Build the evidence to prioritise therapeutic targets
We evaluate molecular evidence across datasets to support target prioritisation and further experimental work, inside a secure, reproducible research environment.
The Target Validation Challenge
Target discovery often produces multiple candidates but limited clarity on which targets have the strongest evidence. We help teams evaluate targets across datasets and biological contexts.
Which targets show consistent biological signals?
Signals that appear in one dataset but not others are hard to act on. We help teams establish which targets hold up across evidence sources.
How do targets behave across datasets and cohorts?
Context matters. We analyse how a target behaves across patient populations, tissue types, and experimental conditions.
What evidence supports moving a target forward?
We generate clear, structured evidence summaries that give programme teams the confidence to prioritise or deprioritise.
What Teams Can Decide
We synthesise molecular evidence to support target prioritisation. Each output sets out which targets the evidence supports, what supports that ranking, and what evidence is still missing.
Consolidated molecular evidence highlighting biological signals associated with each target.
Assess target behaviour and signal consistency across multiple datasets and cohorts.
Understand how targets interact within biological pathways and disease mechanisms.
A report that connects findings to your question, with annotated figures, candidate priorities, limitations and recommended next analyses.
Consistent molecular signals can support target prioritisation, but do not by themselves establish therapeutic efficacy or safety.
Modalities We Analyse
We connect datasets within a unified analytical environment to ensure consistent, comparable results across evidence types.
Genomics and Variant data
Identify genetic variants and mutations associated with target biology across patient cohorts and disease contexts.
RNA-seq and transcriptomics
Evaluate gene expression signals linked to target activity and pathways across tissues and phenotypes.
Proteomic signals
Measure protein abundance and signalling markers associated with treatment response and disease progression.
Public and internal datasets
Integrate evidence across studies to assess target consistency and relevance — combining curated public sources with your proprietary data.
How Teams Work With Sonrai
Every analysis is executed within Sonrai's Trusted Research Environment (TRE), ensuring data remains secure, workflows are versioned and reproducible, and outputs are traceable from raw data to finding.
Define the target hypothesis
We work with your team to frame the biological question, review available datasets, and align on the evidence needed to evaluate each target candidate.
Integrate datasets and evaluate evidence
Our scientists run multi-modal analyses across genomic, transcriptomic, and proteomic datasets inside Sonrai Discovery, with versioned workflows, the exact data versions recorded for each analysis, and provenance from raw data to finding, so each assessment can be reviewed and reproduced.
Deliver prioritisation summary
You receive a structured evidence summary: ranked targets, supporting signals, and programme-ready outputs your team can use to make and defend prioritisation decisions.
Cases and Worked Examples
Examples of how Sonrai analysis can support target and mechanism decisions. Each example is labelled by type.
View all case studies →
Integrates imaging, omics, and clinical data from a Phase II oncology trial to study target engagement and treatment response.
Combines histopathology imaging with omics data to link molecular signals to tissue-level features.
Integrates preclinical and clinical data to understand immune response mechanisms and support translational development.
Analyses preclinical data to evaluate treatment efficacy and inform candidate progression decisions.
Ready to strengthen your target evidence?
If you're evaluating therapeutic targets, Sonrai can integrate molecular datasets to assess biological signals and support target prioritisation.
What to expect
We'll discuss your programme, your datasets, and what evidence you need to move forward.
We'll outline how we'd integrate your data and which analyses would best address your question.
Structured outputs your team can use to prioritise, deprioritise, or build the case to move forward.
Questions about target identification and validation
Questions from research and development teams about evaluating therapeutic targets, integrating multi-modal evidence, and generating reproducible outputs that support target prioritisation decisions.
How does Sonrai help teams evaluate and prioritise therapeutic target candidates?
Sonrai integrates molecular datasets including proteomics, genomics, transcriptomics, and curated public data within a single governed analytical environment to help teams build a consolidated evidence picture for each target candidate. By bringing multiple sources of evidence together, researchers can assess target-associated signals within a broader biological context rather than evaluating datasets independently. This supports more informed target prioritisation and downstream experimental planning.
What data types does Sonrai use for target identification and validation?
Sonrai supports proteomics, genomics, transcriptomics, clinical datasets, high-throughput screening outputs, mass spectrometry data, and curated public biological datasets. These data types can be integrated and analysed within a single environment, allowing teams to explore evidence across modalities without relying on disconnected systems and workflows.
How does Sonrai help teams investigate target signals across multiple datasets?
Target evaluation often involves comparing evidence across different experiments, cohorts, and modalities. Sonrai enables teams to investigate target-associated signals across transcriptomic, proteomic, genomic, and clinical datasets within the same analytical environment. Signals observed consistently across multiple datasets may warrant further investigation, while inconsistent findings can be explored in greater detail before additional resources are committed.
What challenges does Sonrai help address during target identification and validation?
Target identification programmes often involve large volumes of biological data distributed across multiple systems and teams. Common challenges include fragmented datasets, manual data processing, limited reproducibility, and difficulty comparing findings across experiments. Sonrai addresses these challenges through automated workflows, integrated data management, reproducible analytical processes, and collaborative access to shared datasets and results.
Can Sonrai integrate proprietary data with public proteomic databases for target evaluation?
Yes. Sonrai supports the integration of proprietary datasets with publicly available biological and proteomic resources within a governed analytical environment. This enables researchers to compare internal findings against external evidence sources while maintaining control of proprietary data and ensuring analytical processes remain reproducible and traceable.
How does Sonrai make target evaluation evidence reproducible and traceable?
All analyses run within Sonrai's secure Trusted Research Environment using versioned workflows, with the exact data versions recorded for each analysis and provenance from raw data to finding. Every result can be traced back to the underlying data and analytical methods used to generate it. This supports scientific review, collaboration, and the ability to revisit analyses as programmes evolve.
How does Sonrai support proteomic analysis during target identification?
Proteomic analysis can provide important evidence when investigating protein expression and biological pathways associated with potential therapeutic targets. Sonrai supports mass spectrometry-derived proteomics data alongside transcriptomic, genomic, and clinical datasets within the same analytical environment, enabling researchers to explore protein-level evidence alongside other molecular signals.
How does target evaluation with Sonrai differ from manual data review and disconnected workflows?
Many target identification programmes rely on datasets spread across multiple systems, manual analysis steps, and disconnected workflows. Sonrai provides a unified analytical environment where data integration, processing, and analysis can be performed using reproducible workflows with shared access to results. This reduces administrative overhead and makes it easier for teams to review and compare evidence across studies.
Can Sonrai help explore additional therapeutic targets beyond the initial candidate list?
Yes. Sonrai enables teams to investigate multiple target candidates within the same analytical framework. By integrating molecular and clinical evidence across datasets, researchers can compare candidate targets, explore associated biological signals, and prioritise areas for further experimental investigation.
What does a target identification and validation engagement with Sonrai look like?
Engagements typically follow three stages. First, Sonrai works with stakeholders to define the biological question, target hypotheses, and available datasets. Second, relevant molecular, clinical, and public datasets are integrated and analysed within Sonrai Discovery. Third, findings are delivered through reproducible outputs that may include target evidence summaries, comparative analyses, visualisations, and supporting analytical evidence. Sonrai scientists remain involved throughout the process to provide scientific context and support interpretation of the findings.