Integrate genomics, transcriptomics, proteomics, and clinical phenotype data into a single multi-omic analysis platform, with pathway analysis, interactive scientist workspaces, and ML model training on integrated omic datasets for cancer research and precision medicine software development.
Six integrated capabilities for research teams and precision medicine programmes that need cross-omic biological insight, not just genomic data in isolation.
Harmonises WGS output, RNA-Seq expression data, proteomics profiles, methylation data, and clinical phenotypes on shared patient and sample identifiers — removing the format incompatibilities and identifier mismatches that make cross-omic analysis impractical without dedicated infrastructure.
Pathway enrichment and network analysis across omic layers — KEGG, Reactome, STRING — enabling biological mechanisms to be identified across genomic, transcriptomic, and proteomic evidence simultaneously. Critical for cancer research and rare disease genomics programmes.
Interactive scientist-facing analysis environments with configurable cohort selection, cross-omic correlation views, differential abundance tools, and biomarker candidate ranking — designed for research scientists running multi-omic analysis for cancer research without computational biology support overhead.
Multi-modal feature extraction from cross-omic datasets, feature selection pipelines, and model training infrastructure for tumour classification, drug response prediction, and patient stratification — with experiment tracking and clinical deployment engineering.
Cross-omic visualisation: volcano plots, heatmaps, network graphs, and PCA projections — all configurable per cohort, per omic layer, and per analysis question. Exportable as audit-ready reports for publication or regulatory submission.
Governed data layer for whole genome sequencing data management — storing WGS variant data, alignments, RNA-Seq quantifications, and proteomic datasets in a structured genomics data lake with versioning, provenance tracking, and consent management.
The platform integrates open-source multi-omic frameworks with custom-built data harmonisation and governance layers — deployable on AWS, GCP, or Azure, with a HIPAA-compliant architecture for clinical research environments, and GCP BigQuery or AWS Athena as the analytical query engine.
Tell us your omic data types, your analysis questions, and your research programme. We will scope the integration architecture.
What types of cancer research programmes does the multi-omic platform support?
The multi-omic analysis platform for cancer research supports tumour profiling programmes that generate data across multiple omic layers simultaneously — somatic mutation data from WGS or targeted panels, gene expression changes from RNA-Seq, copy number profiles, methylation data, and, in some settings, proteomic and metabolomic datasets. The platform integrates those layers on shared patient and tumour identifiers, enabling pathway and network analysis across the full molecular landscape of the cancer in question. The platform also supports rare disease genomics programmes that require multi-omic evidence to interpret variants of uncertain significance in the context of gene expression and protein abundance.
How does the platform handle data from different omic technologies and formats?
Data harmonisation is the core engineering challenge of multi-omic analysis, and it is where most off-the-shelf tools fail. The platform includes a purpose-built data harmonisation layer that handles format conversion, identifier reconciliation across omic datasets using shared patient and sample ID schemes, reference version alignment (genome build, transcript annotation, protein database), and normalisation strategy selection appropriate to each omic layer. Batch effect detection and correction are applied where multi-site or multi-instrument data is combined. All harmonisation steps are versioned, parameterised, and logged — producing a reproducible data preparation pipeline that can be re-run with updated inputs or extended to new omic layers without rebuilding the analysis environment from scratch.
