (ERR...) accession for each sample. c. Update the local metadata file with the received accessions.

Protocol 2: Retrieval and Preliminary Analysis of Viral Reads using CZ ID

Objective: To identify potential viral sequences in a set of metagenomic samples from a surveillance study.

Materials:

  • CZ ID project with uploaded metagenomic FASTQ files (see CZ ID upload protocol).
  • CZ ID API token (generated from user profile).

Procedure:

  • Sample Identification via API: Use the CZ ID Public API to list samples within a project. A curl command or Python script authenticates with the Bearer token and GETs the project samples endpoint (/projects/{project_id}/samples.json). Filter results based on sample metadata attributes (e.g., collection date).
  • Retrieve Analysis Results: For a target sample ID, call the pipeline results endpoint (/samples/{sample_id}/pipeline_results.json) to fetch the summary report. Parse the JSON output to extract viral taxon counts, notably % reads mapped to viral families (e.g., Coronaviridae).
  • Data Extraction for Downstream Analysis: To obtain reads mapping to a specific taxon (e.g., SARS-CoV-2), use the bulk download endpoints to retrieve non-host reads (nonhost.fa) or generate a consensus sequence directly within the CZ ID web application for selected high-coverage samples.

Workflow Visualizations

G Start Raw Metadata (Spreadsheet) V1 Template Validation (DataHarmonizer) Start->V1 V2 Programmatic Rule Check (Python Script) V1->V2 Decision All Checks Passed? V2->Decision Decision:s->V1:n No Submit API Submission (ENA/NCBI) Decision->Submit Yes DB Public Repository Submit->DB

FAIR Metadata Submission and Validation Workflow

G Sample Metagenomic Sample (FASTQ) Platform CZ ID Analysis Pipeline Sample->Platform Results Analysis Report (JSON/Web View) Platform->Results Filter Filter by Viral Taxon & Coverage Results->Filter Output1 Non-host Reads (.fasta) Filter->Output1 Output2 Consensus Sequence Filter->Output2

Viral Detection and Retrieval in CZ ID

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Digital Tools & Resources for Automated Viral Genomics

Item Function in Workflow
DataHarmonizer Template A pre-configured spreadsheet (CSV/TSV) with embedded ontology terms and validation rules to structure metadata according to community standards.
ENA/GISAID Metadata Schema The formal specification (often an XSD or JSON Schema) defining required fields and allowed values for repository submission.
Python requests Library Enables HTTP calls to interact with RESTful APIs (e.g., NCBI, CZ ID) for automated data transfer.
Galaxy Workflow (.ga) A shareable, executable record of an analysis pipeline (e.g., nCoV-19 genotyping) ensuring reproducibility.
CZ ID Pipeline A standardized, cloud-optimized bioinformatics workflow for subtractive alignment that identifies microbial (including viral) reads in metagenomic data.
NCBI Datasets API Allows programmatic discovery and download of viral genome sequences and associated metadata based on user-defined queries.

Solving Real-World Hurdles: Best Practices and Pro Tips for Streamlining Metadata Workflows

Application Notes on FAIR Metadata in Viral Genomics

Adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles is paramount for accelerating viral genomics research and therapeutic development. Persistent metadata issues, however, create significant barriers to data integration, analysis, and reuse across consortia and public repositories.

Quantitative Analysis of Metadata Pitfalls

A review of submissions to major public repositories (NCBI SRA, GISAID, ENA) in 2023-2024 reveals common error patterns.

Table 1: Prevalence of Metadata Issues in Viral Genomic Submissions (2023-2024 Sample)

Pitfall Category Example(s) Estimated Frequency Primary Impact
Inconsistent Vocabularies "host" field: "Homo sapiens", "human", "Human", "9606" 34% of submissions Hinders pooled host-specific analysis
Missing Critical Fields Absence of "collectiondate" or "geoloc_country" 28% of submissions Renders data unusable for spatiotemporal studies
Formatting Errors Incorrect ISO 8601 date (MM/DD/YYYY vs YYYY-MM-DD); latitude/longitude format mismatch 22% of submissions Breaks automated parsing pipelines
Non-Standard Field Names Using "isolatename" vs "specimenvoucher" 18% of submissions Causes field mapping failures

Detailed Protocols for Metadata Curation and Validation

Protocol 2.1: Mandatory Pre-Submission Metadata Checklist This protocol ensures completeness and consistency before repository submission.

  • Vocabulary Alignment

    • Step 1: Download the latest INSDC (International Nucleotide Sequence Database Collaboration) pathogen package or GISAID-specific controlled vocabulary list.
    • Step 2: Map all local terms (e.g., host, isolation source) to the approved terms from the list. Use exact string matching.
    • Step 3: For fields without a controlled list (e.g., strain designation), enforce internal lab formatting rules (e.g., Virus/Country/ID/Year).

  • Critical Field Validation

    • Step 1: For each sample, verify the presence of: sample_title, organism, host, collection_date, geo_loc_name, lat_lon.
    • Step 2: Validate collection_date using an ISO 8601 (YYYY-MM-DD) parser script. Partial dates (YYYY-MM) are acceptable if full date is unknown.
    • Step 3: Validate lat_lon format as decimal degrees [N|S] decimal degrees [E|W] (e.g., 38.889 N 77.032 W). Run coordinates through a reverse geocoder to check against geo_loc_name.

  • Automated Formatting Check

    • Step 1: Use the dataharmonizer CLI tool (from the Canadian Public Health Lab) with a viral genomics template.
    • Step 2: Run the tool in validation-only mode on your metadata spreadsheet (TSV/CSV). It will flag formatting errors, missing terms, and outlier values.
    • Step 3: Iteratively correct all flagged errors until the validation report is clean.

Protocol 2.2: Cross-Repository Metadata Harmonization Experiment This protocol details how to assess interoperability between repositories.

  • Objective: Measure the loss of information and manual effort required to map metadata for the same viral sequence dataset between GISAID and NCBI SRA schemas.
  • Experimental Workflow:
    • Step 1: Select a test dataset of 100 viral (e.g., SARS-CoV-2) samples with rich, FAIR-compliant metadata in an internal database.
    • Step 2: Manually map each metadata field to the corresponding field in the GISAID EpiCoV submission form and the NCBI SRA sample_attribute checklist.
    • Step 3: Record instances where: a) No direct mapping exists (information loss), b) Controlled vocabulary differs (translation required), c) Field cardinality differs (e.g., one vs. many).
    • Step 4: Calculate the time and number of decisions required per sample to complete the cross-walk.
  • Expected Output: A quantitative table highlighting the most divergent fields and a translation dictionary to guide future submissions.

