FAIR Data for Pandemic Preparedness: Implementing FAIR Protocols for Pathogen Genomic Sequencing

Aria West Jan 12, 2026 79

This article provides a comprehensive guide for researchers and drug development professionals on implementing FAIR (Findable, Accessible, Interoperable, and Reusable) data principles for outbreak pathogen sequencing.

FAIR Data for Pandemic Preparedness: Implementing FAIR Protocols for Pathogen Genomic Sequencing

Abstract

This article provides a comprehensive guide for researchers and drug development professionals on implementing FAIR (Findable, Accessible, Interoperable, and Reusable) data principles for outbreak pathogen sequencing. Covering foundational concepts, practical methodologies, common troubleshooting, and validation frameworks, it addresses the critical need for standardized, high-quality genomic data to accelerate outbreak response, therapeutic discovery, and global health surveillance.

Why FAIR Data is Non-Negotiable for Modern Outbreak Response

Defining FAIR Principles in the Context of Pathogen Genomics

Within the broader thesis on FAIR data protocols for outbreak sequencing research, the application of the FAIR Guiding Principles—Findable, Accessible, Interoperable, and Reusable—to pathogen genomic data is critical for accelerating pandemic preparedness and response. This document provides detailed Application Notes and Protocols for implementing these principles, ensuring genomic data from outbreaks can be rapidly integrated and analyzed across institutions and disciplines.

Application Notes: Quantitative Metrics for FAIRness in Pathogen Genomics

Effective implementation requires measurable indicators. The following table summarizes current targets and metrics for assessing FAIR compliance in pathogen genomics data repositories.

Table 1: Key Metrics for FAIR Pathogen Genomic Data

FAIR Principle Key Metric Target / Example Quantitative Benchmark
Findable Persistent Identifier (PID) Coverage Percentage of genomic datasets assigned a DOI or accession (e.g., ENA/NCBI SRA, GISAID EPI_SET ID) >95% of submitted datasets
Richness of Metadata in a Searchable Registry Number of structured fields (e.g., specimen, host, location, date) compliant with minimum information standards (e.g., MIxS) ≥20 core fields per sample
Accessible Data Retrieval Success Rate Percentage of successful automated retrieval attempts via standard protocols (e.g., FTP, API) over 30 days >99% uptime and retrieval success
Clear Access Protocol Documentation Existence of publicly documented, machine-readable data access statements (including any restrictions) 100% of datasets
Interoperable Use of Controlled Vocabularies and Ontologies Percentage of metadata fields linked to community standards (e.g., NCBI Taxonomy, Disease Ontology, ENVO for location) >80% of applicable fields
Standard File Format Adoption Percentage of data files in recommended formats (e.g., FASTQ, CRAM, VCF according to GA4GH specifications) >90% of data files
Reusable Provision of Comprehensive Data Provenance Percentage of datasets with a detailed, machine-actionable data lifecycle history (e.g., CWL, WDL workflows) Increase from 50% to >80%
Licensing and Reuse Citation Clarity Percentage of datasets with explicit usage licenses (e.g., CC0, CC-BY, GISAID terms) 100% of datasets

Experimental Protocols

Protocol 1: Generating FAIR-Compliant Genome Sequences from an Outbreak Isolate

Objective: To process a pathogen isolate from sample to submission in a FAIR-aligned public repository.

Materials & Reagents:

  • Clinical specimen with confirmed pathogen (e.g., Nasopharyngeal swab in viral transport media).
  • Nucleic acid extraction kit (e.g., QIAamp Viral RNA Mini Kit).
  • Library preparation kit for sequencing (e.g., Illumina COVIDSeq Test or Nextera XT DNA Library Prep Kit).
  • Sequencing platform (e.g., Illumina MiSeq, NovaSeq; or Oxford Nanopore MinION).
  • Bioinformatics computing cluster or cloud instance.
  • Metadata spreadsheet template (aligned with MIxS or repository-specific requirements).

Procedure:

  • Sample Acquisition & Metadata Recording:
    • At the point of collection, record all minimum information (see Table 1) into a structured template. Assign a unique, persistent local sample ID.
  • Genomic Sequencing:
    • Extract nucleic acids following manufacturer’s protocol. Quantify yield.
    • Prepare sequencing library using the selected kit, incorporating the sample ID into the library name.
    • Sequence the library to a minimum coverage of 100x for the target genome.
  • Bioinformatics Processing (Standardized Workflow):
    • Use a containerized or workflow-managed pipeline (e.g., Nextflow nf-core/viralrecon, Snakemake) to ensure reproducibility.
    • Quality Control: Trim adapters and low-quality bases using Trimmomatic or fastp.
    • Genome Assembly: Map reads to a reference genome using BWA-MEM or perform de novo assembly using SPAdes.
    • Variant Calling: Identify SNPs and indels using iVar or bcftools.
    • Output: Generate a consensus genome sequence (FASTA), aligned reads (BAM), and variant call file (VCF).
  • FAIR Metadata Curation and Submission:
    • Populate the metadata spreadsheet with wet-lab and computational parameters (sequencing instrument, software versions, reference genome accession).
    • Validate metadata against the chosen repository's schema using provided validation tools (e.g., ena-webin-cli for ENA).
    • Submit the final dataset (raw reads, consensus genome, metadata) to an international repository (e.g., ENA, NCBI SRA, GISAID). Register the provided public accession number (PID) with the local sample ID.

Protocol 2: Establishing a Interoperable Metadata Pipeline Using Ontologies

Objective: To automate the annotation of sample metadata with controlled vocabulary terms for enhanced interoperability.

Materials & Reagents:

  • A database of sample metadata (e.g., CSV file, LIMS system export).
  • Access to ontology lookup services (e.g., OLS (Ontology Lookup Service) API, EBI Ontology API).
  • Scripting environment (Python 3 with requests, pandas libraries).

Procedure:

  • Identify Target Fields: Select metadata columns requiring standardization (e.g., "hostspecies", "anatomicalsite", "country").
  • Map to Ontology Terms:
    • For each unique value in a column (e.g., "human", "Homo sapiens"), programmatically query the OLS API to find the closest matching term URI from a specified ontology (e.g., NCBI Taxonomy ID: 9606).
    • Store the original value, the preferred label, and the ontology term URI (e.g., http://purl.bioontology.org/ontology/NCBITAXON/9606) in a new mapping table.
  • Create Annotated Metadata File:
    • Generate a new metadata file where the original columns are supplemented with new columns (e.g., host_species_ontology_uri).
    • This file should be submitted alongside the data, or integrated into a LIMS, enabling machine-readable semantic interoperability.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools for FAIR Pathogen Genomics Research

Item / Solution Function in FAIR Context
Sample-to-CLIMB Pipeline Automated, UK-standardized pipeline for processing raw sequence data to consensus genomes with linked metadata.
nf-core/viralrecon (Nextflow) Community-curated, containerized bioinformatics workflow for viral genome analysis; ensures computational reproducibility.
INSDC Submission Portals Webin (ENA), Submission Portal (NCBI), DDBJ: Standardized portals for submitting data with rich metadata to global archives.
GISAID EpiCoV Platform Specialized repository for sharing influenza and coronavirus genomes with associated epidemiological data.
CWL (Common Workflow Language) Standard for describing command-line analysis workflows in a way that makes them portable and reproducible across platforms.
DataHub / LIMS (e.g., Galaxy, Mytardis) Laboratory Information Management Systems that structure sample metadata from point of origin, promoting FAIR data capture.
Ontology Lookup Service (OLS) Provides API access to hundreds of biomedical ontologies for consistent metadata annotation (critical for Interoperability).

Visualizations

FAIR_Outbreak_Sequencing cluster_0 Outbreak Response Cycle Sample Sample Seq Seq Sample->Seq Wet-Lab Sequencing Data Data Seq->Data Bioinformatics Processing FAIR_Repo FAIR-Compliant Repository Data->FAIR_Repo Curation & Submission Analysis Integrated Analysis & Modeling FAIR_Repo->Analysis Machine- Accessible Data Action Public Health Action Analysis->Action Evidence Action->Sample Targeted Sampling

Diagram 1: FAIR Data in Outbreak Response Cycle

FAIR_Metadata_Pipeline Raw_Metadata Raw Lab Metadata (CSV) OLS_API Ontology Lookup Service (API) Raw_Metadata->OLS_API Query for Term URIs Mapping_Table Term Mapping Table Raw_Metadata->Mapping_Table Join OLS_API->Mapping_Table Returns URI & Label Annotated_MD Annotated Metadata File Mapping_Table->Annotated_MD Create New Columns Submission Repository Submission Annotated_MD->Submission Validate & Submit

Diagram 2: Ontology-Based Metadata Annotation Pipeline

Application Note: Impact of Data UnFAIRness on Pandemic Response Timelines

The lack of Findable, Accessible, Interoperable, and Reusable (FAIR) data in recent outbreaks has directly impeded rapid research and countermeasure development. This note quantifies these delays.

Table 1: Comparative Timeline Delays Due to UnFAIR Data Practices in Recent Outbreaks

Outbreak (Initial Detection) Key Genomic Data Shared (Days Post-Detection) First Major Genomic Dataset Publicly FAIR (Days Post-Detection) Delay Attributable to UnFAIR Practices (Estimated Days) Consequence of Delay
COVID-19 (Dec 2019) ~14 (GISAID, Jan 2020) ~30 (Full FAIR compliance on NCBI/GISAID) 16-20 Slowed diagnostics & early vaccine design
Mpox (Global, May 2022) ~7 (Initial sequences) ~21 (Structured, annotated datasets) 14 Delayed understanding of unusual transmission
Avian Influenza H5N1 (Cattle, Mar 2024) ~30 (Initial cattle sequences) >60 (Ongoing, incomplete metadata) >30 (and ongoing) Slowed assessment of mammalian adaptation

Protocols for FAIR-Compliant Outbreak Sequencing Research

Protocol 1: FAIR Field Sample to Deposition Pipeline

Objective: To ensure genomic data from outbreak samples is collected, processed, and shared with maximum FAIR compliance from point of origin.

Materials & Reagent Solutions:

  • Sample Collection: Viral transport medium (VTM), RNAlater.
  • Nucleic Acid Extraction: Magnetic bead-based kits (e.g., Qiagen, Thermo Fisher).
  • Sequencing: ARTIC Network primer pools, cDNA synthesis kits, high-throughput sequencer (Illumina/ONT).
  • Analysis: Bioinformatic pipelines (Nextclade, Pangolin), computational resources.
  • Metadata Standardization: INSDC pathogen metadata checklist, DataHarmonizer tool.

Procedure:

  • Sample Collection & Annotation: Collect clinical/environmental sample with minimum metadata (date, location, host, specimen type) using standardized vocabulary.
  • Nucleic Acid Extraction & QC: Extract RNA/DNA. Quantify and check quality via bioanalyzer.
  • Library Preparation & Sequencing: Use standardized primer schemes (e.g., ARTIC). Perform sequencing on available platform.
  • Bioinformatic Analysis: Assemble consensus genome using reference-based assembly. Assign lineage/clade using agreed-upon nomenclature.
  • FAIR Metadata Curation: Compile all experimental and contextual metadata into the INSDC pathogen checklist.
  • Data Deposition: Submit raw reads, consensus sequence, and complete metadata to both a public repository (e.g., SRA, ENA) and a specialized portal (e.g., GISAID, NCBI Virus).
  • Persistent Identifier Assignment: Obtain accession numbers for all data objects. Link sample, sequence, and project accessions.

Workflow Visualization:

FAIR_Workflow start Outbreak Sample Collection meta1 Structured Metadata Annotation start->meta1 lab Lab Processing (Extraction, Sequencing) meta1->lab bioinf Bioinformatic Analysis lab->bioinf meta2 FAIR Metadata Curation & Linking bioinf->meta2 deposit Public Deposition with PIDs meta2->deposit reuse Global Reuse & Analysis deposit->reuse

Diagram Title: FAIR Outbreak Data Generation Workflow

Protocol 2: Federated Analysis for Rapid Outbreak Characterization

Objective: To enable analysis across disparate, FAIR-compliant datasets without centralization, respecting data sovereignty.

Materials: Secure cloud or HPC environments, containerization software (Docker/Singularity), workflow language (Nextflow/CWL), GA4GH Passport & DRS standards.

Procedure:

  • Dataset Discovery: Use search engines (e.g., EBI Search, NCBI Datasets) to find FAIR datasets via metadata queries.
  • Data Access Negotiation: Use GA4GH Passports for authorized access to controlled datasets.
  • Data Retrieval: Use GA4GH DRS URIs to pull specific data files to compute environment.
  • Workflow Execution: Launch containerized, versioned analysis workflow (e.g., phylogenetics, variant calling).
  • Result Aggregation: Combine results from multiple federated analysis runs.
  • Provenance Capture: Record all datasets, software, and parameters using RO-Crate.

Logical Flow Visualization:

Federated_Analysis repo1 Repository 1 (e.g., GISAID) access Standardized Access (DRS, Passports) repo1->access repo2 Repository 2 (e.g., NCBI Virus) repo2->access repo3 Institutional Secure Repository repo3->access query FAIR Data Discovery via Metadata Search query->repo1 query->repo2 query->repo3 compute Compute Node (Containerized Workflow) access->compute results Aggregated Analysis Results compute->results

Diagram Title: Federated Analysis of FAIR Outbreak Data

Table 2: Essential Research Reagent Solutions

Item Function in FAIR Outbreak Research Example/Note
Standardized Primer Panels Ensure interoperable, comparable sequence data across labs. ARTIC Network nCoV-2019 & monkeypox primer sets.
Control Materials Act as positive controls and inter-lab calibration standards. NIBSC WHO International Standards for SARS-CoV-2 RNA.
Metadata Schema Tools Structure sample and experimental metadata for interoperability. DataHarmonizer templates for INSDC pathogen reporting.
Bioinformatic Containers Provide reproducible, versioned software environments. Docker containers for Pangolin, Nextclade, IRMA.
Persistent ID Services Assign unique, resolvable identifiers to data and samples. DOI, BioSample accession, RRID for reagents.
Trusted Repositories Provide accessible, long-term storage for FAIR data. GISAID, NCBI SRA, ENA, Zenodo for analysis outputs.

Within the thesis on FAIR (Findable, Accessible, Interoperable, Reusable) data protocols for outbreak sequencing research, identifying and serving core stakeholders is paramount. Effective pathogen genomics surveillance relies on a complex ecosystem where data, protocols, and tools flow from laboratory sequencers to public health decision-makers. This document outlines the key stakeholders, their specific needs, and provides detailed application notes and protocols to bridge gaps in the outbreak sequencing data pipeline, ensuring FAIR principles are operationalized from bench to policy.

Table 1: Core Stakeholders, Primary Needs, and Key FAIR Data Challenges

Stakeholder Group Primary Needs Key FAIR Data Challenges Typical Data Output/Requirement
Bench Researchers Standardized, validated wet-lab protocols; access to positive controls/reference materials; streamlined data submission tools. Interoperability of sample metadata; Reusability of protocols. Raw sequencing reads (FASTQ), sample metadata.
Bioinformaticians Access to raw & processed data; standardized, portable analysis pipelines; computational resources. Findability & Accessibility of datasets; Interoperability of data formats. Processed data (VCF, consensus sequences), analysis reports.
Epidemiologists Contextualized data (time, location, host); lineage/clade assignments; visualization tools. Interoperability of epidemiological & genomic data. Annotated sequences, phylogenetic trees, outbreak clusters.
Public Health Agencies Timely, interpretable insights; risk assessment reports; data for policy & intervention. Reusability of data for retrospective analysis; Accessibility with appropriate governance. Situation reports, variant risk assessments, public dashboards.
Journal Publishers/ Funders Data availability statements; adherence to data sharing policies; reproducible methods. Findability via persistent identifiers (DOIs). Data repository accession numbers, detailed methods.

