Every analysis is run by a senior scientific team. Our team of 10+ scientific associates - each a doctorate or postdoc with up to 15+ years of experience - spans genomics, drug discovery and statistics, taking your project from raw sequencing reads to interpreted, publication-ready results.
Every project is handled by experienced analysts and delivered as a reproducible workflow - versioned code, documented parameters and figures ready for your manuscript or report. Tell us the biological question; we handle the pipeline.
End-to-end WES and WGS analysis, from quality control through annotated, prioritized germline and somatic variants ready for interpretation.
Association testing for complex traits and disease, with rigorous QC, imputation and population-structure correction to genome-wide significance.
Construction and validation of polygenic scores for risk stratification, with careful ancestry handling and transparent predictive-performance reporting.
Gene- and region-based aggregation tests that recover rare-variant signal single-marker GWAS misses, matched to your study design and power.
Turning association signals into biology - fine-mapping causal variants, linking loci to genes and testing causal relationships across traits.
Differential expression and functional interpretation from bulk transcriptomes, delivered with publication-ready figures and full statistical reporting.
Complete single-cell workflows from count matrices to annotated cell states, trajectories and condition-level comparisons.
Reference-free transcriptome reconstruction and functional annotation for non-model organisms, with rigorous completeness assessment.
Genome-wide profiling of protein–DNA binding, open chromatin and DNA methylation - from peak calling through differential and integrative analysis.
Assembly, annotation and comparative analysis of bacterial and microbial genomes from short- and long-read sequencing data.
Genotype–phenotype association across microbial populations - resistance, virulence and adaptation - while accounting for clonal structure.
Community profiling and functional metagenomics from amplicon or shotgun data - diversity, taxonomy and differential abundance.
Evolutionary reconstruction for genes, genomes and populations, delivered as well-supported, publication-ready trees.
Joint analysis across genomic, transcriptomic, epigenomic and other layers to surface signal that no single assay reveals on its own.
Predictive and classification models for biological data - biomarker discovery, stratification and phenotype prediction - built with rigorous validation.
Custom, reproducible pipelines that scale from laptop to HPC and cloud - portable, versioned and audit-ready workflows built around your data.
From target structure to lead candidate - physics-based simulation, cheminformatics and AI-driven design that move a discovery program from hypothesis to prioritized molecules.
We build reliable 3D models of your target when no experimental structure exists, combining deep-learning prediction with template-based modeling. Every model is validated for geometry and quality before it feeds downstream design.
We screen focused or large-scale compound libraries against your target to rank likely binders and predict binding poses. The output is a prioritized shortlist of candidates worth taking to the bench.
We simulate proteins and protein–ligand complexes in atomic detail to probe stability, conformational change and binding behaviour over time. Trajectories are analysed for the mechanistic insight static structures can't provide.
We quantify how tightly candidates bind using rigorous free-energy methods, sharpening rankings that docking alone can't resolve. Built for lead optimisation, where small affinity differences are decisive.
We relate chemical structure to activity and properties with validated statistical and ML models, so you can predict before you synthesise. Each model reports its applicability domain and performance transparently.
We distil the essential 3D features a molecule needs for activity and use them to screen and design new candidates. A fast route to novel chemistry around a known binding profile.
We predict absorption, metabolism, toxicity and drug-likeness early, so liabilities surface before they cost you time and budget. Filters and flags are tuned to your program's goals.
We assemble, standardise and diversify compound collections ready for screening, stripping out the noise that derails downstream analysis. Clean, well-characterised libraries in - better hits out.
We train models on curated bioactivity data to predict activity, selectivity and key properties across large chemical spaces. Modern graph and deep-learning approaches, applied where they genuinely add value.
We generate novel, synthesisable candidates optimised toward your target profile using generative and reinforcement-learning methods. A way to explore chemistry well beyond the existing catalogue.
We identify existing drugs with new therapeutic potential using signature-matching and network-based evidence. A faster, lower-risk path from hypothesis to viable candidate.
We nominate and prioritise disease targets by integrating omics evidence with druggability and essentiality analysis. Grounding your program in the right target from the outset.
We map how compounds act across interacting targets and pathways, capturing the multi-target effects a single-target view misses. Especially valuable for complex diseases and natural-product mixtures.
Rigorous statistical support for research and clinical studies - from designing the study to interpreting the results, with sound methods and transparent reporting throughout.
We help design studies that can actually answer the question - the right sample size, sufficient power and sound randomisation, settled before data collection begins. Getting this right upfront prevents underpowered, uninterpretable results later.
We profile your data thoroughly - distributions, relationships, missingness and outliers - and turn it into clear, publication-ready visuals. The step that surfaces what the data is really telling you before any formal testing.
We select and apply the right tests for your design, with proper handling of assumptions, multiple comparisons and effect sizes. Results are reported so both statistical significance and practical magnitude are clear.
We build and validate regression models - linear, logistic and mixed-effects - to quantify relationships and predict outcomes. Full diagnostics ensure the model is sound, not merely fitted.