Omics schema
International and SPHN-aligned genomic and omics semantic mappings for structured, machine-readable clinical and research data.
The technology is here. The trusted handoff is missing.
Labs, hospitals and companies already have powerful genomic systems. What they lack is a shared way to pass genomic information between them with its meaning intact. This project builds that handoff, creating more value for research, healthcare and industry.
The project
The technology is here. The handoffs are not.
Genomics already supports precision medicine, healthcare, research, drug discovery and pharmaceutical R&D. But genomic information can still lose its meaning when it moves between organisations and systems.
We are bringing Swiss and UK partners together to build and prove the shared standards that make those handoffs work.
The problem
Information changes hands. Context gets lost.
Different systems still require repeated translation, integration and verification. World-class systems cannot communicate.
The project
Build the shared rules. Prove they work.
Swiss and UK partners will test common standards across independent organisations and real genomic workflows.
The principle
Standardise the handoff, not the algorithm.
Labs, hospitals, researchers and companies keep their own tools. Their genomic information becomes easier to understand, verify and reuse.
Switzerland-UK funding opportunity
Swiss and UK participants are invited to submit proposals for joint research and development projects that lead to innovative applications and products with strong commercial potential in both Switzerland and the UK. This bilateral call is open exclusively to Swiss and UK partners and aims to strengthen cross-border innovation collaboration. This project is being developed for the Switzerland-UK bilateral call, supported by Innosuisse and Innovate UK. For more information see the Innosuisse, Swiss Innovation Agency call page.
Evidence at a glance
Genomics already operates at population, healthcare and commercial scale. The project addresses the remaining interoperability gap between independent systems.
Business need
Genomic information already moves at scale between healthcare, research and industry, but provenance, analysis criteria and supporting evidence still require repeated translation and verification.
680,000+
NHS genomic tests / year
1M+
WGS across UK Biobank + All of Us
Approach & innovation
Start from working standards and implementations, then standardise the handoff between independent genomic systems rather than the algorithms themselves.
5
working foundations
[X]
validation studies
[X]
real cases
Project team
The consortium combines genomic data generation, healthcare, research, scientific methodology, software implementation and commercial adoption across Switzerland and the UK.
[X]
partners
[X]
institutions
[X]
major programmes represented
Target market
The primary adopters are genome centres, hospitals, diagnostic laboratories, software providers and biotechnology and pharmaceutical companies that generate, analyse or consume genomic information.
5
Swiss university medical-genetics centres
680,000+
NHS genomic tests / year
Outcomes & route to market
Working standards and reference implementations already exist. The project connects them, removes interoperability bottlenecks and validates adoption through real partner workflows.
2,073
User reports
1,302
Package downloads
793
Engine downloads
Wider impact
Open specifications, mappings, reference implementations and conformance tools are designed for independent adoption beyond the initial Swiss-UK consortium.
490,640
UK Biobank WGS
535,000+
All of Us WGS
CH + UK
initial cross-border validation
Added value
Shared infrastructure protects existing investment and reduces repeated integration while allowing organisations to retain their own technology, intellectual property and commercial differentiation.
£200M
UK Biobank WGS programme
$4.34BN
Illumina FY2025 revenue
£223.9M
Oxford Nanopore FY2025 revenue
$160M
PacBio FY2025 revenue
>$6.8BN
selected Roche acquisitions
What the project will deliver
Five foundations for one trusted genomic handoff, connecting rare-disease diagnosis, pharmaceutical discovery, R&D and business-to-business compatibility.
Work package 1. Genomic data and provenance interoperability
Purpose: Make genomic and omics information portable between data generators, laboratories, research environments and downstream systems.
The project will validate the shared representations and mappings for genomic data provenance, sample and sequencing metadata, machine-readable semantic structures, and alignment between national and international data models.
Outcome: A genome centre produces structured genomic metadata that can be consumed by an independent hospital, research environment or commercial analytical system without reconstructing its provenance.
Working validation: Omics schema with SPHN-aligned genomic and omics semantic mappings already support structured metadata conversion for clinical and research integration, including FAIR representations in RDF, SQL and other machine-readable formats (van der Horst et al., 2023).