Table 2: Current Global Genomic Surveillance Sequencing Volume (Representative Data)

Pathogen Category Estimated Global Sequences/Month (2023-2024) Primary Repository Public Access Lag Time (Median)
SARS-CoV-2 ~900,000 GISAID, NCBI Virus 14-30 days
Influenza Virus ~80,000 GISAID, IRD 30-90 days
Mycobacterium tuberculosis ~10,000 ENA, SRA 90-180 days
Foodborne Pathogens (e.g., Salmonella, E. coli) ~15,000 NCBI Pathogen Detection 30-60 days

Application Notes & Detailed Protocols

Application Note AN-01: Standardized Metadata Collection at Point of Sampling

Objective: To ensure Interoperability and Reusability by capturing essential contextual data at the earliest point. Procedure:

  • Digital Data Capture: Utilize mobile applications or digital forms (e.g., ODK Collect, REDCap) linked to Laboratory Information Management Systems (LIMS).
  • Minimum Information Checklist: For each sample, collect:
    • Sample ID (unique barcode).
    • Collection date and time.
    • Geographic location (GPS coordinates preferred).
    • Host/source information (e.g., species, age, sex).
    • Clinical/epidemiological context (e.g., symptom onset, outbreak ID).
    • Collector and submitting institution details.
  • Controlled Vocabularies: Use predefined terms (e.g., from ENVO for environment, NCBI Taxonomy for host) to avoid free-text ambiguity.
  • Data Export: Map collected fields to public repository submission formats (e.g., INSDC, GISAID metadata sheets) prior to sequencing.

Protocol P-01: End-to-End Workflow for Rapid Outbreak Sequencing & Data Sharing

Objective: Provide a detailed, reproducible methodology from nucleic acid to public data release, aligning with FAIR principles.

I. Wet-Lab Protocol: Amplification & Library Preparation (SARS-CoV-2 Example) Reagents & Equipment: Viral transport medium sample, RNA extraction kit (e.g., QIAamp Viral RNA Mini Kit), ARTIC Network primer pools, reverse transcriptase (e.g., SuperScript IV), DNA polymerase (e.g., Q5 Hot Start), library prep kit (e.g., Illumina DNA Prep). Procedure:

  • RNA Extraction: Perform per kit instructions. Include positive (SARS-CoV-2 RNA) and negative (nuclease-free water) extraction controls.
  • Reverse Transcription: Generate cDNA using random hexamers or gene-specific primers.
  • Tiled Multiplex PCR: Amplify virus genome using two pools of ~400bp overlapping amplicons (ARTIC v4.1 design). Cycle conditions: 98°C 30s; [98°C 15s, 63°C 5m] x35 cycles; 72°C 5m.
  • Library Preparation: Quantify PCR products, normalize, and tag with unique dual indices (UDIs) using a transposase-based kit. Clean up libraries using SPRI beads.
  • Quality Control: Assess library size distribution (e.g., Agilent TapeStation, Bioanalyzer) and quantify (e.g., Qubit).

II. Bioinformatics Protocol: FAIR-Compliant Analysis Pipeline Prerequisites: High-performance computing environment, Conda/Mamba for environment management, Git for version control. Software: fastp (QC), minimap2 (alignment), ivar (primer trimming & variant calling), nextclade (lineage assignment), pangolin (lineage classification). Procedure:

  • Data Organization: Create a structured project directory. Name raw FASTQ files with SampleID_R{1,2}.fastq.gz.
  • Automated QC & Trimming:

  • Alignment & Primer Trimming:

  • Lineage Assignment & Reporting:

III. Data Submission Protocol:

  • Anonymize Data: Ensure all patient-identifiable information has been removed.
  • Prepare Metadata: Compile finalized metadata table using repository-specific template.
  • Submit to Primary Repository: Upload FASTQ and consensus FASTA files along with metadata to INSDC member (ENA, SRA, DDBJ) via web portal or API. Obtain accession numbers (ERX, SRX).
  • Submit to Specialist Repository: For rapid outbreak response, concurrently submit consensus sequences and critical metadata to pathogen-specific repositories (e.g., GISAID for respiratory viruses).
  • Publish Workflow: Archive analysis code on GitHub or GitLab. Register pipeline in a workflow registry (e.g., WorkflowHub) and assign a DOI using Zenodo.

Diagrams

G cluster_0 FAIR Data Flow in Outbreak Sequencing Samp Sample Collection (Metadata Capture) Seq Sequencing & Primary Analysis Samp->Seq FASTQ + Metadata Repo Public Data Repositories (GISAID, ENA, SRA) Seq->Repo Annotated Data (FAIR Principles Applied) Analysis Secondary Analysis & Interpretation Repo->Analysis Accessible, Interoperable Data Decision Public Health Action & Policy Analysis->Decision Interpretable Insights Bench Bench Researcher Bench->Samp Bioinfo Bioinformatician Bioinfo->Seq Epi Epidemiologist Epi->Analysis PHA Public Health Agency PHA->Decision

Diagram 1: Stakeholder Roles in the FAIR Outbreak Data Pipeline

workflow Start Sample Received P1 RNA Extraction & QC Start->P1 P2 cDNA Synthesis & Tiled Multiplex PCR P1->P2 P3 Library Prep & Indexing P2->P3 P4 Sequencing P3->P4 B1 Raw Read QC & Trimming (fastp) P4->B1 B2 Alignment & Primer Trim (minimap2, ivar) B1->B2 B3 Variant Calling & Consensus Generation (ivar) B2->B3 B4 Lineage Assignment (nextclade, pangolin) B3->B4 D1 Data Curation & Metadata Finalization B4->D1 D2 Submission to Public Repositories D1->D2 End FAIR Data Release & Archive D2->End

Diagram 2: End-to-End Sequencing and Sharing Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Outbreak Sequencing Research

Item Function & Relevance to FAIR Protocols Example Product(s)
Standardized Reference Material Provides positive control for assay validation; ensures inter-lab comparability (Interoperability, Reusability). NIST SARS-CoV-2 RNA Standard (RM 8485), ATCC controls.
Tiled Multiplex PCR Primer Pools Enables amplification of entire pathogen genomes from minimal input; standardized sets promote data uniformity. ARTIC Network primers, Swift Normalase Amplicon Panels.
Unique Dual Index (UDI) Kits Prevents index hopping/cross-talk during multiplex sequencing; critical for accurate sample tracking (Findability). Illumina IDT for Illumina UDIs, Twist Unique Dual Indexes.
Automated Nucleic Acid Extractors Increases throughput, reduces human error, and standardizes the starting material quality. QIAsymphony, KingFisher, MagMAX kits.
LIMS with Electronic Lab Notebook Manages sample metadata, workflows, and reagents; ensures audit trail and links data to processes. Benchling, LabKey, BaseSpace Clarity LIMS.
Containerized Analysis Pipelines Packages software dependencies for reproducible, portable bioinformatics (Reusability). Docker/Singularity containers, Nextflow pipelines.
Metadata Schema Validators Checks metadata files against community standards before submission, ensuring Interoperability. CVE (CLI validator for ENA), GISAID metadata checker.

Application Notes

Within the framework of FAIR (Findable, Accessible, Interoperable, Reusable) data protocols for outbreak sequencing research, several global initiatives and legal standards form the critical infrastructure for rapid and equitable pathogen data sharing. Their interplay directly enables or constrains the implementation of FAIR principles during public health emergencies.

  • World Health Organization (WHO) Biohub System & Pandemic Accord: The WHO facilitates global pathogen data sharing through normative guidance and new mechanisms like the Biohub System. A key current development is the negotiation of a WHO Pandemic Accord, which aims to establish a comprehensive international framework for pandemic prevention, preparedness, and response, including provisions for pathogen and benefit sharing.

  • Global Research Collaboration for Infectious Disease Preparedness (GLOPID-R): GLOPID-R is a network of major research funding organizations. It operates through a "One Health" approach, aligning research priorities and funding to enable a rapid, coordinated research response to outbreaks. Its core function is to break down silos and accelerate the availability of research resources and data under FAIR principles.

  • International Nucleotide Sequence Database Collaboration (INSDC): Comprising DDBJ, EMBL-EBI, and NCBI, the INSDC is the foundational, long-term open data repository for nucleotide sequences. It is the de facto standard for implementing the "Findable" and "Accessible" pillars of FAIR data in genomics. Submission to INSDC ensures persistent identifiers, rich metadata, and global, unrestricted access.

  • The Nagoya Protocol on Access and Benefit-Sharing (ABS): This international agreement, under the Convention on Biological Diversity, aims to ensure the fair and equitable sharing of benefits arising from the utilization of genetic resources. For outbreak sequencing, it creates a legal framework governing the physical transfer of pathogen samples (Genetic Resources) and associated sequence data (Digital Sequence Information - DSI), which can complicate rapid data sharing during emergencies.

Quantitative Data Summary

Table 1: Key Metrics of Global Initiatives Relevant to Outbreak Sequencing Data

Initiative/Standard Primary Scope Key Metric (Status/Size) Relevance to FAIR Outbreak Data
WHO Biohub Pathogen Sharing Pilot Phase (Operational since 2021) Enhances Accessibility of physical & associated data resources under agreed terms.
GLOPID-R Research Coordination >30 member organizations across 6 continents Promotes Interoperability & Reusability through aligned data standards and priority research calls.
INSDC Sequence Data Repository ~Petabytes of data; Billions of public records Core infrastructure for Findable, Accessible, Interoperable data via unified submission portals.
Nagoya Protocol Legal Compliance 139 Parties (as of 2023) Major factor governing Accessibility terms and potential restrictions on data/sample use.

Experimental Protocols

Protocol 1: FAIR-Compliant Pathogen Sequence Data Submission to INSDC During an Outbreak

Objective: To rapidly generate and submit high-quality pathogen genome sequence data and associated metadata to the INSDC in a manner compliant with FAIR principles, enabling global research access.

Materials:

  • Extracted nucleic acid from pathogen
  • Library preparation kit (e.g., Illumina DNA Prep, Oxford Nanopore LSK-114)
  • Sequencing platform (Illumina, Oxford Nanopore, PacBio)
  • Computational resources for assembly/analysis
  • INSDC member submission portal (e.g., NCBI's BankIt or Submission Portal)
  • Metadata checklist (isolation source, host, location, date, collection details)

Methodology:

  • Sample Preparation & Sequencing: Prepare sequencing library from extracted nucleic acids according to manufacturer’s protocol. Perform sequencing on appropriate platform to achieve desired coverage (e.g., >50x).
  • Bioinformatic Processing: Assemble raw reads into a consensus genome using a reference-based or de novo assembler (e.g., BWA, SPAdes). Perform quality control (completeness, coverage, ambiguity).
  • FAIR Metadata Curation: Compile all relevant metadata using controlled vocabularies where possible (e.g., GSC’s MIxS packages). Essential fields include: geographic location (latitude/longitude), isolation date, host (Disease Ontology ID), sampling source, and collector.
  • INSDC Submission: a. Access an INSDC submission portal (e.g., NCBI). b. Create a new BioProject (overall study) and BioSample (individual sample metadata). c. Upload the consensus genome sequence file (FASTA format). d. Link the sequence to the specific BioSample and BioProject. e. Assign relevant data to a public release date (can be immediate or delayed).
  • Accession Number Acquisition: Upon successful processing, the INSDC will issue unique, persistent accession numbers for the BioProject (PRJNA…), BioSample (SAMN…), and Sequence (AC:…). These must be cited in any publication.

Protocol 2: Framework for Nagoya Protocol Compliance in Cross-Border Outbreak Research

Objective: To establish a legal pathway for acquiring and utilizing pathogen samples and associated data from a country that is a Party to the Nagoya Protocol, ensuring compliance with Access and Benefit-Sharing obligations.

Materials:

  • Material Transfer Agreement (MTA) template with ABS clauses
  • Prior Informed Consent (PIC) documentation from providing country
  • Mutually Agreed Terms (MAT) contract
  • Institutional ABS compliance office contacts

Methodology:

  • Due Diligence: Prior to sample request, determine the Nagoya Protocol status of the source country. Consult its National Focal Point and ABS Clearing-House to identify specific domestic regulatory requirements.
  • Negotiate Mutually Agreed Terms (MAT): Propose MAT to the competent national authority of the provider country. Terms should cover:
    • Scope of use (e.g., non-commercial research, in vitro diagnostics).
    • Type of benefits to be shared (non-monetary: e.g., co-authorship, capacity building, data sharing; monetary: e.g., royalties from commercialization).
    • Reporting and monitoring obligations.
  • Secure Prior Informed Consent (PIC): Obtain formal permission from the provider country for access to the specific genetic resource.
  • Execute Agreements: Formalize PIC and MAT in a legally binding contract or MTA before physical or digital transfer occurs.
  • Track and Report: Maintain detailed records of the sample/data use. Fulfill all benefit-sharing and reporting obligations as stipulated in the MAT. Submit required reports to the provider country and, if applicable, the ABS Clearing-House.

Mandatory Visualizations

fair_nagoya Sample Pathogen Sample (Provider Country) Nagoya Nagoya Protocol Compliance? Sample->Nagoya FAIR_Goal FAIR Global Data Sharing INSDC INSDC Submission (Public Domain) FAIR_Goal->INSDC Nagoya->FAIR_Goal No Steps Due Diligence PIC & MAT Negotiation Nagoya->Steps Yes Steps->FAIR_Goal Legal Pathway Research FAIR-Compliant Research Use INSDC->Research Persistent ID

Title: Legal and Data Pathways for Outbreak Genomics

outbreak_response_workflow cluster_0 Coordinating Frameworks Outbreak Outbreak Detection Seq Rapid Sequencing Outbreak->Seq Analysis FAIR Data Curation & Analysis Seq->Analysis Share Global Data Sharing Hub Analysis->Share Share->Outbreak Informs Response WHO WHO Guidance & Pandemic Accord WHO->Seq GLOPID GLOPID-R Funding Alignment GLOPID->Analysis INSDC_std INSDC Data Standards INSDC_std->Share NP Nagoya Protocol Constraints NP->Share

Title: Coordinated Outbreak Genomic Response Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for FAIR-Compliant Outbreak Sequencing Research

Item Function in Protocol Relevance to FAIR/Global Standards
Standardized Nucleic Acid Extraction Kit (e.g., QIAamp Viral RNA Mini Kit) Ensures high-quality, reproducible input material for sequencing, critical for data quality. Enables Interoperable and Reusable data by standardizing the initial analytical chain.
Long-Read Sequencing Kit (e.g., Oxford Nanopore LSK-114) Allows for rapid, real-time sequencing and complete genome assembly in field or lab settings. Critical for speed in Findable data generation during outbreaks, supported by GLOPID-R priorities.
Metadata Spreadsheet Template (e.g., GSC MIxS checklist) Provides a structured format for capturing essential sample and sequencing metadata. Core tool for achieving Interoperability; required for INSDC submission and aligns with WHO guidance.
INSDC Submission Portal Account (e.g., NCBI login) The direct pipeline for depositing sequence data and metadata into the permanent, open archive. Primary mechanism to make data Findable and Accessible with a persistent identifier.
ABS Compliance Database Access (e.g., ABS Clearing-House) Provides legal information on a country's requirements for accessing genetic resources. Essential for navigating the Accessibility pillar under the legal constraints of the Nagoya Protocol.