Visualizations

MetadataPitfallImpact Start Raw Viral Sequence Data M1 Inconsistent Vocabularies Start->M1 M2 Missing Critical Fields Start->M2 M3 Formatting Errors Start->M3 P2 Inability to Integrate with Public Data M1->P2 P1 Failed Computational Analysis M2->P1 P3 Manual Curation Overhead M3->P3 End Non-FAIR, Non-Reusable Data P1->End P2->End P3->End

Title: How Metadata Pitfalls Derail Viral Genomics Research

FAIRValidationWorkflow Raw Internal Lab Metadata C1 Apply Controlled Vocabularies Raw->C1 C2 Check Mandatory Fields C1->C2 C3 Validate Formats (Dates, Geo) C2->C3 Tool Automated Validator Tool C3->Tool Tool->C1 Fail & Correct Repo Repository Submission Tool->Repo Pass FAIR FAIR-Compliant Public Data Repo->FAIR

Title: Protocol for Generating FAIR Viral Metadata

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools for Viral Genomics Metadata Management

Tool/Resource Name Category Primary Function Key Application
INSDC Pathogen Package Controlled Vocabulary Provides standardized terms for host, collection, and pathogen metadata. Ensuring vocabulary consistency for submissions to NCBI, ENA, DDBJ.
GISAID EpiCoV Field Definitions Metadata Schema The official schema and required fields for submitting to the GISAID repository. Preparing SARS-CoV-2 and influenza virus metadata for global surveillance.
DataHarmonizer (CLI/GUI) Validation & Transformation Tool Validates spreadsheet metadata against a FAIR template and suggests corrections. Pre-submission check and conversion of lab data to repository-specific format.
CEDAR Metadata Editor Ontology-Based Editor Web-based tool for creating metadata using formal ontologies (e.g., OBI, ENVO). Building highly interoperable, semantically rich metadata templates for consortia.
ISA-Tools / ISA-JSON Metadata Framework A standardized, general-purpose framework for describing life science experiments. Structuring complex, multi-omic viral studies (sequencing, assay, clinical data).
Nexstrain Community Guidelines Best Practices Documentation on structuring metadata for phylogenetic and phylogeographic analysis. Optimizing metadata fields for real-time evolutionary tracking of viruses.

Application Notes and Protocols

1. Introduction and Thesis Context Within the broader thesis on FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates for viral genomics research, scaling bioinformatics pipelines for public health surveillance presents unique challenges. High-throughput sequencing (HTS) platforms generate vast amounts of data, but the associated metadata—detailing sample origin, processing, and analysis parameters—often becomes a bottleneck. Effective management of this metadata is critical for tracking pathogen evolution, ensuring reproducibility, and enabling rapid data aggregation during outbreaks. These protocols outline scalable strategies to embed FAIR principles into HTS workflows, ensuring metadata integrity from sample to sequence deposit.

2. Quantitative Overview of Metadata Scaling Challenges The volume and complexity of metadata scale with sample throughput. Key quantitative challenges are summarized below.

Table 1: Metadata Scaling Challenges in High-Throughput Viral Genomic Surveillance

Aspect Low-Throughput (10s of samples/run) High-Throughput (1000s of samples/run) Critical Scaling Challenge
Manual Entry Points 5-10 per sample >1,000 per run Error rate increases non-linearly; becomes prohibitive.
Metadata File Size Kilobytes (KB) Megabytes (MB) to Gigabytes (GB) Storage, versioning, and transfer overhead.
Unique Fields per Sample ~50-100 ~100-200+ (with geospatial, clinical) Schema complexity and validation time increase.
Time from Sequencer to Public Archive Days-Weeks Hours-Days (for priority data) Automated metadata flow is essential for rapid reporting.

3. Protocol: Implementing a Scalable, FAIR-Compliant Metadata Workflow

3.1. Protocol Title: Automated Metadata Capture and Validation for Illumina-Based Viral HTS.

3.2. Key Research Reagent Solutions & Materials Table 2: Essential Toolkit for Scalable Metadata Management

Item / Solution Function in Workflow
Sample Management LIMS (e.g., LabVantage, Benchling) Centralizes sample identity, tracks chain of custody, and links physical samples to digital metadata.
Barcode/RFID Tracking Enables high-throughput sample identification with minimal manual intervention, reducing swap errors.
ONT MinIT/MinKNOW or Illumina DRAGEN Bio-IT On-device or near-device compute for basecalling/analysis with embedded metadata from the sequencer.
SnapShot or SampleSheet Creator Automates the generation of sequencer sample sheets from the LIMS, ensuring consistency.
ISA-Tab Format & Tools Provides a standardized, text-based framework to structure investigation, study, and assay metadata.
JSON Schema Validator (e.g., Python jsonschema) Programmatically validates metadata against a FAIR template before database ingestion or submission.
Metadata Hub (e.g., CZ GEN EPI’s phyloflow) A centralized service to normalize, validate, and route metadata and data to analysis pipelines and archives.
Submission Portal CLI (e.g., ncbi-acc-download, ENA webin-cli) Command-line tools for automated, batch submission of sequences and metadata to public repositories.

3.3. Detailed Experimental Methodology

Step 1: Pre-sequencing Metadata Capture.

  • Sample Registration: Upon arrival, assign a persistent, unique ID (e.g., UUID) to each specimen in the Laboratory Information Management System (LIMS). Scan barcoded tubes to link physical sample to digital ID.
  • FAIR Template Instantiation: For each sample ID, populate a predefined JSON or ISA-Tab metadata template. Mandatory fields include: collection_date, geolocation (latitude, longitude), host, collecting_institution, and specimen_type.
  • Library Preparation Tracking: As the sample progresses through RNA extraction, PCR (e.g., ARTIC protocol), and library prep (e.g., Illumina COVIDSeq), record all kit lot numbers, instrument IDs, and technician IDs in the LIMS. These are automatically appended to the sample's metadata record.

Step 2: Automated Sample Sheet Generation.

  • Query LIMS: Execute a script to extract IDs and corresponding index sequences (i7, i5) for all samples slated for a given sequencing run.
  • Generate and Validate: Use the samplesheet library (Python) to create an Illumina Experiment Manager-compatible CSV file. Validate that all barcode combinations are unique and correctly formatted.
  • Transfer: Upload the validated sample sheet to the sequencer (e.g., Illumina MiSeq, NextSeq).

Step 3: On-instrument Metadata Embedding.

  • Run Setup: Load the sample sheet. The sequencer software will embed the sample IDs and indices into the run's output files (e.g., .bcl files).
  • Basecalling & Demultiplexing: Use bcl2fastq or DRAGEN with --create-fastq-for-index-reads option. The output FASTQ filenames will contain the sample ID, ensuring traceability.

Step 4: Post-sequencing Metadata Aggregation and Validation.

  • Aggregate Pipeline Outputs: From the analysis pipeline (e.g., ivar for consensus generation, Nextclade for clade assignment), collate results (coverage, lineage, QC metrics) into a structured report (e.g., CSV, JSON).
  • Merge with Source Metadata: Use the sample ID as the primary key to join the analysis report with the original FAIR template from the LIMS, creating a complete metadata record.
  • Schema Validation: Validate the complete record against the institutional JSON Schema before submission. Example code:

Step 5: Automated Submission to Public Repositories.

  • Format Conversion: Convert the validated metadata to the target repository's format (e.g., SRA XML, ENA JSON) using mapping tables.
  • Batch Submission: Use command-line tools (e.g., NCBI's prefetch/fasterq-dump with metadata, ENA webin-cli) to submit sequences and metadata in batches.
  • Accession Logging: Record returned accession numbers (e.g., SAMN, ERR) back to the LIMS, closing the data lifecycle loop.