- SIB Personalized Health Informatics Group, SPHN Omics Guidelines Existing foundation Adoption evidence
- Switzerland Omics schema implementation Existing foundation Adoption evidence
- SPHN-compliant genomic metadata conversion for clinical and research integration In validation
- Ready-to-use implementation for data generated by genome centres and other omics providers Planned extension In validation
Work package 2. Reproducible genomic analysis criteria
Purpose: Make the criteria used to select and analyse genomic variation explicit, portable and reproducible.
The project will validate machine-readable specifications for variant selection and analysis criteria, allowing independent organisations to communicate exactly which records of genetic variation qualify for an analysis and why.
Outcome: A genomic analysis or gene panel provider can communicate both the variants reported and the criteria used to select them, allowing another organisation to reproduce or independently evaluate the analysis.
Working validation: The Qualifying Variant Framework records reusable criteria for reproducible genomic analysis, while SGA-QVSS-1.0.0 defines a portable, auditable and reusable representation of rule-based criteria for qualifying records of genetic variation (Lawless et al., 2026; Swiss Genomics Association, 2026a).
- Qualifying Variant database Existing foundation
- Qualifying Variant publication, Bioinformatics (Lawless et al., 2026) Adoption evidence
- Open normative standard: SGA-QVSS-1.0.0 Existing foundation In validation
- Portable, auditable and reusable representation of rule-based criteria for qualifying records of genetic variation In validation
Work package 3. Structured variant interpretation
Purpose: Make genomic variant interpretation structured, portable and independently reviewable.
The project will validate structured representations for interpretation criteria, supporting evidence, classification decisions and consistent reporting, together with reference implementations and conformance mechanisms.
Outcome: A variant interpretation produced by one organisation can be inspected and independently reviewed by another organisation without depending on the originating software or reconstructing the assessment from an unstructured report.
Working validation: ACMG Validator provides a working implementation for structured ACMG/AMP variant interpretation, including evidence recording, completeness checking, internal consistency validation and structured report export (Richards et al., 2015; Tavtigian et al., 2020).
- Working ACMG/AMP implementation Existing foundation
- Capabilities: structured multi-variant review, evidence recording, completeness checking and internal consistency validation Existing foundation In validation
- Outputs: PDF, HTML, JSON and Markdown Existing foundation
- Live web platform Adoption evidence
Work package 4. Clinical context and family history
Purpose: Preserve the clinical and family context needed to understand genomic information after it changes hands, demonstrating value through real-world evaluation in healthcare and commercial workflows using structured pedigree, family history, phenotype and clinical context based on established nomenclature and interoperable healthcare standards.
Outcome: A receiving clinical or research organisation can understand the family and clinical context behind genomic information without depending on the originating software or reconstructing the record manually.
Working validation: Pedigree records relationships, phenotype and genomic findings in a structured format based on established human pedigree nomenclature and interoperable healthcare standards including HL7 FHIR (Bennett et al., 1995; Bennett et al., 2008; Bennett et al., 2022).
- Open source Pedigree application Existing foundation In validation
- User reports: 2,073 as of July 2026 Adoption evidence
- User adoption: Clinics in EU, USA, CH Adoption evidence In validation
Work package 5. Verifiable evidence
Purpose: Preserve the supporting evidence needed to independently assess genomic information after it changes hands, demonstrating value across healthcare, research, commercial and AI-agent workflows through structured evidence requirements, completeness and review.
Outcome: A receiving organisation can inspect the supporting evidence and assess whether declared evidence requirements have been satisfied without depending on the originating software.
Working validation: The Qualifying Evidence Matrix (SGA-QEM-1.0.0) provides an open, structured representation of declared evidence requirements and whether verifiable evidence is present or absent. QuantBayes adds a quantitative layer for assessing evidence sufficiency under those declared requirements, while QuantBayes Studio applies the same framework across scientific, clinical, commercial and AI-agent workflows without deciding whether the underlying conclusion is true (Quant Group et al., 2025; Swiss Genomics Association, 2026b).
