We help translational teams uncover the biological mechanisms driving response and resistance, using integrated clinical and molecular data to generate reproducible, decision-ready evidence.
Strengthen biomarker strategy. Refine patient selection. Support programme decisions.
Secure TRE
Reproducible Workflows
Built for Traceability
The challenge
The Translational Challenge
Understanding treatment response requires more than identifying signals. It requires explaining the biology behind them. Molecular and clinical data are often disconnected, making it difficult to link biomarkers to mechanism, evaluate findings, and generate evidence that can guide programme decisions.
Biomarkers
Which biomarkers truly predict response and why?
We connect molecular signals to biological pathways and clinical outcomes to distinguish meaningful biomarkers from noise.
Mechanism
How do responders differ from non-responders at a biological level?
We integrate multi-modal datasets to uncover the mechanisms driving response and resistance.
Evidence
What biological mechanisms explain treatment response?
We generate mechanistic insights that support biomarker strategy, patient selection, and programme direction.
Deliverables
What We Deliver
We deliver mechanistic and biomarker evidence that helps teams understand treatment response and make more confident programme decisions.
Sonrai Discovery: Volcano plot
Differential expression analysis: responders vs non-responders
What you receive
Candidate biomarker evaluation
Evaluate candidate biomarkers linked to response and resistance across datasets and modalities.
Responder vs non-responder biology
Identify biological differences driving treatment response using integrated multi-modal data.
Translational evidence reports
A report that connects findings to your question, with annotated figures, candidate priorities, analytical limitations and recommended next analyses.
Mechanism of action insights
Uncover the biological pathways and mechanisms underlying treatment response.
Associations with treatment response do not, by themselves, establish predictive performance or a causal mechanism.
Data types
Built on Integrated Multi-Modal Data
Understanding treatment response requires connecting signals across datasets. Sonrai integrates clinical and molecular datasets within a structured analytical environment, ensuring each modality contributes to a coherent, reproducible picture of treatment response.
Teams use Sonrai to turn complex multi-modal data into clear, biologically grounded evidence that explains treatment response and supports translational decision-making.
From early signal exploration to mechanism analysis, our work supports programme decisions as the evidence builds. Each example is labelled by type.
We work with your team to define the right biological question, integrate multi-modal data, and deliver reproducible evidence that explains treatment response and supports programme decisions.
01
Define the scientific question
Align on mechanism, biomarker hypotheses, and the translational context driving the analysis.
If you're investigating treatment response or predictive biomarkers, Sonrai can help integrate and analyse your molecular, imaging, and clinical datasets to generate clear, reproducible translational evidence.
We'll discuss your programme, your datasets, and the biological question you need to answer.
A tailored analytical approach
We'll outline how we'd integrate your data to uncover and evaluate the biological signals behind treatment response.
Decision-ready translational evidence
Clear, reproducible outputs your team can use to support biomarker strategy, patient selection, and programme direction.
Common Questions
Questions about translational analysis and biomarker discovery
Questions from translational teams, biomarker leads and bioinformaticians about integrating multi-modal data, evaluating biomarker findings, and generating evidence that supports programme decisions.
Can Sonrai work with RNA-seq, proteomics, imaging and clinical trial data we already generate?
Yes. Sonrai integrates transcriptomics, genomics, proteomics, single-cell sequencing, flow cytometry, whole slide imaging, and clinical trial datasets within a single governed analytical environment. If you are already generating these data types, Sonrai can connect them without requiring you to change how data is collected or stored. For a full breakdown of supported modalities, see the Sonrai data modalities page.
How can we generate stronger evidence for biomarker-driven programme decisions?
Sonrai generates decision-ready evidence by integrating molecular and clinical datasets, running reproducible analyses, and delivering outputs structured around a specific biological question - whether a biomarker predicts response, how responders differ from non-responders, or what mechanisms are driving resistance. Every output is traceable from raw data to finding. Case studies include multi-omic clinical trial work that revealed biological signals driving response across cohorts, and bench-to-bedside immunotherapy development where preclinical and clinical data were integrated to support translational strategy.