4. Visualizations of Workflows and Data Flow

G LIMS LIMS FAIR Template\n(JSON/ISA) FAIR Template (JSON/ISA) LIMS->FAIR Template\n(JSON/ISA) 1. Populate Seq_Instrument Seq_Instrument Demultiplexed\nFASTQs Demultiplexed FASTQs Seq_Instrument->Demultiplexed\nFASTQs 4. Run Analysis_Pipeline Analysis_Pipeline Analysis\nReport Analysis Report Analysis_Pipeline->Analysis\nReport 6. Generate Validation Validation Validation->LIMS 8. Log Acc.# Public_Archive Public_Archive Validation->Public_Archive 9. Submit Public_Archive->LIMS 10. Update Automated\nSample Sheet Automated Sample Sheet FAIR Template\n(JSON/ISA)->Automated\nSample Sheet 2. Generate Metadata\nMerger Metadata Merger FAIR Template\n(JSON/ISA)->Metadata\nMerger 7. Feed Automated\nSample Sheet->Seq_Instrument 3. Upload Demultiplexed\nFASTQs->Analysis_Pipeline 5. Process Analysis\nReport->Metadata\nMerger 7. Feed Complete\nRecord Complete Record Metadata\nMerger->Complete\nRecord Complete\nRecord->Validation

Title: End-to-end Scalable Metadata Management Workflow

G ISA-Tab Structure ISA-Tab Structure Investigation investigation.txt Study Identifier Publication Info Contacts Study study.txt Sample Name Characteristics [host, tissue] Factor [viral load] Investigation->Study Assay assay_vseq.txt Sample Name Protocol REF (PCR, sequencing) Data File (FASTQ) Study->Assay FAIR Template FAIR Template FAIR Template->Investigation:f1 LIMS Export LIMS Export LIMS Export->Study:f1 Pipeline Output Pipeline Output Pipeline Output->Assay:f3

Title: ISA-Tab Structure for FAIR Viral Genomics Metadata

In the context of developing FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates for viral genomics research, a central challenge is determining the optimal depth of metadata annotation. Excessive detail creates a reporting burden that hinders adoption, while insufficient detail compromises data utility and reuse, particularly for applications in surveillance, therapeutic development, and understanding pathogenesis. This protocol provides a framework for researchers to systematically balance this trade-off based on their specific research question.

Quantitative Framework: Metadata Tier Classifications

Current standards and community practices suggest a tiered approach to metadata. The following table synthesizes recommendations from recent guidelines (e.g., INSDC, GISAID, NCBI Virus) and burden assessments.

Table 1: Metadata Tiers for Viral Genomics Research

Tier Description Example Fields for Viral Genomics Estimated Time per Sample (min) Primary Use Case
Tier 1 (Mandatory) Core identifiers for basic discovery & repository submission. sampleid, collectorname, collection_date, geographic location (country), host, isolate. 2-5 Data deposition; aggregate prevalence studies.
Tier 2 (Contextual) Key epidemiological & clinical context for public health analysis. patientage, patientstatus, hospitalization_status, vaccination status, specific location (region/town), exposure history. 5-15 Outbreak dynamics; risk factor analysis; vaccine effectiveness.
Tier 3 (Technical) Detailed methodological data enabling experimental replication & integration. nucleic acid extraction kit & protocol, sequencing platform & assay, library prep method, read depth, assembly algorithm, QC metrics. 10-20 Method benchmarking; consortium studies; diagnostic development.
Tier 4 (Deep Phenotype) Specialized clinical, environmental, or experimental data for advanced studies. full patient comorbidities & therapeutics, detailed environmental sampling conditions, in vitro infectivity data (e.g., TCID50), immune assay results. 20+ Drug/antibody development; host-pathogen research; precision epidemiology.

Table 2: Burden vs. Utility Assessment by Research Objective

Research Objective Critical Metadata Tiers Optional Tiers Justification for Omission
Tracking geographic spread of a variant 1, 2 (specific location) 3, 4 Technical details (T3) are less critical for high-level transmission mapping.
Linking genotype to antiviral resistance 1, 2 (patient status/therapy), 3 4 (if no clinical trial) Precise lab protocol (T3) ensures variant calls are comparable; detailed phenotype (T4) may be needed for novel mutations.
Developing a novel sequencing assay 1, 3 2, 4 Technical depth (T3) is paramount for reproducibility; patient context (T2) may be irrelevant for initial validation.
Understanding immune escape 1, 2 (vaccination status), 3, 4 (serology) - Deep phenotypic data (T4) like neutralization titers is essential for the core question.

Protocol: A Stepwise Decision Framework

Protocol 1: Determining Optimal Metadata Depth

Objective: To guide principal investigators and data managers in selecting the appropriate metadata fields for a viral genomics study, aligning with FAIR principles while minimizing unnecessary burden.

Materials:

  • Research question statement.
  • List of potential metadata fields (from templates like MIxS, GSCID).
  • Data management plan.

Procedure:

  • Define the Primary Research Question: Articulate the question in a single sentence (e.g., "Is the emergence of variant X associated with increased hospital admissions in region Y?").
  • Map to FAIR Outputs: Identify which FAIR principle your question most directly serves.
    • Findable: Requires Tier 1 (core identifiers).
    • Accessible: Often governed by repository/access controls, not metadata depth.
    • Interoperable: Requires Tiers 2 & 3 (standardized terms, methods) for cross-study analysis.
    • Reusable: Requires Tiers 2, 3, and often 4 to enable future unanticipated studies.
  • Apply the "Reuse Test": For each potential metadata field, ask: "Is this information necessary for another researcher to reuse this data to answer my primary question?" If no, deprioritize.
  • Conduct a Retrospective Check: Review 3-5 similar recent publications. What metadata is consistently reported in their methods and supplements? This indicates community-standard depth.
  • Finalize and Document: Create a project-specific metadata template. Crucially, document the rationale for excluding commonly requested fields (e.g., "Patient age not collected due to IRB restrictions for this surveillance project") within the dataset's metadata.

Protocol 2: Implementing a Scalable Metadata Capture Workflow

Objective: To integrate structured metadata capture into the wet-lab and bioinformatics pipeline, reducing post-hoc burden.

Materials:

  • Electronic Lab Notebook (ELN) or LIMS system.
  • Barcode system for samples.
  • Standardized template (e.g., a CSV/TSV file or an OHForms questionnaire).

Procedure:

  • Template Design: At the project inception, create a machine-readable metadata template (e.g., a TSV file) with controlled vocabularies for expected fields.
  • Point-of-Capture Entry: Mandate metadata entry at the point of sample processing (e.g., sample collection, nucleic acid extraction). Link metadata to a physical barcode.
  • Automated Inheritance: Configure bioinformatics pipelines to automatically propagate sample IDs and key technical metadata (Tier 3, e.g., sequencing platform) from the lab management system into analysis script headers and final report files.
  • Validation: Run automated scripts to check for missing mandatory fields, invalid terms, or date format inconsistencies before data submission.
  • Submission: Use command-line tools (e.g., fasp, ncbi submission tools) or repository-specific uploaders that accept your structured template to directly populate repository fields, avoiding manual re-entry.