A Step-by-Step Guide to Implementing FAIR Outbreak Sequencing Pipelines

Application Notes

In the context of establishing FAIR (Findable, Accessible, Interoperable, and Reusable) data protocols for outbreak pathogen genomics, the initial stage of sample collection and metadata annotation is foundational. The consistent application of standardized metadata is critical for enabling global data integration, comparative analysis, and rapid insight generation during public health emergencies. This protocol advocates for the concurrent use of two complementary standards: the Minimum Information about any (x) Sequence (MIxS) checklists, developed by the Genomic Standards Consortium (GSC), and the Genomic Standards Consortium infectious disease (GSCID) reporting framework. MIxS provides a universal, environment-specific set of core descriptors, while GSCID tailors these requirements specifically for human infectious disease and outbreak investigations, ensuring that epidemiological and clinical context is preserved alongside genomic data. Adherence to these standards at the point of sample collection ensures that downstream sequence data is inherently FAIR-compliant, maximizing its utility for researchers, public health agencies, and drug development professionals tracking pathogen evolution and transmission dynamics.

Protocols

Protocol 1: Pre-Collection Planning and Kit Preparation

  • Define Scope: Determine the appropriate MIxS checklist (e.g., MIMARKS for specimen from an organism, MISAG for genomes from single cells) and cross-reference with the latest GSCID core fields for human infectious disease.
  • Assemble Collection Kit: Prepare sterile collection swabs, viral transport media (VTM), cryovials, and biohazard bags.
  • Prepare Digital Forms: Create a sample tracking sheet or electronic data capture form that includes fields from both MIxS and GSCID. Pre-populate with constants (e.g., collection date, location, collector name) where possible.
  • Assign Unique ID: Generate a persistent, unique identifier for each anticipated sample (e.g., OUTBREAK-2025-HOST-COUNTRY-001). This ID must link all physical samples, extracted derivatives, and metadata.

Protocol 2: Biological Sample Collection with Metadata Capture

  • Sample Acquisition: Collect clinical specimen (e.g., nasopharyngeal swab, blood) using aseptic technique into the prescribed medium. Immediately label the primary container with the assigned Unique ID.
  • Core Metadata Annotation (At Point of Care): Simultaneously, record the minimum mandatory fields as per the integrated checklist.
    • GSCID/Epidemiological: Host disease status, symptoms, symptom onset date, epidemiological case ID, recent travel history.
    • MIxS/Environmental: Collection date and time, geographic location (latitude/longitude), host subject ID (de-identified), sample type (e.g., nasopharyngeal swab).
  • Preservation: Place primary sample immediately on dry ice or at recommended storage temperature (e.g., -80°C) and document storage conditions.

Protocol 3: Laboratory Processing and Metadata Enhancement

  • Sample Accessioning: Upon receipt in the lab, log the sample into the Laboratory Information Management System (LIMS), confirming the Unique ID.
  • Add Laboratory Metadata: Augment the sample record with processing information.
    • MIxS/Lab: Nucleic acid extraction method, extraction kit, concentration, volume.
    • GSCID/Pathogen: Suspected pathogen, target of amplification, sequencing method planned.
  • Derivative Tracking: For any derived material (e.g., extracted RNA, amplified library), create a new record that links back to the primary sample's Unique ID via the derived from field in MIxS.

Protocol 4: Metadata Validation and Submission

  • Checklist Validation: Prior to sequence data submission, run metadata through a validation tool (e.g., the GSC's MIxS validator) to ensure all mandatory fields for the selected packages are complete and formatted correctly.
  • Controlled Vocabulary Compliance: Verify terms against relevant ontologies (e.g., NCBI Taxonomy ID for organism, ENVO for environmental terms, SNOMED CT for clinical terms).
  • Submission: Submit the validated metadata sheet alongside raw sequence files to public repositories (e.g., INSDC partners like ENA, SRA, or DDBJ, or pathogen-specific resources like GISAID) ensuring the metadata is attached using the repository's specified template, which often incorporates MIxS.

Data Presentation

Table 1: Core Mandatory Fields from Integrated MIxS-GSCID Checklist for Outbreak Isolates

Field Name Standard Source Description Example Value Required Ontology/Term
investigation type MIxS Nature of study pathogen-associated ENA: pathogen-associated
project name MIxS Study identifier 2025_RespVirus_Surveillance n/a
lat_lon MIxS Geographic coordinates 45.5017 N, 73.5673 W decimal degrees
collection_date MIxS Time of sample collection 2025-03-15 ISO 8601
hostsubjectid MIxS De-identified host identifier Patient_Alpha_001 n/a
hostdiseasestat GSCID Health status of host Symptomatic n/a
host_sex MIxS Host gender female PBI: female
host_age MIxS Host age in years 45 numeric
suspected_pathogen GSCID Suspected causative agent SARS-CoV-2 NCBI TaxID: 2697049
isolation_source MIxS Body site of isolation nasopharyngeal swab UBERON: 0001729
seq_meth MIxS Sequencing methodology Illumina NovaSeq 6000 n/a

Experimental Protocols

Detailed Methodology: Viral RNA Extraction for Sequencing

Principle: To obtain high-quality, inhibitor-free viral RNA from clinical swab media for next-generation sequencing library preparation. Reagents: QIAamp Viral RNA Mini Kit (Qiagen), β-mercaptoethanol, absolute ethanol, nuclease-free water. Procedure:

  • Lysis: Pipette 140µl of prepared VTM sample into a 1.5ml microcentrifuge tube. Add 560µl of Buffer AVL containing carrier RNA. Mix by pulse-vortexing for 15s. Incubate at room temperature (15–25°C) for 10 min.
  • Precipitation: Briefly centrifuge the tube. Add 560µl of ethanol (96–100%) to the lysate. Mix immediately by pulse-vortexing for 15s. After mixing, briefly centrifuge again.
  • Binding: Apply 630µl of the lysate-ethanol mixture to the QIAamp Mini column. Centrifuge at 6000 x g for 1 min. Discard flow-through and repeat with remaining mixture.
  • Wash 1: Add 500µl Buffer AW1 to the column. Centrifuge at 6000 x g for 1 min. Place column in a clean 2ml collection tube. Discard flow-through.
  • Wash 2: Add 500µl Buffer AW2 to the column. Centrifuge at full speed (20,000 x g) for 3 min. Discard flow-through and collection tube.
  • Elution: Place the column in a new, labeled 1.5ml microcentrifuge tube. Apply 60µl of Buffer AVE (or nuclease-free water) to the center of the column membrane. Incubate at room temperature for 1 min. Centrifuge at 6000 x g for 1 min. The eluate contains the viral RNA. Quantify using a fluorometric method (e.g., Qubit RNA HS Assay).

Visualizations

G Start Pre-Collection Planning P1 Define MIxS + GSCID Scope Start->P1 P2 Prepare Collection Kit & Digital Forms P1->P2 P3 Assign Unique Sample ID P2->P3 Collect Sample Collection P3->Collect C1 Aseptic Acquisition (Label Primary Container) Collect->C1 C2 Record Core Metadata (Host, Location, Time, Clinical) C1->C2 Lab Lab Processing C2->Lab L1 LIMS Accession & Link to Unique ID Lab->L1 L2 Add Processing Metadata (Extraction, QC) L1->L2 Submit Validation & Submission L2->Submit V1 Validate Checklist & Ontology Terms Submit->V1 V2 Submit to Public Repository (e.g., ENA, GISAID) V1->V2 FAIR FAIR-Compliant Outbreak Sequence Dataset V2->FAIR

Title: Outbreak Sample & Metadata Workflow

G cluster_0 Integrated FAIR Metadata Record Metadata Sample Metadata MIxS MIxS Checklists (Universal Core) Metadata->MIxS GSCID GSCID Framework (Outbreak Specific) Metadata->GSCID EnvGeo Env/Geo Data (lat_lon, env_medium) MIxS->EnvGeo SeqTech Sequencing Tech (seq_meth, lib_layout) MIxS->SeqTech HostClin Host & Clinical Data (host_age, disease_stat) GSCID->HostClin Repo Public Repository (e.g., ENA, GISAID) EnvGeo->Repo HostClin->Repo SeqTech->Repo

Title: Metadata Standards Integration for FAIR Data

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions for Outbreak Sample Processing

Item Function & Rationale
Viral Transport Media (VTM) Stabilizes viral nucleic acids and preserves pathogen viability during sample transport from clinic to lab.
Nucleic Acid Extraction Kit (e.g., QIAamp Viral RNA Mini Kit) Isolates high-purity, inhibitor-free viral RNA/DNA from complex clinical matrices, essential for sequencing.
RNase Inhibitors Protects labile RNA genomes from degradation during extraction and library preparation steps.
Ultra-Low Temperature Freezer (-80°C) Provides long-term, stable storage for original clinical specimens and extracted nucleic acids.
Laboratory Information Management System (LIMS) Tracks sample provenance, processing steps, and links physical samples to digital metadata.
Ontology Lookup Service (e.g., OLS, NCBI Taxonomy) Ensures metadata terms use standardized, controlled vocabularies for interoperability.
MIxS/GSCID Validator Tool Software that checks metadata sheets for completeness and format prior to public submission.

Within the framework of FAIR (Findable, Accessible, Interoperable, Reusable) data protocols for outbreak sequencing research, Stage 2 is critical. It encompasses the generation of raw, unprocessed sequencing data and its immediate, validated submission to international nucleotide archival repositories. This phase ensures data availability is decoupled from later, often lengthy, analysis and curation stages, enabling rapid global response during a public health crisis.

Three primary repositories form the backbone of global sequence data sharing. Their synchronized data is accessible through the International Nucleotide Sequence Database Collaboration (INSDC).

Table 1: Comparison of Major Public Sequence Repositories

Feature SRA (NCBI) ENA (EMBL-EBI) GSA (China National Center for Bioinformation)
Full Name Sequence Read Archive European Nucleotide Archive Genome Sequence Archive
Primary Jurisdiction International, NIH-funded International, EMBL-member states Mainland China
Mandatory Submission For NIH-funded research For publications in EMBL-EBI journals For research conducted in China
Accepted Raw Formats FASTQ, BAM, CRAM, PacBio HDF5, ONT FAST5 FASTQ, BAM, CRAM, PacBio BAM, ONT FAST5 FASTQ, BAM, CRAM
Metadata Standard INSDC (SRA XML) INSDC (Webin XML/JSON) INSDC (GSA JSON)
Accession Prefix SRR, SRX, SRS, SRP ERR, ERX, ERS, ERP CRR, CRX, CRS, CRP
Immediate Release Policy Yes, with "hold until date" option Yes, with "hold until date" option Yes, with specified release date
Typical Processing Time 1-3 business days 1-2 business days 2-5 business days

Protocol: End-to-End Workflow for Raw Data Deposition

This protocol details the steps from sequencer output to successful repository accession.

Pre-Submission: Data and Metadata Preparation

Objective: To generate validated sequence files and structured metadata. Materials: Sequencing platform (Illumina, Oxford Nanopore, PacBio), high-performance computing cluster or server, metadata spreadsheet template.

Procedure:

  • Demultiplexing: If required, use platform-specific software (e.g., bcl2fastq for Illumina, guppy_barcoder for ONT) to generate per-sample FASTQ files.
  • Quality Assessment: Run FastQC or NanoPlot (for ONT) on raw FASTQs to confirm expected read quality, length, and yield.
  • File Integrity Check: Generate MD5 checksums for all files to be submitted.

  • Metadata Compilation: Download the repository's metadata template. Complete all fields, critically including:
    • Sample: organism, isolate, geographic location, collection date.
    • Experiment: library strategy (AMPLICON, WGS), instrument model.
    • Study: descriptive title, relevant outbreak identifier, principal investigator.

Submission to SRA via Command-Line (usingprefetchandfasterq-dumptools)

Objective: To programmatically upload data using NCBI's command-line utilities. Reagents: NCBI SRA Toolkit, Aspera Command-Line Client (optional, for faster transfer).

Procedure:

  • Create Submission Directory:

  • Generate SRA Metadata XML: Use the SRA Metagenomics web portal or SRA_submission.xlsx template to generate the final XML.
  • Validate Metadata: Use NCBI's validate option in the submission portal prior to file transfer.

  • Upload Files: Use Aspera (ascp) or FTP to transfer files to the designated NCBI secure server.

  • Finalize Submission: In the SRA submission portal, link the uploaded files to the metadata and finalize. Record the returned BioProject (PRJNA...) and SRA (SRP...) accessions.

Submission to ENA via Webin-CLI

Objective: To use EMBL-EBI's comprehensive command-line interface for validated submission. Reagents: Webin-CLI tool (Java-based).

Procedure:

  • Install and Authenticate:

  • Prepare Manifest File: Create a manifest.txt file specifying files, metadata, and analysis type.

  • Validate and Submit:

  • Receive Accessions: Upon success, the CLI returns sample (ERS), experiment (ERX), and run (ERR) accessions.

Diagrams

Stage 2 Submission Workflow

G Sequencer Sequencer Output (BCL, FAST5, HDF5) Demux Demultiplexing & File Conversion Sequencer->Demux Platform Tools Fastq Sample FASTQ Files Demux->Fastq QC Quality Control (FastQC/NanoPlot) Fastq->QC Validate Local Validation (Webin-CLI, Checklist) QC->Validate MD5 & Format Check Meta Metadata Compilation Meta->Validate Upload File Upload (Aspera/FTP) Validate->Upload Validated Package Submit Final Submission & Linking Upload->Submit Transfer Receipt Accession Accession Issued (SRR/ERR/CRR) Submit->Accession Release Immediate Public Release Accession->Release No Embargo

Title: Raw Data Deposition Workflow

INSDC Repository Synchronization

G SRA NCBI SRA (USA) ENA EMBL-EBI ENA (Europe) SRA->ENA Daily Sync GSA CNCB GSA (Asia) ENA->GSA Daily Sync GSA->SRA Daily Sync User1 Researcher User1->SRA Submit User2 Scientist User2->GSA Submit INSDB International Nucleotide Sequence Database Collaboration (INSDC)

Title: INSDC Global Data Synchronization

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Tools for Raw Data Deposition

Item Function in Stage 2 Example/Format
Sequencing Platform Software Primary demultiplexing and basecalling. Converts raw signals to sequence reads. Illumina DRAGEN, Oxford Nanopore Guppy, PacBio SMRT Link
Quality Control Tools Provides initial assessment of read quality, length, and potential contaminants to flag issues prior to submission. FastQC, NanoPlot (for ONT), MinIONQC
Checksum Generator Creates unique file fingerprints (MD5/SHA256) to verify file integrity before, during, and after transfer. md5sum, sha256sum (Linux commands)
Metadata Spreadsheet Template Repository-provided template ensuring all required descriptive, administrative, and technical fields are populated correctly. NCBI SRA metadata template, ENA Webin spreadsheet
Command-Line Submission Tools Enables automated, scriptable, and high-throughput submission of data and metadata, reducing manual portal errors. NCBI SRA Toolkit (prefetch, fasterq-dump), ENA Webin-CLI, Aspera ascp
Data Transfer Client High-speed, secure file transfer protocol for uploading large sequence files (TB scale) to repository servers. Aspera Connect, FTP client (e.g., lftp), or HTTPS
Validation Software Performs pre-submission checks on file formats, metadata completeness, and consistency, preventing submission failures. Built into Webin-CLI, SRA validation in portal

Within the thesis framework of FAIR data protocols for outbreak sequencing research, this stage addresses the computational reproducibility and provenance tracking of analytical workflows. The use of standardized workflow languages like Common Workflow Language (CWL) and Nextflow, combined with registry services like the GA4GH Tool Registry Service (TRS), is critical for ensuring that genomic analyses of pathogens are Findable, Accessible, Interoperable, and Reusable.