- Open access QuantBayes Studio - Web community platform and AI-agent API. Existing foundation Planned extension
- Open source R package and engine - CRAN downloads: 1,302 as of July 2026
Existing foundation Adoption evidence
- Open access page QuantBayes engine Existing foundation
- Open source macOS/Linux engine - Desktop software downloads: 793 as of July 2026 record Existing foundation Adoption evidence
- Open normative standard: SGA-QEM-1.0.0 Existing foundation In validation
Current status in deployment
Working foundations are already available for public access, private use, online deployment, secure offline use and partner integration.
| Implementation | Public access | Private / local use | Online | Offline secure | HPC / API | Desktop | Non-restrictive licence |
|---|---|---|---|---|---|---|---|
| WP1 · Omics schema | |||||||
| WP2 · QV standard | |||||||
| WP3 · ACMG Validator | |||||||
| WP4 · Pedigree | |||||||
| WP5 · QEM standard |
QV standard
A portable standard for declaring qualifying-variant criteria so genomic analyses can be audited, reused and independently reproduced.
ACMG Validator
A browser-based review interface that turns ACMG/AMP variant classification into a structured evidence record.
Pedigree builder
A standards-based product interface that turns family history into a clinical pedigree, report and exchangeable record.
QuantBayes Studio
A web platform and AI-agent API for checking whether declared evidence requirements are present, missing or ready for review.
QuantBayes Engine
An open-source Bayesian engine for quantifying evidence sufficiency under declared requirements across genomic, scientific, clinical and automated workflows.
Cross-organisational validation and adoption
Keep your system. Add the trusted handoff. Measure the value.
The consortium is organised around focused partner roles, so each organisation can test the handoff in the part of the genomic workflow it already knows best. We have developed open-source conversion layer ready to use: schemas, mappings, reference tools, APIs and conformance checks. Prospective partners apply it to one existing output or workflow and measure whether it reduces cost, speeds review or increases acceptance.
| Partner | Pilot | Value |
|---|---|---|
| Genome centre / laboratory | Adopt the trusted handoff package for one existing genomic output. | Less custom formatting, easier delivery to partners and higher compatible throughput. |
| Healthcare / clinical genetics | Assess one trusted handoff package in an existing clinical review workflow. | Faster review, lower IT burden and safer use of external genomic results. |
| Academic / research group | Test one existing analysis through the reproducibility and evidence layer. | Stronger publications, easier collaboration and a clearer route from discovery to use. |
| SME / industry partner | Attach the trusted handoff package to one existing product, platform or analysis output. | More eligible customers, shorter sales friction and protected IP. |
Bilateral validation model
| Role | Switzerland | United Kingdom |
|---|---|---|
| Output adopter | [Swiss genome centre / laboratory] | [UK genome centre / laboratory] |
| Clinical reviewer | [Swiss healthcare partner] | [UK healthcare partner] |
| Independent validator | [Swiss academic partner] | [UK academic partner] |
| Product adopter | [Swiss SME / industry partner] | [UK SME / industry partner] |
No partner has to replace its pipeline, accreditation model, software product or commercial IP. Adoption starts with one handoff in one workflow. If it works, the same pattern can support future pilots, service agreements and commercial adoption.
Route to adoption and market
No pipeline to replace. No IP to expose. One trusted handoff to adopt. Partners keep their existing software, regulation, accreditation and commercial products. Adoption starts where it creates value first: one output, one workflow, one partner connection. The market grows through compatibility. Genome centres deliver outputs that are easier to accept. Hospitals review external results with less reconstruction. Researchers move faster from discovery to use. Companies make products easier to trust without giving away their algorithms or product logic. Open standards and reference tools create the trust signal. Partners build clinical, research and commercial products on top.
Wider impact
Independent systems do not need to become the same system. They need a trusted way to connect. Healthcare gains safer access to external genomic information. Research gains reproducible criteria. Industry gains a clearer route into clinical and research markets. Success is not another standard on a website. Success is genomic information that moves faster, costs less to use and keeps its meaning across organisations and borders.
Traction
The project already has practical signals of support, use and adoption. We have secured Amazon AWS funding for HPC services supporting work in Switzerland and the UK. We are working with the Swiss Genomics Association on normative standards for open, evidence-based genomics practice in healthcare, research and national settings. The tools are already being used beyond the proposal stage. Switzerland Omics applications have generated approximately 3,000 user reports to date, and the underlying open-source software has received approximately 3,000 downloads so far this year. We are also gathering supporting letters of interest from prospective users in clinics, laboratories and industry.
Partner benefits
Success will be measured by practical value: lower integration cost, faster review, fewer repeated checks, clearer evidence, safer clinical use and larger compatible markets for providers.