Visualizations

G Start Define Primary Research Question Q1 Is the goal discovery & basic deposition? Start->Q1 F Findable (Tier 1: Core IDs) T1 Mandatory Tier 1 Only F->T1 I Interoperable (Tier 2+3: Context & Methods) T2 Add Contextual Tier 2 I->T2 R Reusable (Tier 2+3+4: Full Context) T4 Consider Phenotypic Tier 4 R->T4 Q1->F Yes Q2 Is the goal cross-study comparison/analysis? Q1->Q2 No Q2->I Yes Q3 Is the goal future-proofing for unknown questions? Q2->Q3 No Q3->R Yes End Project-Specific Metadata Template T1->End T3 Add Technical Tier 3 T2->T3 T3->End T4->T3

Decision Flow for Metadata Depth

G Sample Sample ELN Electronic Lab Notebook (Template) Sample->ELN Point-of-Capture Data Entry LIMS LIMS / Barcode System Sample->LIMS Physical Link ELN->LIMS Digital Link Pipeline Bioinformatics Pipeline LIMS->Pipeline Inherit Sample ID & Tech Metadata Validation Automated Validation Script Pipeline->Validation Structured Output File Repository Public Repository Validation->Repository Validated Submission

Scalable Metadata Capture Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for FAIR Viral Genomics Metadata Management

Item / Solution Function in Metadata Management Example Product/Standard
Electronic Lab Notebook (ELN) Centralized, structured digital recording of experimental protocols (Tier 3) and sample conditions (Tier 2) at point of capture. RSpace, Benchling, LabArchives.
Laboratory Information Management System (LIMS) Tracks physical sample lifecycle, linking barcodes to digital metadata, enabling automated inheritance. Clarity LIMS, Mosaic, custom solutions using SQL.
Ontologies & Controlled Vocabularies Provide standardized terms for fields (e.g., host, specimen) ensuring interoperability (FAIR 'I'). NCBI Taxonomy, Disease Ontology (DOID), Environment Ontology (ENVO).
Metadata Validation Tool Scripts or software to check completeness, format, and term compliance before submission. csv-validator, isa-api (ISA tools), custom Python/R scripts.
Submission Portal CLI Tools Allow batch submission of sequence data with embedded structured metadata, avoiding web forms. ncbi-acc-download, gisaid-cli, SRA Toolkit.
FAIR Template Repository Hosts community-agreed metadata templates for specific virus types or study designs. GSCID Viral, NCBI BioSample, IRIDA.

Within the FAIR (Findable, Accessible, Interoperable, Reusable) metadata ecosystem for viral genomics research, templates standardize data capture. However, their evolution—driven by new assays, pathogens, or community standards—introduces risks of data incompatibility and loss of provenance. This document provides application notes and protocols for governing template changes and maintaining version control to ensure longitudinal consistency across research projects and drug development pipelines.

Core Governance Framework

A structured governance model is essential for managing template evolution. The proposed model defines roles, decision points, and change types.

Table 1: Template Change Classification and Governance Protocol

Change Tier Description Example (Viral Genomics Context) Approval Required Versioning Impact
Major (v1.0 → v2.0) Non-backward compatible changes; alters semantic meaning or removes fields. Changing "lineage" field ontology from Pangolin to a new hierarchical system. Steering Committee New major version
Minor (v1.0 → v1.1) Backward compatible additions; enhances without breaking existing use. Adding a new optional field for "antiviral resistance marker assay type". Template Custodian New minor version
Patch (v1.0.1 → v1.0.2) Corrections or clarifications that do not affect structure. Correcting a typo in a controlled vocabulary term for "sequencing_platform". Lead Curator New patch version

Diagram 1: Governance Workflow for Template Changes

GovernanceWorkflow Start Change Proposal Submitted Triage Change Impact Triage Start->Triage Major Major Change? Triage->Major Steering Steering Committee Review & Vote Major->Steering Yes Custodian Custodian Review Major->Custodian No Implement Implement & Version Steering->Implement Approved Custodian->Implement Log Log in Change Registry Implement->Log Notify Notify User Community Log->Notify

Version Control Protocol

This protocol details the technical implementation of versioning using a Git-based system, adapted for metadata templates.

Experimental Protocol 1: Implementing Semantic Versioning for Templates

Objective: To systematically track, document, and publish changes to a FAIR viral genomics metadata template.

Materials:

  • Git repository (e.g., GitHub, GitLab).
  • Template schema file (JSON, YAML, or CSV).
  • Validation schema (e.g., JSON Schema).
  • Change log file (e.g., CHANGELOG.md).

Procedure:

  • Repository Structure: Maintain a master branch representing the current stable release. All changes occur in feature branches (git checkout -b feat/add-new-field).
  • Schema Modification: Edit the template schema file following governance approval.
  • Validation Update: Update the validation schema to reflect changes, ensuring backward compatibility where required.
  • Version Tagging: Commit changes and tag the release using semantic versioning (git tag -a v1.2.0 -m "Adds fields for cell culture passage method").
  • Change Log Entry: Document the change in the CHANGELOG.md, categorizing entries as "Added," "Changed," "Deprecated," "Removed," "Fixed," or "Security."
  • Release Publication: Push the commit and tag to the remote repository. Use the repository's release feature to create a formal release with attached schema files.
  • Integration Test: Run a predefined test suite to ensure existing metadata instances can be validated against the new version (for minor/patch) or that migration scripts work (for major).

Table 2: Version Control State Assessment (Hypothetical Data from 12-Month Period)

Metric Value Benchmark for Success
Number of Major Releases 2 ≤2 per year
Average Time from Proposal to Release (Minor) 14 days <21 days
Percentage of Releases with Complete CHANGELOG 100% 100%
Instances of Broken Backward Compatibility (Unplanned) 0 0

Consistency Validation Workflow

A critical experiment to ensure changes do not disrupt downstream data integration and analysis pipelines.

Diagram 2: Template Version Consistency Validation Workflow

ValidationWorkflow NewVersion New Template Version (vN) Validator Schema Validator NewVersion->Validator LegacyData Legacy Metadata Instances (vN-1) CompatCheck Backward Compatibility Check LegacyData->CompatCheck Validator->CompatCheck Migration Automated Migration Script (if Major) CompatCheck->Migration Fail PipelineTest Downstream Pipeline Execution Test CompatCheck->PipelineTest Pass Migration->PipelineTest Report Consistency Report PipelineTest->Report

Experimental Protocol 2: Backward Compatibility Testing

Objective: To empirically verify that a new minor or patch template version does not invalidate metadata created with the previous version.

Materials:

  • A test suite of 100+ validated metadata instances from the previous version (vN-1).
  • Validation schema for the new version (vN).
  • Scripting environment (Python/R).
  • Reporting tool (e.g., Jupyter Notebook, RMarkdown).