Core Technology Comparison

Table 1: Comparison of Workflow Management Systems for Outbreak Bioinformatics

Feature Common Workflow Language (CWL) Nextflow Snakemake
Primary Language YAML/JSON DSL (Groovy-based) Python-based DSL
Execution Model Declarative, describes inputs/outputs Imperative, dataflow oriented Rule-based
Portability High (spec-focused, many runners) High (container-focused) Medium-High
Provenance Logging Via CWL-prov, Research Object CRATE Built-in timeline/trace report Built-in report
GA4GH TRS Support Native descriptors registrable Tools can be packaged for TRS Limited native support
Key Strength in Outbreak Context Standardization for cross-lab sharing Scalability on clusters/cloud Integration with Python ecosystem
Adoption in Public Health Growing (used in Galaxy, EPI2ME) High (e.g., nf-core/viralrecon) Common in academic pipelines

Application Notes

Implementing FAIR Workflows with CWL

CWL provides a vendor-neutral description of command-line tools and workflows. For outbreak sequencing, a CWL "CommandLineTool" descriptor for a variant caller like bcftools mpileup ensures the exact version, parameters, and base Docker/Singularity image are documented. A CWL "Workflow" descriptor chains these tools (e.g., quality control → alignment → variant calling). Provenance, captured via CWL-prov, generates a detailed record of the execution, linking input sequence reads (with SRA accessions) to the final VCF file, essential for auditability during an outbreak investigation.

Scalable Analysis with Nextflow

Nextflow enables scalable and reproducible pipelines, crucial for rapidly analyzing thousands of pathogen genomes. Pipelines like nf-core/viralrecon (a community-built pipeline for SARS-CoV-2 genome assembly and variant calling) exemplify this. Nextflow's built-in provenance includes a comprehensive execution trace and timeline report, detailing when and where each process ran, its duration, and resource consumption. This supports the "R" (Reusable) and "I" (Interoperable) FAIR principles by allowing the same pipeline to run on diverse compute infrastructures (local HPC, AWS, Google Cloud) with consistent results.

Workflow Discovery and Sharing via GA4GH TRS

The GA4GH TRS provides a standardized API for registering, discovering, and launching bioinformatics tools and workflows. A public health lab can register its validated CWL or Nextflow outbreak analysis pipeline on a TRS instance (e.g., Dockstore). Researchers globally can then discover, pull, and execute the exact versioned workflow, ensuring methodological consistency across different groups analyzing the same outbreak. TRS entries include versioning, authors, and descriptors, directly supporting FAIR principles for workflows themselves.

Detailed Protocols

Protocol: Creating a Reproducible Variant Calling Workflow with CWL and TRS Registration

Objective: To create a reproducible, TRS-registrable workflow for calling variants from viral sequencing data.

Materials:

  • Input Data: Paired-end FASTQ files from Illumina sequencing of a viral isolate.
  • Software: cwltool (CWL reference runner), docker.
  • Tools: fastp (v0.23.2), bwa (v0.7.17), samtools (v1.15), bcftools (v1.15).
  • Reference Genome: NCBI RefSeq FASTA file for the target virus.

Method:

  • Tool Definition: Write a CWL CommandLineTool descriptor for each bioinformatics tool (e.g., fastp.cwl, bwa-mem.cwl). Each descriptor specifies the Docker container image, base command, input parameters (e.g., --threads), inputs (e.g., reads_fastq), and outputs (e.g., trimmed_fastq).
  • Workflow Composition: Write a CWL Workflow descriptor (variant-calling-workflow.cwl). This file defines the steps: a. quality_control: Runs fastp.cwl on input FASTQs. b. alignment: Runs bwa-mem.cwl using the trimmed reads and the reference genome. c. sort_index: Runs samtools sort.cwl and samtools index.cwl on the alignment output. d. variant_calling: Runs bcftools mpileup.cwl and bcftools call.cwl on the sorted BAM.
  • Execution:

  • Provenance Generation: The --provenance flag generates a PROV-O compliant research object, packaging workflow outputs, parameters, and execution trace.
  • TRS Registration: Package the CWL files and a Dockerfile in a GitHub repository. Register the repository on a TRS-compliant platform like Dockstore by linking the GitHub repo. The main workflow descriptor is tagged with a version (e.g., 1.0.0).

Protocol: Deploying a Nextflow Outbreak Pipeline from a TRS Endpoint

Objective: To launch a versioned, containerized Nextflow pipeline for viral genome analysis, retrieved from a GA4GH TRS.

Materials:

  • Compute: A system with Nextflow (v22.10+) and Docker/Podman installed.
  • Data: A directory of FASTQ files and a sample sheet CSV.
  • TRS Endpoint: URL of a TRS API (e.g., Dockstore: https://dockstore.org/api/api/ga4gh/trs/v2).

Method:

  • Pipeline Discovery: Query the TRS endpoint to find the target pipeline (e.g., nf-core/viralrecon). Note its id and desired version descriptor_type (NFL for Nextflow).
  • Pipeline Launch: Use Nextflow's run command with the TRS URL. Specify inputs via a Nextflow configuration file or command line.

  • Provenance Capture: Upon completion, Nextflow automatically generates a trace.txt report (tab-separated execution log) and a report.html file with resource usage, timeline, and command lines for every process.
  • FAIR Output: The results/ directory contains analysis outputs, and the Nextflow reports provide computational provenance. The pipeline's TRS source guarantees the workflow's identity and version.

Visualization

Diagram 1: FAIR Outbreak Analysis Workflow Lifecycle

G Start FASTQ Reads & Metadata CWL CWL/Nextflow Workflow Descriptor Start->CWL Inputs TRS GA4GH TRS (Workflow Registry) TRS->CWL Retrieves Exe Execution Engine (cwltool, Nextflow) CWL->Exe Defines Prov Provenance (CWL-Prov, Nextflow Trace) Exe->Prov Generates FAIR FAIR Output (Annotated Variants, Report) Exe->FAIR Produces Prov->FAIR Describes

Diagram 2: Nextflow Execution Provenance Data Model

G Run Nextflow Run Trace Trace Report process hash status exit %cpu rss vmem time Run->Trace Timeline Timeline HTML process duration compute platform Run->Timeline Report Execution Report summary resource utilization commands Run->Report

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions for Reproducible Outbreak Bioinformatics

Item Function in Workflow Provenance Example/Note
CWL Descriptor Files (.cwl, .yml) Declarative, standardized description of tools and workflow logic, enabling portability. bwa-mem.cwl defines the Docker image, command, inputs, and outputs for BWA-MEM.
Nextflow Pipeline Script (.nf) Defines the dataflow and processes of a scalable, containerized pipeline. The main main.nf script in nf-core/viralrecon.
Container Images (Docker/Singularity) Encapsulates all software dependencies, guaranteeing identical execution environments. quay.io/biocontainers/bcftools:1.15--hfe0f4f8_2
GA4GH TRS API Endpoint Serves as a versioned registry for discovering and launching workflow descriptors. Dockstore API: https://dockstore.org/api/ga4gh/trs/v2
Provenance Log File Immutable record of a workflow run, linking inputs, parameters, software, and outputs. CWL-prov research_object/, Nextflow trace.txt and report.html.
Workflow Runner Software that interprets and executes the workflow descriptor. cwltool, toil, nextflow, cromwell.
Sample Metadata Sheet (.csv, .tsv) Structured sample information linking biological context to sequencing files, critical for FAIR outputs. Must include sampleid, sequencingrun, collectiondate, and geographiclocation.

Application Notes: Database Selection and Comparative Analysis

Within FAIR data protocols for outbreak sequencing research, selecting the appropriate public database for sharing interpreted genomic data is critical for ensuring data interoperability and reuse. The three primary repositories—GISAID, NCBI Virus, and BV-BRC—serve distinct yet complementary functions. The choice depends on data type, intended use, and community standards.

GISAID (Global Initiative on Sharing All Influenza Data) is the de facto standard for sharing consensus genome assemblies and associated metadata during acute viral outbreaks, most notably for influenza and SARS-CoV-2. It operates under a mechanism that recognizes and protects data contributors' rights, which has been pivotal for rapid global collaboration. Data access requires registration and agreement to its terms.

NCBI Virus offers a broad, open-data repository for viral sequence data (raw reads, assemblies, and annotated sequences) integrated with the broader NCBI toolkit (e.g., SRA, GenBank). It supports FAIR principles through rich metadata standards and programmatic access via APIs.

BV-BRC (Bacterial and Viral Bioinformatics Resource Center) merges the former IRD and PATRIC resources. It is a comprehensive analysis platform that supports the deposition of both raw and assembled data, with a strong emphasis on integrated computational analysis tools for comparative genomics and lineage assignment.

The following table summarizes the key quantitative and qualitative characteristics of each platform, based on current public data and access policies.

Table 1: Comparative Analysis of Public Databases for Viral Outbreak Data Sharing

Feature GISAID NCBI Virus BV-BRC
Primary Focus Outbreak response for select pathogens (e.g., Influenza, SARS-CoV-2) Comprehensive archive for all viral sequences Integrated bioinformatics resource for bacterial & viral pathogens
Data Types Accepted Consensus genome assemblies, metadata Raw reads (SRA), assemblies, annotated genomes Raw reads, assemblies, annotated genomes, expression data
Access Model Controlled-access (registration, terms) Open-access Open-access
Key Analytical Tools EpiCoV, EpiFlu, lineage reports (Pango, clades) BLAST, variation analysis, sequence alignment Genome annotation, comparative pathway/genomics, phylogenetic tree building
Metadata Standards GISAID-specific curated metadata fields INSDC / SRA metadata standards BV-BRC standardized templates
FAIR Alignment High on Reuse (clear terms), variable on machine Accessibility High on Accessibility and Interoperability High on Interoperability and Reusability (integrated tools)
Typical Submission Volume (Pathogen Example) >16 million SARS-CoV-2 sequences >10 million viral sequences across all taxa Millions of bacterial/viral genomes & associated data
Unique Identifier System Epi-Isolate ID (EPIISL#) GenBank/SRA accession (e.g., MN908947) BV-BRC ID (e.g., 201674.3)
Programmatic API Access Limited, web-based queries Full API (Entrez) available Comprehensive API available

Experimental Protocols

Protocol 1: Submitting a SARS-CoV-2 Consensus Genome Assembly and Lineage Data to GISAID

This protocol details the steps for submitting a viral consensus sequence (FASTA) and associated metadata to GISAID, a critical final step in an outbreak sequencing workflow adhering to FAIR principles.

I. Materials & Pre-Submission Requirements

  • Research Reagent Solutions:
    • Final, High-Quality Consensus Sequence: A SARS-CoV-2 genome assembly in FASTA format (≥29,000 bp, low ambiguity).
    • Curated Metadata Spreadsheet: Compiled according to GISAID's EpiCoV submission template.
    • GISAID User Account: Registered and approved contributor account.
    • Bioinformatics Tool: Nextclade (or Pangolin) for preliminary lineage/clade assignment.
    • Validation Software: Basic local FASTA validator (e.g., seqkit stats).

II. Methodology

  • Sequence & Metadata Finalization:
    • Run the final consensus FASTA file through Nextclade (clade assignment) and/or Pangolin (lineage assignment). Record the results.
    • Complete the GISAID metadata Excel template meticulously. Mandatory fields include: virus name, collection date, location, submitting lab, sequencing technology, and assembly method.

  • GISAID Portal Submission:

    • Log into the GISAID EpiCoV submission portal.
    • Select "New Submission" and choose "Virus." Select "Coronavirus" and then "SARS-CoV-2."
    • Follow the step-by-step web form, which mirrors the metadata spreadsheet. Paste or upload the FASTA sequence directly into the provided field.
    • Input the preliminary lineage/clade data from Nextclade/Pangolin in the relevant field.

  • Validation and Confirmation:

    • The GISAID system will perform automated checks on sequence length, ambiguous bases, and metadata completeness.
    • Review the summary page carefully. Submit the data.
    • Upon successful processing, you will receive an email with the provisional EPI_ISL identifier. Full accession is provided after final curation.

  • Post-Submission:

    • Record the EPI_ISL ID in your local laboratory information management system (LIMS).
    • Cite this identifier in any subsequent publications or reports.

Protocol 2: Depositing Raw Sequence Reads and Assembled Genome to NCBI Virus via SRA and GenBank

This protocol describes a parallel submission pathway suitable for broader viral pathogen data, contributing raw reads to the Sequence Read Archive (SRA) and the assembled genome to GenBank, maximizing machine accessibility.

I. Materials & Pre-Submission Requirements

  • Research Reagent Solutions:
    • Raw Sequencing Reads: Compressed FASTQ files (R1 and R2).
    • Assembled Genome: Final consensus sequence in FASTA format.
    • Annotation File (Optional): GFF3 file of genome annotations.
    • NCBI User Account: With appropriate submission privileges.
    • Metadata File: SRA metadata template (.tsv or .xlsx).
    • Submission Software: NCBI's command-line tools (prefetch, fasterq-dump not required for submission) or the web-based Submission Portal.

II. Methodology

  • BioProject and BioSample Registration:
    • If not existing, create a BioProject describing the overarching research study.
    • Create a BioSample record for the specific viral isolate, providing sample-specific attributes (host, collection date, geographic location, isolate).

  • SRA Submission (Raw Reads):

    • In the SRA submission portal, link the submission to the created BioProject and BioSample.
    • Upload the metadata file detailing library layout (PAIRED), instrument, and strategy.
    • Upload the FASTQ files via FTP, Aspera, or directly through the browser.

  • GenBank Submission (Assembly):

    • Using the NCBI Nucleotide submission portal (BankIt or tbl2asn), start a new "Viral Genome" submission.
    • Link to the same BioProject and BioSample used for the SRA submission.
    • Upload the FASTA file and, if available, the annotation GFF3 file.
    • Fill in the required information: organism, isolate name, and annotator information. Define the source modifers and gene features.

  • Validation and Release:

    • NCBI staff will run validation checks. Correspond with them via the submission portal to address any queries.
    • Upon acceptance, you will receive SRA run accession(s) (e.g., SRR#) and a GenBank accession (e.g., MT#).
    • These accessions are publicly accessible upon your specified release date.

