The infrastructure behind the evidence you can trust
Sonrai Discovery helps our scientific teams generate evidence that remains transparent, reproducible, and defensible throughout the life of a programme - from discovery through clinical development.
The Platform
Sonrai Discovery
Sonrai Discovery is the environment our scientific team uses to generate decision-ready evidence. Built for multimodal biomedical data - omics, imaging, and clinical - it combines analytical capability with the governance and reproducibility that programme decisions depend on.
Every partner and client works within the same governed framework. Every analysis is traceable to the workflow and data version that produced it, and can be re-run against that data version.
Why teams trust the decisions they make with Sonrai
Every programme decision depends on the quality of the evidence behind it. Sonrai Discovery provides the transparency, reproducibility, and governance needed to ensure that evidence remains trustworthy throughout the life of a programme.
Transparent analysis
Every result can be reviewed, challenged, and understood - not just accepted.
Reproducible workflows
Methods remain consistent and repeatable across projects, teams, and time.
Governed infrastructure
Sensitive data stays within a controlled environment, with access managed per user.
Scientific expertise embedded
Analysis is delivered with biological context - not just computational outputs.
How Discovery supports the work
Four ways the environment supports a scientific team. Each example says what Sonrai did, what Discovery enabled, what the team received and what the example does not show. Client cases and illustrative workflows are labelled separately.
Connect multimodal trial data
A clinical-stage pharmaceutical company wanted to know whether its drug produced the expected downstream effects in a Phase II oncology trial, and whether those effects related to patient response.
- What Sonrai did
- Our scientists integrated tissue RNA-seq, plasma and urine proteomics, digital pathology and clinical outcome data, then applied pathway-level analysis and multi-omic factor analysis (MOFA).
- What Discovery enabled
- All data types in one governed environment, with each step recorded so the work can be traced back to the source data.
- What the team received
- A clearer picture of treatment activity, candidate biomarkers for further investigation, and a reproducible analytical foundation.
- What this does not show
- No quantified result is reported, and the candidate biomarkers are not validated markers.
- ConnectRNA-seq, proteomics, pathology and clinical outcomes
- IntegratePathway-level analysis and MOFA
- ReviewA scientist checks what the data supports
- ResultCandidate biomarkers for further investigation
Run and hand over reproducible analysis
How a single analysis is recorded so that it can be re-run and checked later, by us or by your own team.
- What Sonrai does
- A scientist runs the analysis in a versioned workflow and records the version of the data it used.
- What Discovery enables
- Workflows can be re-run against the exact data version, with provenance from raw data to finding.
- What the team receives
- The outputs, with the workflow version and data version behind them. Where we develop custom code for your analysis, it is generally provided alongside the results so you can run it outside Sonrai in future.
- What this does not show
- This is a schematic of the process. It is not a client result and does not show how any particular analysis performed.
- InputsData version recorded
- Quality controlChecks recorded
- AnalysisVersioned workflow
- OutputsTied to data and workflow versions
- HandoverRe-runnable and reviewable
Collaborate with controlled access
A global science park wanted its labs to share data and collaborate without exposing patient data or intellectual property.
- What Sonrai did
- Set up a parent instance for the science park and a separate instance for each lab, with unified data management, analysis tools, staff training and 24/7 support.
- What Discovery enabled
- Each lab works within its own governed instance, with control over what is shared between labs and what remains separate. Security measures include end-to-end encryption and multi-factor authentication, with regular security audits.
- What the team received
- Easier data sharing between labs, less time from data collection to insight as reported by the labs, lower overheads from not running separate data systems, and the ability to add labs without major extra investment. No data breaches have been reported.
- What this does not show
- Outcomes are as reported by the labs, and none is measured independently. It shows one deployment model and says nothing about certification or compliance status.
Consolidate trial data on one managed platform
A clinical-stage biopharmaceutical company wanted data from several studies and trials in one place, so that biomarkers could be compared across projects.
- What Sonrai did
- Brought data from multiple studies and trials onto one platform managed by Sonrai, deployed Nextflow pipelines for bulk RNA-seq and mass spectrometry proteomics, set up a cross-project biomarker dashboard, and provided training and a dedicated client success manager.
- What Discovery enabled
- Access controls by dataset and project, no-code tools alongside R, Python and Nextflow, and imaging tools for whole slide images.
- What the team received
- Their own client-owned Sonrai Discovery instance, with data from several studies in one place and biomarkers that can be compared across projects.
