Every omics layer,
expertly analysed

End-to-end LC-MS/MS proteomics for DDA and DIA acquisition modes. We handle everything from raw file processing to differential protein expression, functional annotation, and STRING protein network analysis. Suitable for plasma, tissue, cell lysate, and urinary proteomes.

Untargeted and targeted lipidomics workflows for plasma, urine, tissue, and cell-derived samples. Species-level lipid identification, class-level profiling, statistical modelling, and lipid pathway mapping. Includes urinary lipidomics capability.

Whole-genome and whole-exome sequencing analysis using GPU-accelerated NVIDIA Parabricks for ultra-fast runtimes (<5 hours for full WGS). Includes variant calling, annotation, and pharmacogenomics star allele calling for clinical drug response profiling.

Bulk RNA-seq analysis from raw reads to biological insight. Includes alignment, quantification, differential expression, and pathway enrichment — with support for multiple experimental designs including paired samples, time series, and multi-group comparisons.

Comprehensive scRNA-seq analysis from count matrices to biological interpretation. Cell type annotation using curated marker sets, trajectory inference, and differential abundance testing between conditions.

Shotgun metagenomics and 16S rRNA amplicon sequencing analysis. Taxonomic profiling from species to strain level, alpha and beta diversity analysis, differential abundance testing, and functional annotation.

Untargeted metabolomics from LC-MS and GC-MS data. Feature detection, peak alignment, PQN normalisation, metabolite annotation against HMDB and KEGG, and statistical analysis with pathway mapping.

Bulk ATAC-seq and ChIP-seq analysis including peak calling, signal heatmaps, differential chromatin accessibility, and transcription factor motif enrichment across multiple histone marks.

Multi-platform spatial transcriptomics analysis for Visium, MERFISH, CosMx, and Stereo-seq. Spatially variable gene detection, cell type deconvolution, and spatial cell communication analysis to reveal tissue architecture.

Joint analysis of multiple omics datasets to identify cross-layer biological signatures. MOFA+ latent factor analysis, DIABLO supervised integration, and cross-omics correlation analysis — ideal for precision medicine and biomarker discovery studies.

Specialised cancer genomics analysis including somatic mutation calling, tumour mutational burden (TMB), copy number variation (CNV), mutational signature analysis, and integration with clinical metadata for oncology research.