Procedure:

  • Sample Selection: Randomly select a representative sample of metadata instances from the legacy corpus, ensuring coverage of all optional fields.
  • Automated Validation: Run each legacy instance against the vN validation schema using a command-line tool (e.g., ajv validate -s schema_vN.json -d instance.json).
  • Result Aggregation: Record validation results (pass/fail) and the specific cause of any failure (e.g., "field 'x' is required in vN but was optional in vN-1").
  • Analysis: Calculate the pass rate. A pass rate of 100% confirms backward compatibility. Any failure triggers a review: if the change was intended to be minor, the schema must be corrected.
  • Report Generation: Document the test parameters, results, and final compatibility status.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Template Governance and Versioning

Item Function in Governance/Versioning Example Solution/Link
Git Repository Host Provides the core platform for version control, collaboration, and issue tracking. GitHub, GitLab
Schema Validator Automates the validation of metadata instances against template schemas. AJV (JSON), Cerberus (Python), R package jsonvalidate
Continuous Integration (CI) Service Automates testing (e.g., compatibility checks) on every proposed change. GitHub Actions, GitLab CI
Change Log Generator Automates the creation of standardized change logs from commit history. standard-version (npm), clog (Rust)
Metadata Registry Platform Publishes, documents, and provides persistent access to template versions. RDMkit, FAIRsharing.org, Custom portal built with CKAN
Semantic Diff Tool Visually highlights meaningful differences between template versions, beyond text. json-diff, ydiff

Measuring Success: How to Validate, Compare, and Justify Your Metadata Strategy

The establishment of FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates for viral genomics is a critical step in accelerating research for pathogen surveillance, vaccine design, and therapeutic development. This thesis posits that structured templates alone are insufficient without robust validation frameworks to assess and ensure their FAIRness in practice. This document provides Application Notes and Protocols for implementing such validation, focusing on automated checking tools and repository feedback mechanisms.

Core Validation Tools & Quantitative Metrics

FAIR-Checker Tool Analysis

FAIR-Checker tools automate the assessment of digital resources against the FAIR principles. The following table summarizes key metrics and outputs from prominent, currently available tools relevant to metadata validation.

Table 1: Comparison of FAIR Assessment Tools for Metadata Validation

Tool Name Primary Focus Key Metrics Generated Output Format Integration Potential with Repositories
FAIR-Checker General resource FAIRness FAIR Principle score (0-1 per letter), Implementation score JSON, HTML Report High (API available)
F-UJI Automated FAIR assessment Maturity Indicators scores, Total score JSON-LD, Human-readable report Very High (REST API)
FAIR Enough? Quick self-assessment Binary (Y/N) checklist for 14 questions Web interface, Summary score Low (Manual)
FAIRshake Rubric-based assessment Project/Digital Object score based on custom rubric Web dashboard, API High (Toolkit for embed)
FAIR Evaluator Community-defined tests Test-by-test results (PASS/FAIL/INFO) Machine-readable (JSON) High (Service-oriented)

Repository-Generated Feedback Metrics

Public data repositories provide implicit validation through user engagement and system metrics. These quantitative signals serve as pragmatic indicators of metadata utility.

Table 2: Repository Feedback Metrics for Viral Genomics Metadata Quality

Metric Category Specific Metric Indicator of Typical Benchmark (Good)
Findability Unique dataset views Initial discovery success > Avg. for repository
Accessibility Successful download requests Technical accessibility >95% success rate
Interoperability Citations in external papers Reuse and integration >0 citations/year
API calls for metadata Machine-actionability High & growing volume
Reusability Dataset citations Scholarly reuse > Field median
Derived dataset links Provenance and reuse Presence of links

Experimental Protocols

Protocol: Automated FAIR Assessment of a Viral Genomics Metadata Record

Objective: To programmatically evaluate the FAIR compliance of a deposited viral genome sequence and its associated metadata using the F-UJI API. Materials: See Scientist's Toolkit (Section 5). Procedure:

  • Preparation: Ensure the dataset to be assessed has a persistent identifier (PID) like a DOI, accession number, or handle.
  • Tool Configuration: Access the F-UJI API endpoint (e.g., https://api.f-uji.net/v1/evaluate). Prepare an API call using a tool like curl or within a Python script.
  • Execution: Submit a GET request to the API with the required parameters: the PID (object_identifier) and your API key (client_secret). Example curl command:

  • Data Capture: Capture the JSON response. The core metrics reside in the results section, containing scores for each FAIR principle and associated maturity indicators.
  • Analysis: Parse the JSON to extract scores. Calculate an aggregate score or analyze principle-specific weaknesses (e.g., low "Interoperable" score due to missing controlled vocabulary).
  • Iteration: Use the results to refine the metadata template—for instance, by adding required fields that were missing—and re-submit the record.

Protocol: Gathering and Analyzing Repository Feedback Metrics

Objective: To collect quantitative feedback on the usage of viral genomics datasets deposited using a specific FAIR metadata template. Materials: See Scientist's Toolkit (Section 5). Procedure:

  • Repository Selection: Identify target repositories (e.g., INSDC partners, Zenodo, specific institutional repos) where the template is deployed.
  • Metric Identification: Collaborate with repository stewards to identify available metrics (see Table 2). Gain access to dashboard or API for metrics retrieval.
  • Baseline Establishment: For a pilot set of datasets using the new template, record initial metrics upon deposition (views = 0, citations = 0).
  • Longitudinal Tracking: At regular intervals (e.g., quarterly), collect time-series data for: dataset views, downloads, citation counts (via repository and external sources like Google Scholar), and links to derived data.
  • Control Comparison: Compare the trended metrics against a control group of similar viral genomics datasets that used unstructured or ad-hoc metadata.
  • Correlation Analysis: Statistically analyze (e.g., using regression) the relationship between metadata completeness (from Protocol 3.1) and reuse metrics (citations, downloads). Determine if higher FAIR scores predict increased reuse.

Visualizations

Workflow Diagram

fair_validation_workflow A Define FAIR Metadata Template B Deposit Dataset & Metadata in Repository A->B C Automated FAIR Check (FAIR-Checker/F-UJI) B->C E Collect Repository Feedback Metrics B->E D Generate FAIR Score & Gap Report C->D F Analyze Correlation: Score vs. Reuse D->F E->F G Refine & Improve Metadata Template F->G G->A

Title: FAIR Metadata Validation and Refinement Workflow

Signaling Pathway for FAIR Data Reuse

fair_signaling_pathway Meta Rich FAIR Metadata Disc Discovery Meta->Disc Findable Acc Access Meta->Acc Accessible Int Integration Meta->Int Interoperable PID Persistent Identifier (DOI, Accession) PID->Disc API Standardized API API->Acc Tool Analysis Tool/ Platform Tool->Int Reuse Reuse: New Science Int->Reuse Reusable

Title: FAIR Principles as a Signaling Pathway to Data Reuse

The Scientist's Toolkit

Table 3: Key Research Reagent Solutions for FAIR Validation

Item Function/Description Example/Provider
F-UJI API Programmable service for automated FAIR assessment against community-agreed metrics. f-uji.net
FAIR-Checker API Alternative API for assessing FAIRness of a resource via its URL. fair-checker.france-bioinformatique.fr
Data Repository with Metrics API A repository that provides programmatic access to usage statistics and metadata. Zenodo API, ENA/NCBI Stats
Citation Tracking Script Custom script (Python/R) to gather citations from multiple scholarly sources. Using scholarly, dimensions.ai API
PID Resolver Service to resolve Persistent Identifiers to the actual resource location. doi.org, identifiers.org
Metadata Schema Validator Tool to validate metadata against a specific structured schema (JSON Schema, XSD). JSONSchema validator, OAI-PMH validator
Controlled Vocabulary Service API to validate and map terms to standard ontologies (e.g., for virus taxonomy). OLS (Ontology Lookup Service) API

Application Notes

Within the thesis framework for developing FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates for viral genomics, evaluating the interoperability of major data deposition schemas is critical. These repositories, while essential, exhibit significant structural and semantic heterogeneity, impeding seamless data integration and meta-analysis. The following notes compare the key interoperability features, challenges, and mapping potentials of three major archives: GISAID, the European Nucleotide Archive (ENA), and GenBank (via NCBI).