What is the best way to integrate RNA-seq, proteomics, pathology imaging and clinical outcomes for biomarker discovery?
The most effective approach is to integrate these datasets within a single governed analytical environment where cross-modality analyses run on consistent, versioned data rather than manually stitched outputs. Sonrai connects transcriptomic, proteomic, imaging, and clinical trial data in this way, allowing differential expression analysis, multi-omics factor analysis (MOFA+), survival analysis, and patient stratification to run across modalities in a single workflow. This produces findings where molecular signals are directly connected to clinical outcomes rather than analysed in isolation.
How do I identify predictive biomarkers that distinguish responders from non-responders?
Sonrai integrates your molecular and clinical datasets and runs comparative analyses across defined responder and non-responder cohorts. Differential expression analysis identifies gene expression patterns linked to response. Multi-modal analyses connect those signals to protein-level, imaging, and clinical endpoint data to build a biologically grounded picture of what distinguishes the two groups. Outputs include prioritised biomarker candidates, volcano plots, expression heatmaps, and translational evidence reports connecting molecular findings to clinical outcomes.
How do I know if a biomarker is biologically meaningful rather than a statistical artefact?
Statistical significance alone is not sufficient evidence that a biomarker is biologically meaningful. Sonrai addresses this by evaluating biomarker candidates across multiple modalities - a signal that appears in RNA-seq data and is corroborated by protein-level and imaging data is considerably stronger than one found in a single dataset. Internal trial cohort findings can also be assessed against public datasets within the same governed environment. This multi-modal, cross-dataset approach is designed to produce evidence that is more defensible for programme decisions than signals that fail to replicate.
How can I validate a predictive biomarker signature using both internal and public datasets?
Sonrai supports integration of internal proprietary datasets alongside public data sources within the same governed environment. Biomarker candidates identified in your trial cohort can be assessed against external datasets, strengthening the evidence base before committing to a stratification strategy. All cross-dataset analyses run within a secure Trusted Research Environment with full data provenance, so you retain control over how your data is used and can demonstrate exactly how findings were generated.
Can I continue using Nextflow, nf-core and my existing analytical workflows?
Yes. Any nf-core pipeline can be run on demand within the Sonrai environment. Teams are not locked into a proprietary workflow or a fixed set of methods. Bioinformaticians can work in code-based environments using existing tools and pipelines, while research scientists can use no-code applications on the same underlying data within the same governed instance. You bring your existing analytical approach into a governed, reproducible infrastructure rather than replacing it.
Should we build our own bioinformatics platform on AWS or use an existing solution?
Building on AWS gives you flexibility but requires significant ongoing investment in infrastructure engineering, pipeline maintenance, security compliance, and data governance - before a single analysis runs. Sonrai provides a governed, reproducible research environment with analytical pipelines, data integration tooling, and collaborative access already in place. Teams avoid the build time, maintenance overhead, and the reproducibility risk that comes with custom infrastructure. The core question is whether infrastructure engineering is the best use of your team's time relative to the science itself.
How much internal resource does a team need to commit to get value from a translational analysis engagement?
Sonrai works as an extension of your team rather than a service that requires significant internal coordination. The engagement begins with a focused scientific conversation to align on the biological question, the available datasets, and what a useful output looks like. From there, Sonrai manages data integration, analytical workflows, and reproducible outputs. Your team's primary input is scientific - defining the hypothesis, reviewing findings, and applying the evidence to programme decisions. Internal bioinformatics resource is not required to run analyses or manage infrastructure.
How quickly can a translational team go from raw multi-modal data to actionable biomarker findings?
Timeline depends on dataset complexity, the biological question, and data readiness. The process follows three stages: defining the scientific question and reviewing available datasets; integrating and analysing multi-modal data within a secure environment; and delivering reproducible outputs. Because Sonrai runs analysis within a governed infrastructure with versioned datasets and pre-built analytical workflows, teams avoid the setup time associated with building analytical environments from scratch. Talk to our team to get a realistic view of what is achievable for your specific programme and dataset.
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