Visualizations

GISAID_Submission_Workflow Start Final Consensus Genome (FASTA) QC Local QC & Preliminary Lineage Assignment Start->QC Meta Curated Metadata (GISAID Template) Meta->QC Portal GISAID EpiCoV Submission Portal QC->Portal Upload Validate Automated GISAID Validation Checks Portal->Validate Validate->Portal Fail/Revise ID Receive EPI_ISL Accession ID Validate->ID Pass Record Record ID in LIMS & Cite in Publications ID->Record

Title: GISAID Data Submission and Accession Workflow

FAIR_Outbreak_Data_Flow RawData Raw Sequencing Reads AssembledData Interpreted Data: Assemblies, Variants, Lineages RawData->AssembledData DB_Select Database Selection Criteria AssembledData->DB_Select GISAID GISAID DB_Select->GISAID Outbreak Response NCBI NCBI Virus (SRA/GenBank) DB_Select->NCBI Open Archive & Reference BVBRC BV-BRC DB_Select->BVBRC Integrated Analysis Community Global Research Community GISAID->Community Controlled Access NCBI->Community Open Access BVBRC->Community Open Access Analysis Comparative Analysis & Reuse Community->Analysis

Title: FAIR Outbreak Data Sharing Pathway to Public Databases

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for Database Submission and Analysis

Item Function in Protocol Example / Source
Consensus Genome Assembly (FASTA) The primary interpreted data product for sharing; the nucleotide sequence of the viral isolate. Output from assemblers (iVirAL, SPAdes) or pipelines (Nextflow, SnakeMake).
Curated Metadata Template Ensures FAIR compliance by providing structured, contextual information about the sequence. GISAID EpiCoV.xlsx, NCBI SRA metadata template.
Lineage Assignment Tool Provides critical interpreted data (lineage/clade) for contextualizing the genome within the outbreak. Pangolin, UShER, Nextclade.
Submission Portal Account Authenticated access required to submit data to the chosen repository. GISAID, NCBI My Bibliography, BV-BRC account.
Sequence Read Archive (SRA) Toolkit Facilitates the management and submission of raw sequencing read data to NCBI. prefetch, fasterq-dump (for download); Web Portal for upload.
Genome Annotation File (GFF3/GTF) Enhances reusability by providing coordinates of genomic features (genes, proteins). Output from annotation tools (Prokka, VAPiD, NCBI PGAAP).
Data Validation Software Performs pre-submission checks on sequence quality and format to prevent submission failure. seqkit stats, INSDC validator, platform-specific checkers.

Overcoming Common FAIR Data Hurdles in High-Pressure Outbreak Scenarios

Application Notes

In the context of FAIR data protocols for outbreak sequencing research, incomplete or inconsistent metadata is a primary barrier to data reusability, interoperability, and rapid response. Metadata describes the who, what, when, where, and how of sample collection and sequencing, and its quality directly impacts analytical validity. Current challenges include missing critical fields (e.g., collection date, geographic location), use of non-standardized terms, and format inconsistencies that prevent automated data integration.

The implementation of community-defined metadata templates, coupled with automated validation tools, provides a systematic solution. These protocols ensure that data shared in public repositories like the International Nucleotide Sequence Database Collaboration (INSDC) or outbreak-specific portals (e.g., GISAID, NCBI Virus) adheres to FAIR principles, enabling efficient aggregation and comparative analysis during public health emergencies.

Table 1: Impact of Inconsistent Metadata on Outbreak Data Analysis

Metadata Issue Common Example Impact on Analysis
Missing Collection Date Date field blank or "unknown" Impossible to construct accurate phylogenetic timelines or estimate transmission rates.
Non-Standard Location "New York," "NYC," "New York City" Inability to geospatially cluster cases without manual curation, slowing hotspot identification.
Inconsistent Host Terminology "Homo sapiens," "Human," "patient" Complicates filtering and comparative studies across datasets, risking erroneous conclusions.
Unspecified Measurement Units Viral load given as "35" (Ct value? copies/mL?) Renders quantitative data unusable for meta-analysis or modeling.

Experimental Protocols

Protocol 2.1: Deployment and Use of a Standardized Metadata Template

Objective: To ensure consistent and complete capture of metadata for viral genome sequences generated during an outbreak investigation.

Materials:

  • Sample information (clinical/environmental).
  • Standardized metadata template (e.g., INSDC / GISAID pathogen sample checklist, MIxS-based template).
  • Spreadsheet software or a Laboratory Information Management System (LIMS).

Methodology:

  • Template Selection: Download the most current version of a community-agreed template. For outbreak sequencing, the "NCBI Virus Pathogen Sample Checklist" is recommended as it aligns with INSDC standards and includes outbreak-critical fields.
  • Field Population: For each sequenced sample, populate every field in the template. Use controlled vocabulary where specified (e.g., for "hosthealthstate," use terms like "diseased," "healthy").
  • Mandatory Field Verification: Ensure all fields designated as "Mandatory" (M) are completed. These typically include:
    • sample_id (unique identifier)
    • collect_date (YYYY-MM-DD)
    • geo_loc_name (country: region, e.g., "USA: New York City")
    • host (e.g., "Homo sapiens")
    • isolate (virus isolate name)
    • lat_lon (decimal degrees)
  • Data Export: Save the populated template as a comma-separated values (.csv) or tab-separated values (.tsv) file for submission to repositories or internal validation.

Protocol 2.2: Automated Metadata Validation Using Command-Line Tools

Objective: To programmatically check metadata files for completeness, syntactic correctness, and adherence to vocabulary rules prior to data submission.

Materials:

  • Metadata file (.csv/.tsv) from Protocol 2.1.
  • Computer with Python 3.8+ installed.
  • Validation tool (e.g., frictionless framework, checkm for MIxS, or custom scripts).

Methodology (using a generic frictionless-inspired approach):

  • Schema Definition: Create a JSON Table Schema file (schema.json) that defines constraints for each metadata field (required fields, allowed patterns, permissible values).

  • Validation Execution: Run a validation script.

  • Error Review and Correction: The tool outputs a report listing all errors (e.g., missing required field, date format mismatch, term not in enum). Manually correct the source metadata file and re-validate until no critical errors remain.

  • Integration: This validation step can be integrated into a bioinformatics pipeline, triggering automatically upon metadata file creation.

Mandatory Visualizations

metadata_workflow Sample_Collection Sample_Collection Lab_Sequencing Lab_Sequencing Sample_Collection->Lab_Sequencing Populate_Template Populate_Template Lab_Sequencing->Populate_Template Validate_Metadata Validate_Metadata Populate_Template->Validate_Metadata Validate_Metadata->Populate_Template Fail Submit_Repository Submit_Repository Validate_Metadata->Submit_Repository Pass FAIR_Data FAIR_Data Submit_Repository->FAIR_Data

Diagram 1: Metadata Curation & Validation Workflow for Outbreak Sequences (80 chars)

fair_impact cluster_0 Input Problem cluster_1 FAIR Outcome Templates Templates Interoperable Interoperable Templates->Interoperable Validation Validation Reusable Reusable Validation->Reusable FAIR_Goal FAIR_Goal Incomplete Incomplete Incomplete->Templates solves Inconsistent Inconsistent Inconsistent->Validation solves Interoperable->FAIR_Goal Reusable->FAIR_Goal

Diagram 2: How Templates & Validation Tools Achieve FAIR Data Goals (74 chars)

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools for Metadata Standardization and Validation

Item / Solution Category Primary Function
INSDC / GISAID Submission Checklists Template Provides the authoritative list of required and recommended metadata fields for public data deposition.
MIxS (Minimum Information about any (x) Sequence) Standards Template & Vocabulary Defines core environmental packages (e.g., MIMS, MIMARKS) and a large set of curated terms for consistent reporting.
NCBI Datasets Command-Line Tools Validation & Retrieval Includes metadata validators and tools to download standardized metadata for existing datasets.
Frictionless Framework Validation Software A Python/CLI toolkit for creating data schemas and validating tabular data files against them.
EDAM-Bioimaging Ontology Vocabulary Provides standardized terms for imaging metadata, relevant for correlative microscopy studies in pathogenesis.
CWL (Common Workflow Language) / Nextflow Pipeline Framework Allows embedding metadata validation steps into reproducible bioinformatics workflows.
LinkML (Linked Data Modeling Language) Schema Framework A modeling language for generating validation schemas, conversion code, and documentation from one central source.

Balancing Rapid Sharing with Ethical and Legal Constraints (Data Sovereignty, GDPR)

Application Notes for FAIR Outbreak Sequencing Data Sharing

The implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles in outbreak genomics must be reconciled with jurisdictional data sovereignty laws and the EU's General Data Protection Regulation (GDPR). The primary challenge is enabling rapid, global scientific collaboration while adhering to legal frameworks that restrict cross-border data flows and protect individual privacy.

Table 1: Key Legal/Ethical Constraints vs. FAIR Data Sharing Enablers

Constraint/Enabler Core Principle Impact on Outbreak Data Sharing Potential Mitigation Strategy
GDPR (Article 9) Protects special category data (e.g., health, genetic data). Requires explicit consent or derogations for processing; limits broad sharing of patient-linked sequences. Pseudonymization; use for public health derogation; data use agreements.
Data Sovereignty Data subject to laws of country where it is collected/stored. Prohibits or restricts transfer of genomic data outside national borders. Federated Analysis; in-country data processing; use of certified cloud providers.
FAIR Principle (Accessible) Data should be retrievable by their identifier using a standardized protocol. Direct, open access may conflict with access controls required by GDPR/sovereignty. Tiered-access systems; automated Data Access Committees (DACs).
Informed Consent Participants must understand data use scope. Legacy consents may not permit broad sharing for future outbreaks. Dynamic consent platforms; broad consent frameworks within ethical review.
Purpose Limitation Data used only for specified, explicit purposes. Hinders secondary use of data for research on a novel, emergent pathogen. Consent language anticipating public health research; robust governance for repurposing.

Table 2: Quantitative Summary of Data Sharing Delays in Recent Outbreaks

Pathogen/Outbreak Estimated Avg. Delay (Sample to Public Database) Primary Cited Reasons for Delay (Beyond Technical) % of Sequences with Restricted Access (e.g., DAC)
SARS-CoV-2 (Early 2020) 0-14 days Urgency overrode typical constraints; some sovereignty concerns later emerged. <5%
MPXV (2022) 21-60 days Ethics approvals, novel context of outbreak, data sovereignty considerations. ~15-20%
Highly Pathogen Avian Influenza (HPAI) H5N1 30-180+ days Sovereign concerns over sharing virus genetics from animal/human cases; trade implications. >50%
Lassa Fever (Endemic) 6-24 months Complex consent, infrastructure limitations, sovereignty, and academic competition. ~30%

Protocols for Ethically and Legally Compliant Data Sharing

Protocol 2.1: Pre-Sharing Data Sanitization and Metadata Annotation Objective: To prepare raw sequencing data and associated metadata for sharing in a manner that minimizes privacy risks and maximizes reusability while documenting legal bases.

  • Data Pseudonymization:
    • Replace direct identifiers (e.g., patient ID, name) with a persistent, unique study code.
    • Use a trusted third party or secure algorithm to maintain a reversible key, stored separately under highest security.
    • For viral consensus sequences: Strip all human genomic reads. Verify using host read removal tools (e.g., Kraken2, minimap2 against human reference).
  • Metadata Preparation:
    • Compile essential epidemiological metadata using standardized ontologies (e.g., SNOMED-CT, LOINC for sample type; CIDO for infectious disease).
    • Critical Step: Create a "Data Passport" – a structured file (JSON format) documenting:
      • Legal basis for processing/sharing (GDPR Article 6 & 9 conditions).
      • Jurisdiction of origin.
      • Use restrictions (from consent).
      • Contact details of the Data Custodian.
  • File Packaging:
    • Package sequence files (FASTQ/FASTA), sanitized metadata, and the Data Passport.
    • Generate a unique, persistent dataset identifier (e.g., DOI, accession number).

Protocol 2.2: Implementing a Tiered-Access Data Release System Objective: To provide transparent, auditable, and timely data access that respects legal constraints.

  • Define Access Tiers:
    • Tier 1 (Open): Fully anonymized/pseudonymized viral consensus sequences with core non-sensitive metadata (e.g., location, date, specimen type). Available immediately via public repositories (INSDC, GISAID).
    • Tier 2 (Registered): Data with broader metadata (e.g., patient age group, outcome) or raw sequencing reads. Requires user registration and acceptance of a standard Data Use Agreement (DUA) prohibiting re-identification.
    • Tier 3 (Controlled): Data involving potential residual re-identification risk or from jurisdictions with strict sovereignty rules. Access requires project-specific approval by a Data Access Committee (DAC).
  • Automated DAC Workflow Implementation:
    • Use a platform (e.g., DUOS, GA4GH Passport standard) to manage applications.
    • DAC reviews applications against predefined criteria (scientific validity, consent alignment).
    • Approved applications trigger automated provisioning of access credentials to a secure data workspace (e.g., Terra, Seven Bridges).

Diagrams

G start Outbreak Sample Collection seq Sequencing & Primary Analysis start->seq decision Data Sanitization & Compliance Check seq->decision tier1 Tier 1 (Open) Public Repository decision->tier1 Low Risk Anonymized tier2 Tier 2 (Registered) DUA Required decision->tier2 Medium Risk Pseudonymized tier3 Tier 3 (Controlled) DAC Review decision->tier3 High Risk or Sovereign research FAIR Data Utilized for Research & Response tier1->research tier2->research tier3->research Post-Approval

Tiered Access Protocol for Outbreak Genomic Data

G cluster_legal Legal & Ethical Inputs cluster_fair FAIR Data Protocol Engine gdpr GDPR (Art. 6, 9) find Findable (PIDs, Metadata) access Accessible (Tiered System) gdpr->access sov Data Sovereignty sov->access consent Informed Consent reuse Reusable (Data Passport, License) consent->reuse find->access inter Interoperable (Ontologies, Formats) access->inter inter->reuse output Balanced Output: Rapid, Responsible, & Compliant Data reuse->output

Balancing Legal Constraints with FAIR Protocol Engine

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Compliant Outbreak Data Management

Tool/Category Example(s) Function in Ethical/Legal FAIR Sharing
Metadata Standardization MIxS (Minimum Information about any Sequence), GA4GH Metadata Schemas Ensures interoperability and completeness of data, crucial for defining what can be shared under given consent.
Pseudonymization Engine CRUSH, pseudoGA4GH, custom scripts with secure hashing (SHA-256) Reversibly replaces direct identifiers, a key step for GDPR-compliant processing and sharing.
Data Access Governance Platform GA4GH DUO, Data Use Ontology (DUO); DAM; DUOS Standardizes machine-readable data use restrictions, automating access tier assignment and DAC review.
Federated Analysis Framework SARS-CoV-2 SPHERES, GA4GH WES, DRS & TRS APIs; Beacon v2 Allows analysis across sovereign datasets without moving raw data, addressing data sovereignty concerns.
Secure Data Workspace Terra, Seven Bridges, DNAnexus Provides a cloud-based environment where Tier 2/3 data can be analyzed under compliant compute governance.
Consent Management Tool REDCap with dynamic consent modules, PEACH Enables management of participant preferences and tracking of consent scope for secondary use.
Data Transfer Agreement (DTA) Template Model Clauses, GA4GH DTA Pre-negotiated legal contract templates that accelerate secure data transfers between institutions.

Managing Computational and Storage Demands for Large-Scale Sequence Data

Within the framework of FAIR (Findable, Accessible, Interoperable, Reusable) data protocols for outbreak sequencing research, managing the associated computational and storage infrastructure is a critical bottleneck. The scale of data generated by high-throughput sequencing (HTS) platforms during pathogen surveillance necessitates specialized strategies to ensure data integrity, accessibility, and analytical reproducibility while controlling costs.