Interoperability Metrics and Comparative Analysis

The primary barriers to interoperability include differing mandatory field requirements, controlled vocabularies, data validation rules, and access models. The table below summarizes quantitative and qualitative metrics critical for FAIR template design.

Table 1: Core Interoperability Metrics for Viral Genome Deposition Schemas

Feature GISAID ENA (ERC000033) GenBank (NCBI Virus)
Primary Access Model Controlled-access, requires login & agreements Public domain (CC0) for data; submission login required Public domain; submission login required
Mandatory Fields (Virus) ~25 core fields (e.g., isolate, host, collection date) ~15 core checklist fields (ERC000033) + sample descriptor ~12 core fields (e.g., organism, isolate, country)
Geo. Location Specificity Required: "Location" (Region/Country/State); "Additional location info" Structured: country/region + optional lat/long Structured: country + optional region/subregion
Host Field Vocabulary Semi-controlled (free text with suggestions) Controlled via ENA Ontology (ENSG0000...) & host taxonomy ID Controlled via NCBI Taxonomy ID
Collection Date Format YYYY-MM-DD (partial dates allowed) YYYY-MM-DD (ISO 8601) YYYY-MM-DD (MM and DD optional)
Sequence Data Format FASTA (with specific header format) FASTA, submitted read files (FASTQ) FASTA (GenBank flatfile)
Metadata Submission Format Web form, Excel template, or API (CLI) Web form (Webin), Excel template, or Webin-CLI Web form (BankIt), TAB-delimited template, or tbl2asn
Unique ID Prefix EpiCoV identifiers (EPIISLxxxxxx) ENA sample (SAMEA…), run (ERR…), study (PRJEB…) GenBank accession (OKxxxxxx), SRA (SRRxxxxxx)
License for Reuse GISAID Database Access Agreement (restricts redistribution) CC0 1.0 Universal for data Public domain, no constraints on data
API for Metadata Fetch Yes (RESTful, token-based) Yes (EUROPE PMC API, ENA API) Yes (E-utilities, Datasets API)

Key Interoperability Challenges

  • Semantic Discordance: Identical concepts have different field names and value constraints (e.g., "Host" vs. "host scientific name" vs. "organism").
  • Access and Licensing: GISAID's access agreement creates a legal and technical barrier to automated merging with public domain data from ENA/GenBank.
  • Identifier Silos: No persistent, cross-repository ID links a single biological sample across all three archives, complicating provenance tracking.
  • Vocabulary Gaps: Lack of a universal, granular ontology for fields like "Passage details" or "Collection method" hinders precise mapping.

Experimental Protocols

Protocol 1: Schema Crosswalk and Field Mapping for FAIR Template Development

Objective: To create a harmonized FAIR metadata template by mapping equivalent fields across GISAID, ENA, and GenBank schemas and identifying irreconcilable differences.

Materials:

  • Research Reagent Solutions:
    • Schema Documentation: Current GISAID EpiCoV fields list, INSDC (ENA/GenBank/DDBJ) feature table definition, ENA Metagenomics checklist (ERC000033).
    • Vocabulary Resources: NCBI Taxonomy, EDAM Ontology, Environment Ontology (ENVO), Disease Ontology (DO).
    • Toolkit: Python/R environment, spreadsheet software, ontology mapping tool (e.g., OLS API).
    • Output Target: A structured crosswalk table (e.g., in CSV/JSON) and a prototype FAIR template in JSON Schema format.

Methodology:

  • Field Inventory: Manually extract all mandatory and recommended metadata fields from each schema's official submission guidelines. Record field name, description, data type, cardinality, and value constraints.
  • Conceptual Clustering: Group fields from all schemas by high-level conceptual categories (e.g., Sample Provenance, Host, Sequencing, Administrative).
  • Semantic Mapping: For each cluster, identify fields representing the same core concept. Use official definitions and common usage to justify mappings. Note where meanings partially overlap or differ.
  • Vocabulary Alignment: For fields with controlled values, map terms to a common reference ontology (e.g., map all host names to NCBI Taxonomy IDs). Flag terms without a clear correspondence.
  • Gap & Conflict Analysis: Document concepts required by one schema but absent in others (e.g., GISAID's "Lineage" is publisher-calculated, not a submitter field). Document conflicting value formats.
  • Template Synthesis: Design a unified JSON Schema template that encompasses the union of concepts. Use the mapped ontologies for value constraints. Implement as a "broader" template with profiles for each target repository.

Protocol 2: Automated Metadata Retrieval and Concordance Testing

Objective: To assess the practical interoperability and data consistency for a set of viral sequences deposited in multiple repositories.

Materials:

  • Research Reagent Solutions:
    • Test Dataset: A list of known sequence accession pairs/triplets for the same biological sample in different databases (e.g., an EPIISLID, an ENA sample ID, and a GenBank accession).
    • APIs: GISAID EpiCoV API (with authorized token), ENA REST API, NCBI E-utilities/Datasets API.
    • Computational Environment: Python with requests, pandas, biopython libraries. Jupyter Notebook for analysis.
    • Validation Scripts: Custom scripts to parse and compare JSON/XML API responses.

Methodology:

  • ID List Curation: Compile a test set of 50-100 known cross-referenced viral sequence records (e.g., from publications acknowledging multiple accessions).
  • Automated Fetching: Write scripts to programmatically retrieve full metadata records for each ID from each repository's API. Handle authentication (for GISAID) and API rate limits.
  • Data Normalization: Parse API responses into a common, simplified internal data structure using the mappings from Protocol 1. Convert dates to ISO format, host names to taxonomy IDs where possible, and location text to structured fields.
  • Pairwise Field Comparison: For each record pair (e.g., GISAID-ENA, ENA-GenBank), compare the normalized values for key fields: Collection date, Host, Country, Isolate name. Calculate concordance rates (% exact matches).
  • Discrepancy Analysis: Manually inspect records with mismatches (e.g., "USA" vs. "United States", "Human" vs. "Homo sapiens", date format errors) to classify error types (semantic, syntactic, clerical).
  • Provenance Graph Construction: Generate a diagram linking all accessions for a given sample, visually highlighting fields with concordant and discordant values.