- What this does not show
- No quantified outcome is given, and it does not show a scientific result.
- StudiesData from several studies and trials
- One managed platformAccess controlled by dataset and project
- Pipelines and dashboardNextflow pipelines and a cross-project biomarker view
- Client-owned instanceBiomarkers comparable across projects
Client cases are anonymised. Illustrative workflows show how the environment works and are not client results.
Product screenshot: survival analysis, biomarker distribution, Sankey plots and IHC pathology in a single governed environment
Want to explore Sonrai Discovery with your own data?
Speak to our scientific teamA Trusted Research Environment built for sensitive biomedical data
Every analysis performed within Sonrai Discovery remains protected, governed, and traceable. Whether you are reviewing biomarker hypotheses, validating clinical findings, or managing governed datasets, analysis runs within the governed environment.
Built for sensitive data
Sonrai Discovery operates as a Trusted Research Environment for sensitive clinical and patient-level data.
Separate governed instances
Every partner, client, or programme operates within its own isolated instance. Environments are configured around each client's access and separation requirements, controlling what teams can share and what remains separate. Access is managed at an individual level.
Cloud-native security
Hosted on Amazon Web Services (AWS), with encryption at rest and in transit, role-based access controls, and logging of user actions and data access.
Analysis runs in a governed environment
Analysis happens inside the platform. Outputs are traceable. Sensitive datasets, whether clinical, genomic, or imaging, are held within the governed environment. Your data remains yours. While you are a customer, you can upload and remove data as you see fit. When the contract ends, our engineering team erases the platform and any remaining data once you have confirmed you have everything you need.
Why evidence remains trustworthy
The value of an analysis comes not only from the result, but from understanding how that result was generated. Sonrai Discovery keeps every conclusion connected to the data and workflow that produced it, so it can be revisited later.
Multi-omics integration
Sonrai Discovery connects data types that typically remain siloed - genomic, transcriptomic, proteomic, imaging, and clinical - within a single analytical environment. That connection is what makes programme evidence coherent rather than fragmented.
- Harmonised data across modalities and cohorts
- Cross-modality analysis without fragmented toolchains
- Consistent data versioning across all analyses
End-to-end traceability
Every analysis in Sonrai Discovery keeps a record from raw data through each processing step to the final output. That record lets scientific and clinical teams see how a finding was produced.
- Provenance from raw data to finding
- Re-runnable analyses tied to exact data versions
- Versioned workflows available for scientific review
- Decision-ready outputs with traceable methodology
Reproducible, traceable, and open to review
When evidence supports programme decisions, teams need confidence that findings can be revisited, challenged, and reproduced. Sonrai Discovery is built to support that.
Transparent workflows
Understand exactly how results were generated - every workflow versioned and documented.
Provenance from raw data to finding
Every processing step is recorded, so a finding can be traced back to the raw data and the workflow version that produced it. User actions and data access are logged.
Connected evidence
Links between datasets, workflows, and conclusions are maintained throughout - so evidence remains coherent as programmes evolve.
Decision-ready outputs
Results are structured for programme reviews and scientific discussion: evidence teams can act on, not raw data.
"For us, it's been like having an extension of our company, with that expertise that we could bring in."
Dr Francesca Barone, MD, PhD
Chief Scientific Officer - Candel Therapeutics
Sonrai Discovery is active across biopharma, diagnostics, and imaging
See Sonrai Discovery in your programme
Discover how Sonrai Discovery combines scientific expertise and governed infrastructure to generate decision-ready evidence for your team.
Talk to our teamQuestions about Sonrai Discovery
Everything you need to know about how Sonrai Discovery works, what it supports, and how teams use it to generate evidence that supports programme decisions.
What is Sonrai Discovery and what problem does it solve?
Sonrai Discovery is a governed, cloud-native research infrastructure built specifically for precision medicine teams. It addresses the challenge of fragmented data and workflows, where genomics, transcriptomics, proteomics, imaging, and clinical data often reside in separate systems and require significant effort to integrate. Sonrai Discovery brings these data types together within a single secure environment, allowing research and clinical teams to analyse, compare, and collaborate on data in context without building or maintaining their own infrastructure.
What is the alternative to building a custom AWS bioinformatics platform for multi-omics analysis?
Rather than assembling a custom stack on AWS, which requires engineering resource, ongoing maintenance, security oversight, and governance processes, biopharma and biotech teams can deploy Sonrai Discovery as a governed cloud environment built for precision medicine research. Sonrai Discovery combines automated bioinformatics pipelines, secure data storage, role-based access controls, and AI and machine learning tooling within a single platform. This allows teams to focus on scientific questions rather than infrastructure management.