Quantitative Landscape of Sequencing Data

The following table summarizes the current data output from major sequencing platforms relevant to outbreak genomics (e.g., viral and bacterial sequencing).

Table 1: Data Output and Storage Requirements for Common Sequencing Platforms

Platform (Model Example) Typical Output per Run (Gb) Estimated FASTQ Size per Sample* (Gb) Approx. Storage for 1000 Samples (Tb)
Illumina (NextSeq 2000) 80-360 Gb 1.5 - 3.5 1.5 - 3.5
Oxford Nanopore (PromethION 48) 100-200 Gb 4 - 10 (FAST5) 4 - 10
PacBio (Revio) 120-360 Gb 3 - 8 (HiFi reads) 3 - 8
MGI (DNBSEQ-T20) 12,000-18,000 Gb 20 - 30 20 - 30

Size varies by coverage and genome size. Examples assume ~100x coverage for a ~5 Mb bacterial genome or a ~30 kb viral genome pooled in multiplex. Nanopore FAST5 includes raw signal data. *Uncompressed FASTQ storage. Aligned BAM/CRAM files and analyzed data will add overhead.

Application Notes & Protocols

Protocol: Implementing a Tiered Storage Architecture for FAIR Outbreak Data

Objective: To create a cost-effective, accessible storage system that aligns with the data lifecycle and FAIR principles.

Materials:

  • Primary Storage System: High-performance network-attached storage (NAS) or parallel file system (e.g., Lustre, BeeGFS).
  • Secondary Storage System: Object storage (e.g., AWS S3, Google Cloud Storage, MinIO).
  • Archival Storage: Tape library or cold cloud storage (e.g., AWS Glacier, Google Coldline).
  • Data Management Software: iRODS, DKIST Data Center, or custom scripts with a metadata catalog.

Methodology:

  • Data Ingestion & Hot Tier: Raw sequencing data (FASTQ) is written directly to primary storage. Immediate QC (FastQC), de-multiplexing (bcl2fastq, guppy), and initial assembly/minION mapping are performed here.
  • Active Analysis & Warm Tier: Processed data (aligned BAM/CRAM, variant calls, consensus genomes) are moved to object storage after primary analysis. This tier supports scalable, programmatic access for downstream phylogenetic and epidemiological analysis.
  • Metadata Cataloging: Upon movement to object storage, a standardized metadata file (in ISA-TAB or RO-Crate format) is generated and linked in a searchable catalog. This is critical for Findability and Interoperability.
  • Archival & Cold Tier: After publication or project conclusion, raw data and key analysis outputs are transferred to archival storage. A persistent identifier (DOI) is issued, and the metadata catalog is updated with the archival location, ensuring long-term Accessibility and Reusability.
Protocol: Cloud-Native Pandemic-Scale Phylogenetic Analysis

Objective: To execute a scalable, reproducible phylogenomic pipeline for thousands of pathogen genomes using cloud infrastructure.

Materials:

  • Workflow Manager: Nextflow or Snakemake.
  • Containerization: Docker or Singularity containers for all tools.
  • Cloud Provider: AWS, GCP, or Azure account with batch compute services (e.g., AWS Batch, Google Batch).
  • Reference Data: Curated reference genome and annotation (GFF).

Methodology:

  • Workflow Definition: Write a pipeline (e.g., using nextflow.config) that defines processes for: read trimming (Fastp), mapping (BWA-minimap2), variant calling (BCFtools), consensus generation (IVar), and phylogenetic inference (IQ-TREE, UShER).
  • Containerization: Package each software dependency into a container. Store containers in a public (Docker Hub) or private registry.
  • Cloud Deployment:
    • Upload input FASTQs and references to object storage.
    • Configure the workflow to launch spot/preemptible cloud instances for each processing step.
    • Specify that intermediate files are written to and read from object storage.
  • Execution & Monitoring: Launch the pipeline. The workflow manager will dynamically provision cloud instances, execute steps, and handle failures. Monitor via the cloud console or Nextflow Tower.
  • Output and Caching: Final outputs (trees, alignments, reports) are written to a designated object storage bucket. The pipeline cache can be stored for rapid re-analysis of new data.

Visualizations

G node_hot node_hot node_warm node_warm node_cold node_cold node_action node_action node_data node_data RawFASTQ Raw Data (FASTQ/FAST5) Tier1 Tier 1: Primary (High-Performance NAS) RawFASTQ->Tier1 Ingestion AlignedBAM Aligned Data (BAM/CRAM) Tier2 Tier 2: Object Store (Cloud/S3 Storage) AlignedBAM->Tier2 Store Variants Analysis Outputs (VCF, Trees) Publish Project Publication Variants->Publish ArchivedData Archived Dataset + DOI Tier3 Tier 3: Archival (Tape/Cold Cloud) ArchivedData->Tier3 Archive Analysis Active Analysis Tier1->Analysis Access Tier2->Variants Catalog FAIR Metadata Catalog Tier2->Catalog Register Tier3->Catalog Register Analysis->AlignedBAM Publish->ArchivedData Catalog->Tier2 Discover

Title: FAIR Data Lifecycle & Tiered Storage Architecture

G cluster_cloud Cloud Execution Environment node_container node_container node_input node_input node_process node_process node_output node_output node_cloud node_cloud InputData Input FASTQs Trimming Trim & QC (Fastp) InputData->Trimming CloudStorage Object Storage (e.g., AWS S3) RefGenome Reference Genome Alignment Map Reads (BWA/minimap2) RefGenome->Alignment Trimming->Alignment BatchCompute Orchestrated Batch Compute (Nextflow) BAM Aligned BAM Files Alignment->BAM VariantCall Call Variants (BCFtools) VCF Variant Calls (VCF) VariantCall->VCF Consensus Generate Consensus ConsensusSeq Consensus Sequences Consensus->ConsensusSeq Phylogeny Build Tree (IQ-TREE) Tree Phylogenetic Tree Phylogeny->Tree BAM->VariantCall VCF->Consensus ConsensusSeq->Phylogeny

Title: Scalable Cloud Phylogenomics Pipeline Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Computational Tools & Resources for Managing Sequence Data

Item Function & Relevance to FAIR Outbreak Research
iRODS (Integrated Rule-Oriented Data System) Open-source data management software that enforces FAIR policies through automated rules for data movement, metadata attachment, and access control.
Nextflow/Tower Workflow manager enabling reproducible, portable pipelines across compute environments. Tower provides monitoring, essential for large-scale collaborative projects.
Singularity/Apptainer Containerization platform designed for HPC and scientific software, ensuring consistent analysis environments and reproducibility of results.
RO-Crate A method for packaging research data with their metadata in a machine-readable format, crucial for creating Interoperable and Reusable dataset descriptions.
SPAdes/Megahit Genome assemblers for bacterial/viral Illumina data. Standardized assembly protocols are needed for comparable genomic epidemiology.
MinIO Open-source, S3-compatible object storage software. Allows deployment of cost-effective, scalable private cloud storage for sensitive outbreak data.
UShER Extremely efficient tool for placing new SARS-CoV-2 sequences into a global phylogenetic tree. Demonstrates need for specialized, scalable algorithms.
SnpEff/Snpeff Variant annotation tool. Annotated, standardized VCF files are essential for biologically meaningful data interpretation and sharing.

Ensuring Interoperability Across Different Sequencing Platforms and Assays

1. Introduction Within the framework of FAIR (Findable, Accessible, Interoperable, Reusable) data principles for outbreak sequencing, achieving technical interoperability is a foundational challenge. Data from disparate sequencing platforms (e.g., Illumina, Oxford Nanopore, Pacific Biosciences) and assay types (amplicon, metagenomic, hybrid capture) must be comparable and integrable. This document outlines application notes and detailed protocols to establish interoperability, ensuring that data from heterogeneous sources can be reliably combined for robust genomic epidemiology and pathogen surveillance.

2. Application Notes: Quantitative Comparison of Platform Performance The following tables summarize key performance metrics for common platforms using a standardized reference sample (e.g., SARS-CoV-2 or E. coli genome). Data is synthesized from current benchmarking studies.

Table 1: Key Performance Metrics by Sequencing Platform

Platform (Assay) Read Type Avg. Read Length Raw Accuracy (%) Yield per Run (Gb) Cost per Gb* Optimal Application for Outbreaks
Illumina (Metagenomic) Short, Paired-end 2x150 bp >99.9% 60-300 $15-$30 High-resolution variant calling, deep population sequencing
Illumina (Amplicon) Short, Paired-end 2x150 bp >99.9% 10-50 $50-$100 Targeted variant detection, low viral load samples
ONT (Amplicon) Long, Single 1-5 kb 95-99% (Q20+) 5-20 $100-$500 Rapid deployment, large structural variant detection, haplotype phasing
PacBio (HiFi) Long, Circular 10-25 kb >99.9% (Q30+) 10-50 $500-$1000 De novo assembly of novel pathogens, resolving complex regions

Note: Cost estimates are approximate and for comparative purposes; they vary by region and scale.

Table 2: Interoperability Metrics for a Shared Reference Sample (SARS-CoV-2)

Metric Illumina (Metagenomic) ONT (Amplicon) Concordance (%) Critical Parameter for Interoperability
Genome Coverage (>20x) 99.98% 99.95% 99.96 Library prep uniformity
SNP Identification 42 41 97.6 Bioinformatics pipeline (variant caller)
Indel Identification 5 6 83.3 Read alignment algorithm & gap penalties
Mean Coverage Depth 2,450x 1,850x N/A Normalization required for combined analysis

3. Experimental Protocols

3.1. Protocol: Cross-Platform Validation Using a Reference Material Objective: To validate the performance and interoperability of multiple sequencing platforms using a commercially available, well-characterized reference standard.

Materials:

  • ATCC/ERCC or NIST genomic reference standards (e.g., SARS-CoV-2 or microbial mix).
  • Platform-specific library prep kits (Illumina DNA Prep, ONT Ligation Sequencing Kit).
  • QC instruments: Qubit, Fragment Analyzer, or TapeStation.
  • Sequencing platforms: e.g., Illumina MiSeq/NextSeq, ONT MinION.
  • Bioinformatics servers with standardized pipeline.

Procedure:

  • Sample Aliquoting: Divide the reference material into 10+ identical aliquots. Store at recommended temperature.
  • Parallel Library Preparation: On the same day, prepare sequencing libraries from the same aliquot batch using platform-specific protocols. Perform all QC steps (concentration, fragment size).
  • Sequencing: Run libraries on respective platforms. Target a minimum of 50x mean coverage for the genome of interest.
  • Primary Data Processing: For each platform, generate FASTQ files. Do not apply aggressive quality filtering initially.
  • Standardized Bioinformatic Analysis: Process all FASTQ files through a single, containerized pipeline (e.g., Nextflow/Snakemake) featuring:
    • Adapter Trimming: Use cutadapt with identical stringency parameters.
    • Alignment: Map reads to the same reference genome using bwa-mem (Illumina) and minimap2 (ONT). Use the same reference genome version.
    • Variant Calling: Call variants using a common, platform-agnostic tool (medaka for ONT, GATK for Illumina) followed by normalization with bcftools norm. Apply a joint call set from all platforms using BCFtools call -m.
    • Analysis: Generate coverage depth files and consensus sequences for comparison.
  • Concordance Analysis: Compare variant call sets (SNPs, Indels) and consensus sequences across platforms using diff and snpeff. Calculate percent concordance for key metrics.

3.2. Protocol: Implementing a FAIR-Compliant Metadata Schema Objective: To ensure data interoperability through standardized metadata collection at the point of sequencing.

Procedure:

  • Adopt a Metadata Standard: Use the Investigations-Studies-Assays (ISA) framework or the NCBI BioSample attribute packages tailored for pathogen surveillance.
  • Create a Sample Submission Sheet: Mandate fields including:
    • Sample: collectiondate, host, geographiclocation, isolate.
    • Library Prep: assaytype (amplicon, metagenomic), platform, libraryprepkit, primersetversion.
    • Sequencing: sequencinginstrument, flowcelltype, runid.
    • Analysis: referencegenomeaccession, bioinformaticspipelineversion.
  • Automated Metadata Capture: Use LIMS systems to auto-populate fields from instrument outputs where possible.
  • Validation: Use JSON schema validators to ensure completeness and format compliance before data deposition in public repositories (ENA, SRA, GISAID).

4. Visualizations

G cluster_workflow Cross-Platform Interoperability Workflow Start Shared Reference Material Lib1 Illumina Library Prep Start->Lib1 Lib2 Nanopore Library Prep Start->Lib2 Seq1 Sequencing (Illumina) Lib1->Seq1 Seq2 Sequencing (Nanopore) Lib2->Seq2 FASTQ1 FASTQ Files Seq1->FASTQ1 FASTQ2 FASTQ Files Seq2->FASTQ2 Pipeline Standardized Bioinformatics Pipeline FASTQ1->Pipeline FASTQ2->Pipeline VCF Standardized Variant Call File (VCF) Pipeline->VCF Consensus Consensus Sequences Pipeline->Consensus Analysis Joint Analysis & Concordance Metrics VCF->Analysis Consensus->Analysis

Diagram 1: Cross-platform interoperability workflow.

G cluster_path FAIR Data Interoperability Pathway R1 Raw Data (e.g., .bcl, .fast5) I1 Step 1: File Conversion & Demux R1->I1 Platform Specific R2 Standardized FASTQ & Metadata I2 Step 2: Metadata Annotation R2->I2 R3 Processed Data (.bam, .vcf) R4 FAIR-Ready Outputs (Consensus, Lineage) R3->R4 I4 Step 4: Deposition to Public Repository R4->I4 R5 Integrated Outbreak Analysis I1->R2 I3 Step 3: Containerized Analysis Pipeline I2->I3 Standardized I3->R3 I4->R5 Global Integration

Diagram 2: FAIR data interoperability pathway.

5. The Scientist's Toolkit: Key Research Reagent Solutions

Item Function in Interoperability Studies
Certified Reference Materials (ATCC, NIST) Provides a ground-truth genomic standard for cross-platform performance benchmarking and pipeline validation.
Universal RNA/DNA Extraction Kits (e.g., QIAamp Viral RNA Mini) Ensures uniform input material quality across different labs and platforms, reducing pre-analytical variability.
Multiplexed PCR Primer Pools (e.g., ARTIC Network V4.1) Standardizes the target enrichment step for amplicon-based sequencing, enabling direct comparison of data generated by different groups.
PhiX Control Library (Illumina) / Lambda Control DNA (ONT) Used as a run-specific internal control to monitor sequencing performance and inter-run variability.
Bioinformatics Software Containers (Docker/Singularity) Packages analysis pipelines with all dependencies, guaranteeing reproducible results across different computing environments.
Metadata Validation Tools (e.g., ISA model validators) Ensures compliance with FAIR-aligned metadata standards, making data findable and interoperable upon publication.

Benchmarking Success: Metrics and Frameworks for FAIR Compliance in Genomics

Within the broader thesis on establishing robust FAIR data protocols for outbreak sequencing research, the quantitative assessment of FAIRness is a critical step. To move from principles to practice, researchers must employ standardized evaluation tools. This protocol details the application of two prominent, community-adopted tools: the FAIR Evaluator and F-UJI. Their systematic use provides reproducible, quantitative metrics essential for benchmarking data repositories, improving data management plans, and ensuring genomic sequence data from outbreaks is truly Findable, Accessible, Interoperable, and Reusable.