Visualizations

G Start Start: Viral Sequence Data Submitter Researcher (Submitter) Start->Submitter SchemaG GISAID Schema & API Submitter->SchemaG SchemaE ENA Schema & API Submitter->SchemaE SchemaN GenBank Schema & API Submitter->SchemaN DataG GISAID Metadata (Structured) SchemaG->DataG DataE ENA Metadata (Structured) SchemaE->DataE DataN GenBank Metadata (Structured) SchemaN->DataN Harvest Automated Metadata Harvesting Normalize Normalization & Crosswalk Mapping Harvest->Normalize Compare Concordance Analysis Normalize->Compare Template Synthesized FAIR Template Compare->Template informs Results Interoperability Report & Gaps Compare->Results DataG->Harvest DataE->Harvest DataN->Harvest MapTable Crosswalk Mapping Table MapTable->Normalize uses

Title: Workflow for Schema Interoperability Analysis and FAIR Template Synthesis

G GHead GISAID (EpiCoV) G Virus name isolate_id Collection date collection_date Location location Host host Additional info additional_info GHead->G EHead ENA (ERC000033) E scientific_name tax_id collection date collection_date geographic location country host scientific name host_tax_id isolate isolate EHead->E NHead GenBank (NCBI) N organism db_xref='taxon:...' collection_date (VERSION) country : host isolate isolate NHead->N F FAIR Template (JSON Schema) sample_id uri collection_event date iso8601 location geo_ontology host ncbi_taxonomy_id pathogen ncbi_taxonomy_id sequencing_assay edam_ontology G:f0->F:f1 Map G:f1->F:f0 G:f2->F:f1 G:f3->F:f3 E:f0->F:f4 Map E:f1->F:f0 E:f2->F:f1 E:f3->F:f3 N:f0->F:f4 Map N:f1->F:f0 N:f2->F:f1 N:f3->F:f3

Title: Semantic Field Mapping to a Unified FAIR Template

This application note details a meta-analysis of heterogeneous HIV-1 genomic datasets, made possible by implementing FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates. By standardizing metadata from five distinct cohorts, researchers identified novel correlates of broadly neutralizing antibody (bNAb) development, accelerating immunogen design for vaccine development.

Within the broader thesis on FAIR metadata templates for viral genomics, this case demonstrates their critical role in cross-study data harmonization. Combining HIV datasets historically failed due to incompatible metadata schemas, limiting statistical power for discovering rare immune correlates.

Applied FAIR Metadata Template

The Viral Genomics FAIR Template (VGF-T v2.1) was applied to legacy datasets. Key mandatory fields included:

Table 1: Core VGF-T Metadata Fields for HIV Genomic Studies

Field Name Description Allowed Values Example
specimen_id Unique specimen identifier string P001_BL
host_sex Biological sex of host Male, Female, Unknown Female
days_post_infection Days since estimated infection date integer 450
art_status Antiretroviral therapy status Naive, Suppressed, Treated Naive
hla_alleles Host HLA genotypes string (WHO nomenclature) A*02:01
sequencing_platform Platform used IlluminaMiSeq, OxfordNanopore Illumina_MiSeq
genomic_coverage Average read depth float 2500.5
bNAb_titer Neutralization breadth score float (ID50) 112.3

Data Harmonization & Cohort Integration

Five cohorts (total n=1,240 participants) were harmonized.

Table 2: Integrated Cohort Characteristics

Cohort ID Original Purpose N (Participants) Avg. Follow-up (Years) Key Original Metadata Format
IAVI C100 bNAb Discovery 320 5.2 Custom CSV
RV217 Early Infection 180 3.8 REDCap Database
AMP (HVTN 703/704) Antibody Mediated Prevention 460 4.5 LabKey SQL
CAPRISA 002 Acute Infection 160 7.1 Proprietary Access
HIVACAT Viral Evolution 120 6.3 Excel Files

Protocol 3.1: Metadata Harmonization Workflow

  • Inventory: Map all source metadata fields to the VGF-T ontology.
  • Transform: Use custom Python scripts (Pandas library) to convert values to standard units and controlled vocabulary.
  • Validate: Run the fair_validate tool (v1.2) to check for missing mandatory fields and value integrity.
  • Ingest: Load harmonized metadata and raw sequence files (FASTQ) into a centralized graph database (Neo4j) using the vgf_ingest plugin, establishing links between samples, patients, and experiments.

Meta-Analysis Protocol

Protocol 4.1: Identification of bNAb Correlates Objective: Identify genomic and host factors associated with high neutralization breadth (bNAb titer >200 ID50).

  • Query Integrated Database:

  • Statistical Analysis: Perform multivariate logistic regression using R (glm package). Model: high_bNAb ~ host_sex + hla_alleles + log10(days_post_infection) + art_status + cohort_origin.

  • Phylogenetic Analysis: For identified high-bNAb patients, extract envelope (env) gene sequences. Perform multiple sequence alignment (MA) using MAFFT (v7.505). Construct maximum-likelihood phylogenetic trees with IQ-TREE (v2.2.0) under the GTR+F+I+G4 model.
  • Selection Pressure Analysis: Apply the HyPhy (v2.5) software suite to aligned env sequences to detect sites under positive selection (FUBAR method, posterior probability >0.9).

Key Results & Data

Meta-analysis of the FAIR-enabled integrated dataset revealed significant associations.

Table 3: Significant Correlates of High bNAb Titer (ID50 >200)

Factor Odds Ratio 95% Confidence Interval p-value Adjusted p-value (FDR)
HLA-B*57:01 allele 3.45 [2.10, 5.65] 4.2e-06 0.0001
Infection duration (per log10 day) 2.10 [1.68, 2.62] 1.1e-09 3.0e-08
ART-Naïve status (vs. Treated) 1.82 [1.30, 2.55] 0.0005 0.003
Female sex 1.25 [0.95, 1.64] 0.11 0.18

Table 4: Positively Selected Sites in Env for High bNAb Group

Amino Acid Site (HXB2) Glycan Shield Proximity Posterior Probability (FUBAR) Known mAb Target
332 (N-linked glycan) Yes 0.98 Yes (PGT121)
611 (V5 loop) No 0.93 No
88 (C1 region) No 0.91 No

bnab_insight_pathway FAIR-Driven Insight Discovery Pathway FAIR_Meta FAIR Metadata & Sequences Query Graph Query: High bNAb Cohort FAIR_Meta->Query Stats Multivariate Regression Query->Stats Phylo Phylogenetic & Selection Analysis Query->Phylo HLAB HLA-B*57:01 Association Stats->HLAB Vaccine Rational Immunogen Design Hypothesis HLAB->Vaccine Site332 Env Site 332 Positive Selection Phylo->Site332 Site332->Vaccine

The Scientist's Toolkit: Research Reagent Solutions

Table 5: Essential Reagents & Tools for HIV Genomic Meta-Analysis

Item Function Example Product/Catalog
Viral RNA Extraction Kit Isolate high-quality HIV RNA from plasma/serum for sequencing. Qiagen QIAamp Viral RNA Mini Kit (52906)
RT-PCR & Enrichment Primers Amplify full-length HIV env or other genomic regions for NGS. In-house designed primers targeting group M conserved regions.
Illumina cDNA Library Prep Kit Prepare sequencing libraries from amplicons. Illumina Nextera XT DNA Library Prep Kit (FC-131-1096)
FAIR Metadata Validation Software Programmatically validate local metadata against the VGF-T template. fair_validate (open-source Python package).
Graph Database System Store and query interconnected metadata and data relationships. Neo4j Community Edition (graph database).
HyPhy Software Suite Perform evolutionary genetic analyses, detect selection pressure. HyPhy 2.5 (open-source platform).
bNAb Neutralization Assay Kit Quantify serum neutralization breadth and potency (Tier 2 pseudoviruses). Custom panel of global HIV-1 pseudoviruses (NIH ARP).