Academic and research partners
Academic partners can make existing analyses easier to reproduce, publish, share and translate. Their pipeline does not need to change; the project adds a structured criteria and evidence layer so another lab, hospital or company can understand how the result was produced, what evidence supports it, and where it can be reused.
Healthcare and clinical genetics partners
Healthcare partners can review external genomic information with less manual reconstruction. The trusted handoff package carries provenance, sample and QC metadata, clinical context and evidence checks in a structured format, reducing IT burden and making external results easier to assess safely without adopting the producer’s software.
Industry, SME and platform partners
Industry partners can sell into more healthcare and research settings by attaching a trusted handoff package to their existing product output. Customers receive the provenance, criteria and evidence checks they need to review the result, while the company keeps its algorithms, databases and product logic private.
Participation and funding model
The Switzerland-UK call can support substantial bilateral R&D activity, subject to the final funding rules, eligibility checks and partner budgets.
Participation does not have to mean building a new internal programme. For many organisations, the useful contribution is work they already understand: staff time, technical review, a representative dataset, an existing output, a live workflow, a pilot, a letter of support or a funded work-package role.
Switzerland Omics leads the shared technical layer: schemas, mappings, reference tools, APIs, conformance checks and partner-facing documentation. Partners bring the real setting where the handoff must work: a laboratory output, a clinical review process, a research analysis, a product export or a customer-facing workflow.
| Participation route | What it means | Suitable for |
|---|---|---|
| Funded project partner | A defined role, budget and deliverable inside the grant proposal. | Organisations ready to commit staff time, eligible costs or matched contribution. |
| In-kind validation partner | A focused test using existing workflows, staff expertise or representative outputs. | Hospitals, labs, research groups and companies that want to validate value without a large new programme. |
| Early adoption partner | A pilot, letter of support or product/service alignment around the trusted handoff. | Partners that want early access, influence on the standard and first-mover compatibility benefits. |
Join where the return is clear: one workflow, one output, one measurable improvement. Lower cost, faster review, safer reuse or easier adoption is enough to justify a focused contribution.
References
Bennett RL, Steinhaus KA, Uhrich SB, et al. Recommendations for standardized human pedigree nomenclature. American Journal of Human Genetics. 1995;56:745–752. DOI: https://doi.org/10.1007/BF01408073.
Bennett RL, French KS, Resta RG, Doyle DL. Standardized human pedigree nomenclature: update and assessment of the recommendations of the National Society of Genetic Counselors. Journal of Genetic Counseling. 2008;17:424–433. DOI: https://doi.org/10.1007/s10897-008-9169-9.
Bennett RL, French KS, Resta RG, Austin J. Practice resource-focused revision: standardized pedigree nomenclature update centered on sex and gender inclusivity. Journal of Genetic Counseling. 2022;31:1238–1248. DOI: https://doi.org/10.1002/jgc4.1621.
Lawless D, et al. Qualifying Variant database: reusable criteria for reproducible genomic analysis. Bioinformatics. 2026. DOI: https://doi.org/10.1093/bioinformatics/btaf676.
Quant Group, et al. A Bayesian model for quantifying genomic variant evidence sufficiency in Mendelian disease. medRxiv. 2025. DOI: https://doi.org/10.64898/2025.12.02.25341503.
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine. 2015. DOI: https://doi.org/10.1038/gim.2015.30.
Swiss Genomics Association. SGA-QVSS-1.0.0: Qualifying Variant Set Standard. Zenodo. 2026a. DOI: https://doi.org/10.5281/zenodo.20553446.
Swiss Genomics Association. SGA-QEM-1.0.0: Qualifying Evidence Matrix. Zenodo. 2026b. DOI: https://doi.org/10.5281/zenodo.17936586.
Tavtigian SV, Harrison SM, Boucher KM, Biesecker LG. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Human Mutation. 2020. DOI: https://doi.org/10.1002/humu.24088.
van der Horst E, Unni D, Kopmels F, Armida J, Touré V, Franke W, Crameri K, Cirillo E, Österle S. Bridging clinical and genomic knowledge: an extension of the SPHN RDF schema for seamless integration and FAIRification of omics data. Preprints.org. 2023. DOI: https://doi.org/10.20944/preprints202312.0373.v1.