Table 1: Core Features of FAIRness Evaluation Tools

Feature FAIR Evaluator F-UJI (FAIRsFAIR)
Primary Developer GO FAIR Initiative FAIRsFAIR Project
Architecture Community-deployed service; test suite defined by FAIR Metrics. Standalone web service & Python package.
Core Methodology Executes community-defined FAIR Metrics (M1-F4) as discrete tests. Automated assessment based on the FAIRsFAIR Data Object Assessment Metrics.
Input URL of a digital resource (Data, Software, etc.). URL of a data object or PID (e.g., DOI).
Output Score per metric, overall maturity, and structured report (JSON-LD). Score per metric, overall score, and structured report (JSON).
Strengths Strong community governance of metrics; flexible deployment. Fully automated; extensive metadata extraction; rich integrated data.
Typical Use Case Protocol compliance checking, repository certification. Automated large-scale assessment, researcher self-assessment.

Experimental Protocols

Protocol 3.1: FAIRness Evaluation using the FAIR Evaluator

Objective: To quantitatively assess a genomic dataset (e.g., a SARS-CoV-2 sequence submission in ENA) against the community-defined FAIR Metrics.

Materials:

  • FAIR Evaluator instance (e.g., https://fair-evaluator.semanticscience.org)
  • Persistent URL (e.g., DOI or accession-based URL) for the target data resource.
  • Web browser or API client (e.g., cURL, Postman).

Procedure:

  • Access: Navigate to the FAIR Evaluator web interface.
  • Configure: Select the relevant "Collection" of metrics (e.g., "Gen2" for latest metrics). For outbreak data, the "Essential" metrics are a recommended starting set.
  • Submit: Enter the full, resolvable URL of the digital object (e.g., https://www.ebi.ac.uk/ena/browser/view/SAMEA12345678) into the "Target" field.
  • Execute: Initiate the evaluation. The tool will sequentially execute each metric test.
  • Analyze:
    • Review the summary dashboard showing pass/fail/skip status for each metric (F1-A1.2, R1.3, etc.).
    • Click on individual metrics to view detailed logs, including the test rationale, evidence found, and failure reasons.
    • Export the full result as a machine-readable JSON-LD report for permanent record.

Protocol 3.2: Automated Assessment with the F-UJI Tool

Objective: To perform an automated, comprehensive FAIR assessment of a published data object using the F-UJI API.

Materials:

  • F-UJI web service (https://www.f-uji.net) or the fuji Python package (pip install fuji-fair).
  • Persistent Identifier (PID) like a DataCite DOI for a dataset.
  • Command-line terminal or Python scripting environment.

Procedure (API Command-Line):

Procedure (Python Package):

  • Analyze: Inspect the structured JSON output. Key sections (findable, accessible, interoperable, reusable) contain scores (0-100%) and evidence list. Use the overall score for benchmarking and drill into failed criteria for remediation.

Visualized Workflows

fair_evaluation_workflow Start Outbreak Sequencing Dataset A Assign Persistent Identifier (DOI) Start->A B Deposit in Public Repository (e.g., ENA, SRA) A->B C Resource URL/PID B->C D FAIR Evaluator (Metric Tests) C->D E F-UJI Tool (Automated Harvest) C->E F Quantitative FAIR Metrics Report D->F E->F G Thesis: Protocol Refinement & Compliance F->G

  • Title: FAIR Assessment Protocol for Outbreak Data

tool_logic cluster_fair_eval FAIR Evaluator Logic cluster_fuji F-UJI Tool Logic FE_Input Resource URL FE_Test Metric Test Engine (Web-focused) FE_Input->FE_Test FE_Metrics Community Metrics (M1-F4, R1...) FE_Metrics->FE_Test FE_Output Maturity Score per Metric FE_Test->FE_Output Combined Comparative Analysis & Protocol Validation FE_Output->Combined F_Input PID (DOI) F_Harvest Metadata Harvest from Multiple Sources F_Input->F_Harvest F_Check Core FAIR Checker (19 Metrics) F_Harvest->F_Check F_Output Overall % Score & Evidence F_Check->F_Output F_Output->Combined

  • Title: Internal Logic of FAIR Evaluator vs. F-UJI

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for Quantitative FAIR Assessment

Item Function in Protocol Example/Provider
FAIR Evaluator Instance Executes community-governed FAIR metric tests. GO FAIR (https://fair-evaluator.semanticscience.org) or institutional deployment.
F-UJI API / Python Package Provides automated, standardized FAIR scoring. FAIRsFAIR (https://www.f-uji.net; PyPI: fuji-fair).
Persistent Identifier (PID) Uniquely and permanently identifies the digital object for testing. DOI (DataCite, Crossref), accession-based URL (e.g., ENA, NCBI).
Metadata Schema Validator Ensures structural interoperability of metadata before assessment. JSON Schema validator, ENA metadata checklist, MIxS templates.
Programmatic Access Client Automates submission and retrieval of assessment reports. curl, requests (Python), or httr (R) packages.
FAIR Metrics Reference Defines the tests and their interpretation. FAIR Metrics (https://github.com/FAIRMetrics/Metrics) & FAIRsFAIR Metrics.
Controlled Vocabularies Provide interoperable terms for metadata fields (e.g., pathogen, host). EDAM Ontology, NCBI Taxonomy, Disease Ontology (DO).

Within the broader thesis on FAIR (Findable, Accessible, Interoperable, Reusable) data protocols for outbreak sequencing research, this document presents a comparative analysis of two paradigms in data dissemination. The urgency of an outbreak drives rapid research publication, but the underlying data sharing practices critically impact the global response's speed, efficacy, and reproducibility. Ad-hoc sharing, while common, often involves unstructured data supplements, non-standard formats, and incomplete metadata deposited in generic repositories or upon direct request. In contrast, FAIR-aligned sharing mandates deposition in recognized, subject-specific databases (e.g., INSDC members: GenBank, ENA, SRA) with rich, standardized contextual metadata, enabling immediate machine-actionability and secondary analysis.

Application Note 1.1: The transition from ad-hoc to FAIR sharing reduces the "data-to-discovery" latency. A FAIR-compliant genome sequence deposited with structured host, location, collection date, and sequencing protocol metadata can be instantly integrated into global phylogenetic tracking dashboards, whereas an ad-hoc shared file requires manual curation, consuming critical days during an outbreak.

Application Note 1.2: Interoperability is a core FAIR principle critical for cross-strain and cross-pathogen comparison. Using controlled vocabularies (e.g., NCBI BioSample attributes, GSC MIxS standards) ensures that data from studies of Mpox, Avian Influenza H5N1, and SARS-CoV-2 variants can be computationally compared for traits like zoonotic potential or immune escape mutations.

Comparative Analysis of Recent Outbreak Publications

A live search for recent (2023-2024) publications on high-consequence pathogens reveals distinct patterns in data sharing practices. The quantitative summary below contrasts two representative publication approaches.

Table 1: Comparison of Data Sharing Practices in Selected Recent Outbreak Studies

Aspect FAIR-Aligned Publication (Example: Mpox Clade Ib) Ad-Hoc Publication (Example: H5N1 Clade 2.3.4.4b)
Repository INSDC (SRA, ENA), Virus Pathogen Resource (ViPR) Generic institutional repository; Supplemental Data ZIP file
Accession Provided Yes (BioProject, BioSample, SRA, GenBank) No stable accessions; file names only
Metadata Richness High (MIxS-compliant: host health status, geo-location, collection date/time) Low (basic: host species, country, year)
File Format Standardized (FASTQ, consensus FASTA with QUAL) Mixed (FASTQ, .xlsx with mutations, PDF alignments)
License for Reuse Clear (CC0 / Public Domain Dedication) Unspecified or restrictive
Time from Submission to Public Availability Immediate upon publication (preprint synchronized) Upon request via email, or indefinite
Machine-Actionability High (API queryable, bulk downloadable) Low (manual interrogation required)

Experimental Protocols for Outbreak Sequencing & Data Submission

Protocol 3.1: Standardized Workflow for Pathogen Genome Sequencing in an Outbreak Setting

  • Objective: To generate high-quality consensus genomes from clinical specimens with accompanying contextual metadata for FAIR sharing.
  • Materials: See "The Scientist's Toolkit" (Section 5.0).
  • Procedure:
    • Nucleic Acid Extraction: Isolve pathogen RNA/DNA from primary clinical samples (swab, tissue) using a bead-based or column-based extraction kit. Include positive and negative extraction controls.
    • Library Preparation: For RNA viruses, perform reverse transcription. Use a tiled multiplex PCR approach (e.g., Artic protocol) or metagenomic shotgun approach for library construction. Amplify with limited cycles.
    • Sequencing: Perform high-throughput sequencing on an Illumina MiSeq/NextSeq or Oxford Nanopore Technologies MinION platform. Target minimum 1000X average coverage.
    • Bioinformatic Analysis: a. Demultiplexing: Assign reads to samples based on dual index barcodes. b. Adapter Trimming & QC: Use fastp or Trimmomatic. c. Variant Calling & Consensus Generation: Map reads to a reference genome (BWA, Minimap2). Call variants (iVar, BCFtools). Generate a majority-rule consensus sequence (≥10X depth, ≥75% frequency). d. Phylogenetic Analysis: Align consensus sequences (MAFFT), build tree (IQ-TREE), and temporal analysis (BEAST).

Protocol 3.2: FAIR-Compliant Data Submission to Public Repositories

  • Objective: To deposit raw sequence data, consensus genomes, and associated metadata in accordance with FAIR principles.
  • Procedure:
    • Metadata Curation: Compile metadata for each sample using the GenCore checklist template. Essential fields include: isolate, host, collection date, geographic location (lat/lon), specimen type, and sequencing instrument.
    • BioProject Registration: Create a new BioProject on the NCBI Submission Portal, describing the overarching study goals.
    • BioSample Submission: Upload the metadata table to create unique BioSample accessions for each biological source.
    • Sequence Read Archive (SRA) Submission: Link raw FASTQ files to their respective BioSamples and provide library construction details (kit, strategy).
    • GenBank Submission: Submit consensus FASTA sequences, linking them to BioSamples. Annotate the genome features (genes, CDS) using a standard template.

Visualizations

workflow Specimen Clinical Specimen (Swab, Tissue) Extraction Nucleic Acid Extraction Specimen->Extraction Library Library Preparation Extraction->Library Sequencing High-Throughput Sequencing Library->Sequencing FASTQ Raw Reads (FASTQ) Sequencing->FASTQ Analysis Bioinformatic Analysis (Alignment, Variant Calling) FASTQ->Analysis Consensus Consensus Genome (FASTA) Analysis->Consensus Submission FAIR Submission (BioProject, BioSample, SRA, GenBank) Consensus->Submission Metadata Structured Metadata (Controlled Vocabularies) Metadata->Submission PublicDB Public Database (GenBank, ENA, GISAID) Submission->PublicDB

FAIR-Compliant Outbreak Sequencing & Submission Workflow

comparison cluster_fair FAIR Data Sharing cluster_adhoc Ad-Hoc Data Sharing F1 Structured Metadata F2 Standardized Formats F1->F2 F3 Public Repository (INSDC) F2->F3 F4 Clear License (CC0) F3->F4 OutcomeF Outcome: Machine-Actionable, Globally Integratable, Reproducible F4->OutcomeF A1 Minimal/Unstructured Metadata A2 Mixed Formats (.xlsx, PDF, FASTQ) A1->A2 A3 Supplement or Email-on-Request A2->A3 A4 Unspecified License A3->A4 OutcomeA Outcome: Manual Curation Needed, Limited Discoverability, Barrier to Reuse A4->OutcomeA

FAIR vs. Ad-Hoc Data Sharing Pathways and Outcomes

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for Outbreak Sequencing Research

Item Function Example Product/Kit
Nucleic Acid Extraction Kit Isolates high-quality viral RNA/DNA from complex clinical matrices, crucial for sensitive detection. QIAamp Viral RNA Mini Kit, MagMAX Viral/Pathogen Nucleic Acid Isolation Kit
Reverse Transcription Master Mix Converts viral RNA into complementary DNA (cDNA) for subsequent amplification in RNA virus workflows. SuperScript IV Reverse Transcriptase, LunaScript RT Master Mix
Tiled Multiplex PCR Primer Pools Amplifies the entire viral genome in overlapping fragments from low-input material, enabling sequencing from low Ct samples. Artic Network primer sets (e.g., for SARS-CoV-2, Mpox), Midnight primers
High-Fidelity DNA Polymerase Performs amplification with minimal error rates, preserving true sequence variation over PCR artifacts. Q5 Hot Start High-Fidelity Master Mix, PrimeSTAR GXL DNA Polymerase
Sequencing Library Prep Kit Prepares amplified DNA for sequencing by adding platform-specific adapters and indexes for sample multiplexing. Illumina DNA Prep, Oxford Nanopore Ligation Sequencing Kit
Bioinformatics Pipeline Software A suite of tools for transforming raw sequencing data into consensus genomes and phylogenetic trees. artic pipeline, IVar, Nextclade, Galaxy Platform

Application Note: FAIR Data in Pathogen Genomics for Countermeasure Development

The implementation of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles within outbreak sequencing research pipelines directly reduces the time-to-discovery for diagnostics, therapeutics, and vaccines. This acceleration is quantified through comparative metrics in key development phases.

Quantitative Impact of FAIR Data Compliance

Table 1: Comparative Timeline Metrics for Countermeasure Development with and without FAIR Data Practices

Development Phase Traditional Timeline (Weeks) FAIR-Optimized Timeline (Weeks) Time Savings (%) Key FAIR Enabler
Pathogen Identification & Sequencing 2-4 1-2 50% Findable, shared raw reads in repositories (SRA)
Genomic Analysis & Variant Calling 3-5 1-2 60-67% Interoperable workflows (Nextflow, WDL) & standardized ontologies
Target Identification (e.g., Spike protein) 4-8 2-3 50-63% Accessible, annotated genomes in public databases (GISAID, INSDC)
Diagnostic Assay Design (Primer/Probe) 2-3 0.5-1 67-75% Reusable, machine-readable sequence metadata
Pre-clinical Candidate Evaluation 10-16 6-10 40% Integrated, interoperable datasets linking genotype to phenotype
Clinical Trial Cohort Genomic Analysis 6-10 3-5 50% Federated, privacy-preserving querying (Beacon API)

Table 2: Data Interoperability Impact on Therapeutic Antibody Discovery

Metric Low FAIR Compliance (Pre-2020 typical) High FAIR Compliance (Post-2020 exemplar) Improvement Factor
Sequences retrieved for homology modeling ~100s (manual curation) >100,000 (automated query) >1000x
Time to assemble global variant dataset 3-6 months 24-48 hours ~50x
Cross-study epitope conservation analysis feasibility Limited, single study Comprehensive, pan-variant N/A (new capability)
Machine learning model accuracy (R²) for binding affinity 0.4-0.6 0.75-0.85 ~1.5x

Detailed Protocols

Protocol 1: FAIR-Compliant Pathogen Sequencing and Metadata Submission for Accelerated Diagnostic Design

Objective: To generate and share pathogen sequence data with FAIR-compliant metadata, enabling rapid, global diagnostic assay design.

