Overview
What this category covers
This is the largest category in the catalogue and the most varied: sequencing, spatial, microbiome, proteomic, metabolomic, imaging, cytometry, spectral and clinical data, each analysed to publication standard. What the services share is a way of working — experimental design reviewed before analysis, statistics chosen for the data rather than for convenience, and results delivered with the code, versions and figures needed to defend them.
What we deliver
▸Bulk and single-cell transcriptomics, variant analysis and spatial transcriptomics
▸Microbiome, proteomic, post-translational modification and metabolomic analysis
▸Imaging, radiomics, pathomics and flow cytometry data analysis
▸Evidence synthesis, Mendelian randomisation and clinical prediction modelling
▸Publication-quality figures, methods text and reproducible analysis code
Who this is for
▸Research groups without a dedicated bioinformatics team
▸Biotechs needing analysis to support a publication, grant or investor package
▸Teams holding data generated by a sequencing provider but never properly analysed
▸Clinical researchers building prediction models or synthesising published evidence
How engagements in this category are run
Analysis projects start with a review of the experimental design, the metadata and the batch structure, because these determine what the data can support regardless of how it is processed. Comparisons and statistical models are agreed before analysis begins, so results are not selected after the fact. Everything is delivered with the processed data, the code and the software versions, which means the analysis can be extended, audited or repeated when reviewers ask.
Services
11 computational services in this category
H1
Bulk & Single-Cell RNA-Seq Analysis
Quality control, alignment and quantification, differential expression with count-based statistical models, and pathway enrichment. Single-cell work adds integration, clustering, cell type a…
Learn MoreH2
WES / WGS Variant Analysis
SNV/indel/CNV/SV calling, functional annotation, pathogenicity prediction, somatic/germline classification, tumor mutational burden.
Learn MoreH5
Spatial Transcriptomics Analysis
Tissue spatial gene expression mapping, cell-cell communication, niche identification, deconvolution of cell types in spatial context.
Learn MoreH7
Metagenomics & Microbiome Analysis
Taxonomic and functional profiling of metagenomic or amplicon sequencing data, including diversity analysis, differential abundance, pathway reconstruction, and microbial association network…
Learn MoreH8
Proteomics & Post-Translational Modification Analysis
Quantitative proteomics and post-translational modification analysis, including phosphorylation, acetylation, lactylation, palmitoylation, succinylation, pathway enrichment, and protein netw…
Learn MoreH9
Metabolomics & Mass Spectrometry Data Analysis
Untargeted and targeted LC-MS/GC-MS data processing, metabolite annotation, differential metabolite analysis, pathway enrichment, and integrated metabolomics interpretation.
Learn MoreH10
Medical Imaging, Radiomics & Pathomics Analysis
Medical image and digital pathology analysis, including MRI/fMRI/fNIRS processing, feature extraction, segmentation, radiomics/pathomics modeling, and image-based predictive analysis.
Learn MoreH11
Meta-Analysis, Systematic Review & Mendelian Randomization
Evidence synthesis and causal inference services covering systematic review, pairwise or network meta-analysis, publication bias assessment, and one- or two-sample Mendelian randomization.
Learn MoreH12
Flow Cytometry Data Analysis
Quality control, compensation review, manual or algorithm-assisted gating, population frequency comparison, dimensionality reduction, clustering, and publication-ready visualization for flow…
Learn MoreH13
Polysaccharide Structure & Spectral Data Analysis
Integrated interpretation of chromatography, mass spectrometry, NMR, methylation, and monosaccharide composition data to characterize polysaccharide molecular weight, linkage patterns, branc…
Learn MoreH14
Clinical Prediction Modeling & Public Database Analysis
Statistical and machine-learning prediction models using clinical cohorts or public datasets, including feature selection, model validation, calibration, decision curve analysis, and reprodu…
Learn MoreIn Practice
What a project in this category looks like
Three shapes we are asked for most often. Scope is always set after a feasibility review, so timelines below are typical rather than quoted.
Full analysis of a sequencing dataset from a provider
The situation
A sequencing vendor delivered raw data and a minimal report, and the biology has not been extracted.
What we do
Reprocessing from raw data with proper quality control, batch and confounder assessment, differential analysis with a model matched to the design, and pathway-level interpretation.
What you receive
Quality control per sample, full result tables, enrichment analysis, publication-quality figures, analysis code and a methods section with software versions.
Typical timeline: 2–5 weeks
Single-cell characterisation of a tissue or tumour
The situation
A team has single-cell data and needs cell-type-resolved conclusions that will survive review.
What we do
Filtering, doublet detection, integration across samples, clustering, evidence-based annotation, compositional and pseudobulk differential testing, and trajectory or communication analysis where the design supports it.
What you receive
Processed objects ready for your own exploration, annotated populations with the evidence behind each label, statistics computed at sample level, and figures.
Typical timeline: 3–6 weeks
Building a clinical prediction model that holds up
The situation
A clinical cohort exists and the team wants a prognostic or diagnostic model suitable for publication.
What we do
Data cleaning and missingness assessment with multiple imputation, sample size and events-per-variable review, model development with penalisation, optimism-corrected internal validation, calibration and decision curve analysis.
What you receive
The final model with its prediction equation, honest performance with confidence intervals, interpretability output, manuscript-ready tables and figures, and reproducible code.
Typical timeline: 3–8 weeks
Questions
Asked before we scope this work
Will you tell us if the study design limits what we can conclude?
Yes, and before the analysis rather than in the discussion. Replication, batch structure and confounding determine what the data can support regardless of how it is processed.
Can you reanalyse data another group produced?
Yes, and we start from raw data wherever possible so that processing choices are visible rather than inherited.
What do we receive besides figures?
Processed data objects, full result tables, the analysis code, software versions and a methods section. If an analysis cannot be re-run, it is difficult to defend.
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Other service categories
Target Identification & Validation5Virtual Screening (Small Molecule)3Small Molecule Design & Optimization2ADMET & Drug-Likeness Prediction4Molecular Dynamics & Simulation3AIDD — Small Molecule Platform2Structure-Based Drug Design (SBDD)3Antibody Design & Engineering (AI)5Peptide & Cyclic Peptide Drug Design4Protein Design & Engineering (AI)4ADC & Bioconjugate Design2Compound & Biological Library Design3
Bring us the scientific question, not a service code
Share your target, dataset, sequences or objective. We will recommend a scoped computational plan, the expected deliverables, a timeline and the validation options.
Request a Feasibility Review