Can a biotech company run reproducible multi-omics pipelines without an internal bioinformatics team?
Yes. Sonrai Discovery is designed for teams with mixed technical capabilities. Bioinformaticians can work in code-based environments, while research scientists can perform analysis using no-code applications on the same underlying data within the same governed instance. Workflows are versioned and can be re-run against the same data version. One partner reported that after deploying Sonrai Discovery, research scientists could perform initial exploratory analysis independently, reducing the need for bioinformaticians to act as intermediaries for routine analytical tasks and helping remove bottlenecks within the programme.
What data types does Sonrai Discovery support?
Sonrai Discovery integrates transcriptomics, genomics, epigenomics, proteomics, imaging, and clinical data within a single analytical environment. Automated Extract, Transform, Load (ETL) workflows are used to harmonise and connect datasets across modalities. Publicly available gene expression, proteomics, and metabolomics datasets can also be incorporated alongside proprietary data where appropriate. Whole slide imaging, cytometry, and structured clinical trial data are supported within the same governed platform.
How does Sonrai Discovery handle data security and governance?
Sonrai Discovery operates as a Trusted Research Environment for sensitive clinical and patient-level data. Every partner or programme operates within its own isolated, separately governed environment. Environments are configured around each client's access and separation requirements, controlling what teams can share and what remains separate. The platform provides encryption at rest and in transit, role-based access controls, and logging of user actions and data access. Sensitive clinical, genomic, and imaging datasets remain within the governed environment throughout the analytical process. Your data remains yours. While you are a customer, you can upload and remove data as you see fit. When the contract ends, our engineering team erases the platform and any remaining data once you have confirmed you have everything you need.
How does Sonrai Discovery make analysis reproducible and traceable?
Workflows in Sonrai Discovery are versioned and can be re-run against the exact data version they used, so findings can be revisited and reproduced later. Each analysis keeps provenance from raw data through each processing step to the final output. This lets teams see how a finding was generated and review the analytical process behind it. Suitability for any regulatory purpose depends on the study and is not implied by this record.
How does Sonrai approach biomarker discovery for treatment response and resistance in immunotherapy programmes?
Sonrai Discovery connects molecular, imaging, and clinical data within a single governed environment to support the investigation of biomarkers associated with treatment response and resistance. Differential expression analysis, survival analysis, and patient stratification workflows can be applied across integrated RNA-seq, proteomic, imaging, and clinical trial datasets, allowing teams to compare responders and non-responders within a traceable analytical framework. Visualisations such as survival curves and biomarker distribution plots can be generated within the environment. This helps translational teams investigate resistance mechanisms and generate evidence that can support patient stratification and translational research programmes.
What analytical methods does Sonrai use to integrate RNA-seq, proteomics and clinical trial data for translational biomarker research?
Sonrai Discovery harmonises RNA-seq, proteomic, and clinical trial data across modalities and cohorts within a single analytical environment. Analytical workflows include differential expression analysis, multi-omics factor analysis (MOFA+), survival analysis, patient stratification, and machine learning-based biomarker discovery within a governed and reproducible framework. Cross-modality analysis can be performed without fragmented toolchains or manual data stitching. Every result is connected to the underlying data through full pipeline provenance, allowing translational teams to trace findings back to source data and reproduce analyses as programmes evolve.
Can Sonrai Discovery work alongside our existing tools and cloud environment?
Yes. Sonrai Discovery is designed to complement existing scientific workflows rather than replace them. Teams can continue using tools such as Nextflow, nf-core, Python, R, and Jupyter-based workflows while benefiting from a governed environment that provides reproducibility, security, and collaboration capabilities. This allows organisations to retain existing analytical expertise and workflows while reducing the overhead associated with infrastructure management and governance.
How long does it take to get up and running on Sonrai Discovery versus building in-house?
Building a custom multi-omics cloud infrastructure in-house typically requires engineering effort, platform design, security configuration, governance processes, and ongoing maintenance. Sonrai Discovery is deployed as a managed, governed environment, allowing teams to begin integrating data and running analyses without needing to design, build, and maintain their own infrastructure stack. Sonrai works alongside client teams throughout implementation and analysis, combining technology with scientific expertise. This can reduce the time required to move from data integration to analysis compared with building and maintaining equivalent capabilities internally.