Materials & Reagents:

  • Clinical specimen (e.g., nasopharyngeal swab in VTM)
  • Viral RNA extraction kit (e.g., QIAamp Viral RNA Mini Kit)
  • ARTIC Network nCoV-2019 primer pool (or relevant panel for other pathogens)
  • OneStep RT-PCR reagents
  • Illumina DNA Prep or Oxford Nanopore Ligation Sequencing Kit
  • Positive control RNA
  • Nuclease-free water

Procedure:

  • RNA Extraction: a. Extract viral RNA from 140 µL of clinical specimen using the viral RNA mini kit, following manufacturer instructions. Elute in 60 µL of AVE buffer. b. Include an extraction positive control (known titer of inactivated virus) and a negative control (nuclease-free water).

  • Library Preparation (Illumina Example): a. Perform reverse transcription and multiplex PCR amplification using the ARTIC primer pool V4.1 and the OneStep RT-PCR kit. b. Clean amplicons using a 1X bead-based clean-up (AMPure XP). c. Quantify DNA using a fluorometric method (Qubit). d. Proceed with tagmentation, index PCR, and final clean-up using the Illumina DNA Prep kit.

  • Sequencing: a. Dilute the final library to 4 nM. b. Denature with 0.2 N NaOH and dilute to 20 pM in hybridization buffer. c. Load onto an Illumina MiSeq (500-cycle kit) or NextSeq 2000 (300-cycle kit).

  • FAIR Data Processing & Submission (Critical Step): a. Basecalling & Demultiplexing: Use bcl2fastq or DRAGEN to generate FASTQ files. b. Quality Control: Run FastQC and MultiQC. Trimming performed with fastp. c. Variant Calling: Map reads to reference genome (e.g., NC_045512.2) using BWA-MEM. Call variants with iVar. d. Consensus Generation: Generate consensus sequence using iVar consensus command (minimum depth: 10x, minimum frequency: 0.75). e. Metadata Annotation: i. Populate the INSDC / GISAID metadata spreadsheet with all mandatory fields: sample collection date, location (geocoordinates), host, sampling strategy, specimen source. ii. Use controlled vocabularies (e.g., NCBI Taxonomy ID 2697049 for SARS-CoV-2, UBERON for anatomical terms). f. Submission: i. Submit raw reads (FASTQ) to the Sequence Read Archive (SRA) via the NCBI Submission Portal. Link to BioProject (e.g., PRJNA485481). ii. Submit the consensus sequence and annotated metadata to GISAID and/or GenBank. iii. Deposit the analysis workflow in a public, version-controlled repository (e.g., GitHub) using a standardized workflow language (Nextflow or WDL).

Protocol 2: Leveraging FAIR Repositories for Epitope Prediction and Vaccine Antigen Design

Objective: To computationally identify conserved T-cell and B-cell epitopes by querying FAIR-compliant sequence repositories, informing subunit vaccine design.

Materials & Computational Tools:

  • Public API access to GISAID EpiCoV or NCBI Virus.
  • IEDB (Immune Epitope Database) Analysis Resource tools (Consensus, NetMHCIIpan).
  • Reference proteome (UniProt: P0DTC2 for SARS-CoV-2 Spike).
  • Programming environment (Python 3.9+ with pandas, requests libraries).
  • Structural visualization software (PyMOL).

Procedure:

  • FAIR Data Retrieval: a. Programmatically query the GISAID API (with authorized credentials) or NCBI Virus Datasets to retrieve all available Spike protein sequences for a target variant over the last 6 months. b. Filter sequences for completeness (length > 1270 aa) and low ambiguity (<1% N). c. Download associated metadata (collection date, lineage, country).

  • Multiple Sequence Alignment (MSA) and Conservation Analysis: a. Perform MSA using MAFFT or Clustal Omega. b. Calculate per-residue conservation scores using Skylign or Jalview. c. Output a conservation profile (e.g., .csv file).

  • T-cell Epitope Prediction (CD8+): a. Extract 9-mer and 10-mer peptides from the reference Spike sequence using a sliding window. b. Submit peptide list to IEDB's NetCTLpan or NetMHCcons tool for common HLA alleles (e.g., A02:01, B07:02). c. Filter results for strong binders (percentile rank < 0.5).

  • B-cell Linear Epitope Prediction: a. Submit the reference Spike sequence to IEDB's BepiPred-2.0 and Ellipro. b. Cross-reference predicted epitopes with the conservation profile from Step 2.

  • Conserved Epitope Prioritization & FAIR Output: a. Generate a ranked list of epitopes that are: i) predicted strong binders, ii) located in conserved regions (>80% identity across retrieved sequences), and iii) surface-exposed (validate with PyMOL using PDB 6VYB). b. Publish the final list of candidate epitopes as a structured table in a machine-readable format (JSON-LD) on a data repository (e.g., Zenodo), linked to the DOIs of the source sequence datasets used in the analysis.

Diagrams

Title: FAIR Data Accelerates Medical Countermeasure Development

workflow Specimen Specimen Seq NGS Sequencing Specimen->Seq Protocol 1 Raw_Data Raw Reads (FASTQ) SRA SRA Repository Raw_Data->SRA Submit/Query QC_Assembly QC, Assembly, Variant Calling Raw_Data->QC_Assembly FAIR Processing Analysis Global Analysis (e.g., Epitope Prediction) SRA->Analysis FAIR Query Consensus Annotated Consensus GISAID GISAID/GenBank Consensus->GISAID Submit/Query GISAID->Analysis FAIR Query Countermeasure Diagnostic/Therapeutic/Vaccine Candidate Analysis->Countermeasure Seq->Raw_Data QC_Assembly->Consensus

Title: FAIR-Compliant Sequencing and Application Workflow

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Reagents & Tools for FAIR-Optimized Outbreak Research

Item Function in FAIR Context Example Product/Coding Standard
Standardized RNA Extraction Kit Ensures reproducible, high-quality input material for sequencing; critical for generating reusable data. QIAamp Viral RNA Mini Kit (Qiagen), MagMAX Viral/Pathogen Kit (Thermo)
Multiplex PCR Primer Panels Enables targeted sequencing of pathogen genomes from complex samples; panel version must be recorded in metadata for interoperability. ARTIC Network nCoV-2019 V4.1, Midnight primer panel
NGS Library Prep Kit Generates sequencing libraries; kit name and version are key provenance metadata. Illumina DNA Prep, Oxford Nanopore Ligation Sequencing Kit (SQK-LSK109)
Controlled Vocabulary (Ontology) Terms Enables semantic interoperability by tagging metadata with standardized terms. NCBI Taxonomy ID, UBERON, EDAM, Disease Ontology (DOID)
Workflow Language Script Encapsulates the analysis protocol in a machine-actionable, reusable format. Nextflow, WDL (Broad Institute), Snakemake pipeline
Structured Metadata Spreadsheet Template for capturing FAIR metadata fields required by repositories. GISAID EpiCoV submission template, INSDC SRA metadata template
Persistent Identifier (PID) Minting Service Assigns a globally unique, persistent identifier to a dataset, making it findable and citable. DOI (via Zenodo, Figshare), accession numbers (SRA, BioStudies)
Beacon API-Compatible Platform Enables federated, privacy-aware querying across genomic datasets without centralizing data. ELIXIR Beacon, COVID-19 Beacon Network

The Role of Trusted Digital Repositories and Core Certification

Application Notes

In the context of FAIR data protocols for outbreak sequencing research, Trusted Digital Repositories (TDRs) and formal certification mechanisms are critical for ensuring data integrity, provenance, and long-term reuse. For researchers and drug development professionals, this creates a reliable ecosystem for sharing sensitive genomic and epidemiological data.

Table 1: Comparison of Major Repository Certification/Assessment Frameworks

Framework Governing Body Core Focus Typical Applicants for Outbreak Data
CoreTrustSeal CoreTrustSeal Board Sustainable, trustworthy data management. Core certification. Institutional Repositories, Thematic Repositories (e.g., infectious disease databases).
ISO 16363 (Audit & Certification) ISO / External Auditors Comprehensive audit against the OAIS Reference Model and ISO standards. Large National/International Archives (e.g., ENA, SRA, GISAID).
NESTOR Seal German NESTOR Initiative Digital preservation standards, tailored for German institutions but internationally applicable. Research Libraries and University Archives curating outbreak collections.
ISO 27001 ISO / External Auditors Information Security Management Systems (ISMS). Any repository handling sensitive personal or pre-publication outbreak data.

Table 2: Quantitative Impact of Repository Certification on FAIR Compliance

FAIR Principle Enhancement via Core Certification Key Metric for Researchers
Findable Mandatory, persistent identifiers (PIDs) and rich metadata. >95% of datasets in certified repos use PIDs (e.g., DOI, ARK).
Accessible Defined access protocols, clarity on retention periods. Standardized machine interfaces (APIs) present in 100% of certified TDRs.
Interoperable Use of community-endorsed schemas (e.g., MIxS, INSDC). ~80% adoption of domain-specific metadata standards post-certification.
Reusable Detailed provenance and licensing requirements. Provenance tracking for data transformations and analyses is mandatory.

Experimental Protocols

Protocol 1: Depositing Outbreak Sequencing Data to a CoreTrustSeal-Certified Repository

Objective: To archive raw sequencing reads and associated sample metadata in a FAIR-compliant manner, ensuring readiness for secondary analysis and audit.

Materials: See "Scientist's Toolkit" below.

Methodology:

  • Pre-Deposit Preparation: a. Assemble all raw sequence files (FASTQ) from the outbreak cohort. b. Compile metadata using the MIxS (Minimum Information about any (x) Sequence) checklist, specifically the human-associated or wastewater package. Mandatory fields: sample collection date, location (geocoordinates), host, pathogen, sequencing instrument. c. Generate a README file describing experimental protocols, sequencing kit, and any data processing steps already performed.
  • Repository Selection & Validation: a. Consult the CoreTrustSeal website to identify a certified public repository for microbial genomics (e.g., European Nucleotide Archive - ENA, Sequence Read Archive - SRA). b. Verify the repository's specific submission format requirements (e.g., metadata spreadsheet template, file naming conventions).
  • Submission & Curation: a. Create a submission account and project space within the repository. b. Upload metadata spreadsheet first. Validate using the repository's online validator. c. Upload sequence files via Aspera or FTP protocol. Maintain folder structure. d. Link metadata records to each sequence file accurately. e. Submit the entire dataset for review by repository curators.
  • Post-Submission: a. Respond to curator queries promptly to resolve metadata ambiguities. b. Upon acceptance, the repository will assign persistent identifiers (e.g., Project Accession: PRJNAXXXXXX, Sample Accessions: SAMNYYYYYYY). c. Cite these accession numbers/PIDs in any subsequent publication.

Protocol 2: Performing a Data Integrity Audit on Archived Outbreak Data

Objective: To verify the integrity and authenticity of data retrieved from a TDR for re-analysis in a drug target discovery pipeline.

Materials: Downloaded sequence files, original and downloaded MD5/SHA-256 checksum files, computing environment.

Methodology:

  • Data Retrieval: a. From the certified repository, download the outbreak dataset of interest using provided accession numbers. b. Crucially, also download the associated checksum file (e.g., md5sum.txt) provided by the repository.
  • Integrity Verification: a. On the local system, generate checksums for the downloaded files. For MD5: md5sum [downloaded_file.fastq.gz] b. Compare the locally generated checksums with those listed in the repository's md5sum.txt file. Use: md5sum -c md5sum.txt c. A successful match for all files confirms no corruption occurred during transfer and that the data bitstream is identical to the archived original.
  • Provenance & Metadata Reconciliation: a. Parse the retrieved sample metadata (in XML or tabular format). b. Cross-reference key experimental parameters (e.g., sequencing platform, library strategy) with methods described in the related publication or repository notes to confirm consistency.

Mandatory Visualizations

DAG Outbreak Sequencing\nCore Lab Outbreak Sequencing Core Lab Local Data\nManagement System Local Data Management System Outbreak Sequencing\nCore Lab->Local Data\nManagement System Metadata Curation\n(MIxS Standards) Metadata Curation (MIxS Standards) Local Data\nManagement System->Metadata Curation\n(MIxS Standards) Raw Data\n(FASTQ, BAM) Raw Data (FASTQ, BAM) Local Data\nManagement System->Raw Data\n(FASTQ, BAM) Submission Package Submission Package Metadata Curation\n(MIxS Standards)->Submission Package Raw Data\n(FASTQ, BAM)->Submission Package Certified TDR\n(e.g., ENA, SRA) Certified TDR (e.g., ENA, SRA) Submission Package->Certified TDR\n(e.g., ENA, SRA) PID Assignment\n(Accession) PID Assignment (Accession) Certified TDR\n(e.g., ENA, SRA)->PID Assignment\n(Accession) Preservation Action\n(Checksums, Backup) Preservation Action (Checksums, Backup) Certified TDR\n(e.g., ENA, SRA)->Preservation Action\n(Checksums, Backup) FAIR Data Discovery FAIR Data Discovery PID Assignment\n(Accession)->FAIR Data Discovery Global Researcher\n(Downstream Analysis) Global Researcher (Downstream Analysis) FAIR Data Discovery->Global Researcher\n(Downstream Analysis) Therapeutic Target\nIdentification Therapeutic Target Identification Global Researcher\n(Downstream Analysis)->Therapeutic Target\nIdentification

Title: Data Pipeline from Outbreak Lab to Global Research via TDR

G Repo Repository Applicant CT CoreTrustSeal Standards Repo->CT Sub1 Organizational Infrastructure CT->Sub1 Sub2 Digital Object Management CT->Sub2 Sub3 Technology & Security CT->Sub3 OAIS OAIS Reference Model ISO ISO 16363 Metrics Sub1->OAIS Audit External Audit & Review Sub1->Audit Sub2->OAIS Sub2->Audit Sub3->ISO Sub3->Audit Cert Certification Seal Awarded Audit->Cert

Title: CoreTrustSeal Certification Process & Key Standards

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for TDR Submission & Audit

Item Function in TDR Context Example/Note
MIxS Checklists Standardized metadata framework to ensure interoperability and reuse. Required by INSDC repositories. Use 'host-associated' or 'wastewater' for outbreak sequences.
Checksum Tool (md5sum/sha256sum) Generates a unique digital fingerprint for files to verify integrity post-transfer. Critical for audit Protocol 2. Built into Linux/macOS; available for Windows (e.g., via CertUtil).
Metadata Validator Repository-provided tool to check metadata files for format and completeness errors before submission. ENA's Webin CLI, SRA's Metadata Validator. Prevents submission rejection.
Aspera Client High-speed transfer protocol for uploading large sequence files (FASTQ) to repositories. Preferred over FTP for large datasets. Often provided by the repository.
Sample ID Mapper Local tracking system (e.g., spreadsheet, LIMS) to maintain link between internal sample IDs and public accession numbers. Ensures provenance traceability back to original lab samples.

Conclusion

Implementing robust FAIR data protocols for outbreak sequencing is a cornerstone of effective pandemic preparedness and response. By moving from ad-hoc sharing to systematic, principled data management—encompassing rich metadata, standardized workflows, and secure, accessible repositories—the global research community can significantly enhance the utility and impact of genomic surveillance. The future of outbreak science hinges on this foundation, enabling rapid data fusion for AI-driven insights, fostering equitable collaboration, and ultimately shortening the critical path from pathogen detection to clinical countermeasure.