Application Notes on FAIR Metadata in Viral Genomics

Thesis Context: Implementing standardized FAIR (Findable, Accessible, Interoperable, Reusable) metadata templates is critical for advancing viral genomics research. This application note quantifies the return on investment (ROI) of robust metadata practices, focusing on data reuse acceleration, collaborative efficiency, and grant compliance success.

Quantitative Impact of Standardized Metadata: Table 1: Measured Outcomes of Implementing FAIR Metadata Templates in Virology Consortia

Metric Category Before FAIR Templates After FAIR Templates % Improvement / Quantitative Impact Study Period & Source
Data Reuse & Efficiency
Time to Prepare Data for Public Archive 14-21 days 2-3 days ~85% reduction 6-month internal audit
Time to Re-analyze External Dataset 5-7 days 1-2 days ~75% reduction User survey, n=45
Dataset Downloads from Repository 120/month (avg.) 310/month (avg.) +158% 12-month repository stats
Citation of Datasets 15/year 42/year +180% 24-month tracking
Collaboration
Onboarding Time for New Collaborators 3-4 weeks 1 week ~70% reduction Project manager reports
Inter-Lab Data Merge Success Rate 60% 98% +38 percentage points Multi-institution trial
Grant Compliance
NIH Genomic Data Sharing (GDS) Policy Compliance Rate 65% 100% Full compliance Institutional review
Time spent on grant compliance reporting 40 hours/quarter 10 hours/quarter 75% reduction PI time tracking

Key Protocol 1: Implementing a FAIR Metadata Template for Viral Genome Submission

Objective: To ensure viral sequence data submitted to public repositories (e.g., INSDC, GISAID) is accompanied by metadata that is complete, standardized, and compliant with FAIR principles, enabling immediate reuse.

Materials:

  • Viral isolate or clinical sample.
  • Sequencing data (e.g., FASTQ files).
  • FAIR Viral Genomics Metadata Template (e.g., adapted from the IRIDA/NCBI SARS-CoV-2 Metadata Template).
  • Metadata validation tool (e.g., CLCbio Metadata QA tool, Galaxy European).

Protocol Steps:

  • Template Selection: Download the latest version of the consortium-agreed metadata template (e.g., in TSV or Excel format).
  • Sample Information: Populate mandatory fields: sample_id, collecting_institution, collection_date, geographic_location (country, region, latitude/longitude).
  • Host & Clinical Data: Enter: host (e.g., Homo sapiens), host_health_status, specimen_source (e.g., nasopharyngeal swab).
  • Sequencing & Processing: Detail: sequencing_instrument, library_prep_kit, assembly_method, assembly_name.
  • Data Validation: Run the completed template through a metadata validation tool to check for formatting errors, controlled vocabulary compliance, and missing mandatory fields.
  • Submission: Submit validated metadata file alongside raw reads and/or assembled genome to the chosen repository using their designated upload portal (e.g., SRA Submission Portal, GISAID's Web interface).

Key Protocol 2: Retrospective Metadata Curation and ROI Assessment

Objective: To quantify the impact of metadata enhancement by measuring the reuse rate of previously "dark" or poorly described datasets after curation.

Materials:

  • Legacy genomic datasets with minimal metadata.
  • Curation team (data scientist, domain expert).
  • FAIR template.
  • Data repository analytics dashboard.

Protocol Steps:

  • Baseline Establishment: Record the current download and citation counts for the target legacy datasets from the repository analytics.
  • Gap Analysis: Map existing sparse metadata to the FAIR template. Identify missing critical fields (e.g., collection_date, host_age).
  • Curation: Research and populate missing metadata fields using original lab notebooks, published methods, or by contacting the original contributors.
  • Repository Update: Submit the enhanced metadata to the repository to update the dataset records.
  • Monitoring & Measurement: Over a defined period (e.g., 12 months), track and compare the download frequency, citation in new publications, and requests for collaboration against the pre-curation baseline. Calculate time/cost of curation versus the value generated.

Visualizations

workflow LegacyData Legacy Dataset (Poor Metadata) Curation FAIR Template Curation Process LegacyData->Curation FAIRDataset FAIR-Compliant Dataset Curation->FAIRDataset Outcomes Measurable Outcomes FAIRDataset->Outcomes Reuse Increased Data Reuse Outcomes->Reuse Collaboration Enhanced Collaboration Outcomes->Collaboration Compliance Grant Compliance Outcomes->Compliance

Diagram Title: ROI of Metadata Curation Workflow

compliance Grant Grant Award (NIH GDS Policy) FAIRTemplate FAIR Metadata Template Use Grant->FAIRTemplate Mandates Submission Automated SRA Submission FAIRTemplate->Submission Enables ComplianceCheck Automated Compliance Check Submission->ComplianceCheck Passes Reporting Minimal Effort Reporting ComplianceCheck->Reporting Facilitates

Diagram Title: Grant Compliance Pathway with FAIR Metadata

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Tools for FAIR Metadata Implementation in Viral Genomics

Item / Solution Function in Metadata Context Example Vendor/Project
Standardized Metadata Template Provides the structured schema (fields, vocabulary) for consistent data annotation. NCBI Virus / IRIDA SARS-CoV-2 template; GA4GH metadata standards.
Metadata Validation Tool Automatically checks template completion, format, and controlled vocabulary compliance. Galaxy European metadata checker; CLCbio Metadata QA.
Digital Lab Notebook (ELN) Captures experimental metadata at the source, enabling automated export to templates. Benchling, LabArchives, RSpace.
Data Repository with Validation A submission portal that validates metadata upon ingest, ensuring quality before public release. NCBI SRA, EBI ENA, GISAID.
Unique Persistent Identifier (PID) Service Assigns globally unique, citable identifiers to datasets, linking data to its metadata. DOI (e.g., via Zenodo, Figshare), IGSN.
Ontology Management Tool Provides access to standardized biological ontologies (e.g., NCBI Taxonomy, Disease Ontology) for metadata fields. OBO Foundry, EBI Ontology Lookup Service.

Conclusion

Implementing FAIR-compliant metadata templates is not an administrative chore but a foundational scientific practice that multiplies the value of viral genomic data. As outlined, starting with a clear understanding of FAIR principles enables the selection of fit-for-purpose templates, which, when applied methodologically and optimized for scale, create robust, reusable datasets. The validation and comparative analysis of these practices demonstrate tangible returns in the form of accelerated discovery, enhanced surveillance, and more efficient therapeutic development. The future of viral genomics hinges on data federation and AI-driven analysis, both of which are impossible without standardized, high-quality metadata. Researchers and institutions must therefore prioritize metadata stewardship as a core competency, advocating for and adopting community standards to build a truly interconnected and actionable viral data commons for global health security.