How DataCore Analytics Keeps Data Confidential
12 May 2026
Nine analysis services, organised by the data you have and the output you need.
Every service below states plainly what data we accept, what we do with it, and what you receive at the end. Indicative prices and turnaround times are published on each page so you can budget before you contact us.
DataCore Analytics is analysis-only. We do not sequence samples. Send us data from any provider or platform — or no data at all, if you want us to find and reanalyse what already exists in the public domain.
You send raw reads or a count matrix. We return the differentially expressed genes, the pathways they sit in, and figures ready for your manuscript.
Single-cell data is easy to generate and easy to over-interpret. We do the quality control properly, annotate cells against reference atlases, and tell you which clusters are real.
Variant analysis built for African genomes — where reference bias, population structure and under-representation in annotation databases all matter.
Genomic surveillance analysis with turnaround measured in days, built for public health laboratories that need results while they still matter.
From amplicon reads or shotgun data to taxonomic profiles, diversity statistics and differential abundance results that survive review.
Methylation arrays, bisulfite sequencing, ChIP-Seq, ATAC-Seq and CUT&Tag — processed, tested and interpreted against the right background model.
Statistical support from protocol through to the methods section — and the sample size calculation done before recruitment, not after.
The data that answers your question may already exist. We find it, reprocess it consistently, and analyse it — at a fraction of the cost of generating your own.
Machine learning only earns its place when it beats a simple baseline. We test that first, and we tell you when it does not.
Not sure which applies? Send us a description of your data and we will tell you — at no cost.
| Service | Data you send | What we do | What you receive |
|---|---|---|---|
| Gene Expression | RNA-Seq reads or count matrix | Differential expression, enrichment, co-expression | Volcano and MA plots, heatmaps, enrichment tables, DE results |
| Single-Cell | 10x / CellRanger output, FASTQ | QC, integration, clustering, cell type annotation | UMAPs, marker tables, pseudobulk DE, annotated object |
| Genetic Variation | WGS / WES FASTQ, BAM, VCF, array data | Variant calling, annotation, GWAS, population structure | Annotated VCF, candidate variants, Manhattan and QQ plots |
| Pathogen Genomics | Amplicon or shotgun pathogen reads | Consensus assembly, lineage typing, phylogenetics, AMR | Consensus FASTA, lineage report, trees, resistance profiles |
| Metagenomics | 16S / 18S / ITS or shotgun metagenomes | Taxonomic and functional profiling, diversity, differential abundance | Feature tables, diversity and ordination plots, DA results |
| Epigenetics | Methylation arrays, WGBS, ChIP, ATAC, CUT&Tag | Differential methylation, peak calling, motif enrichment | DMPs and DMRs, peak sets, browser tracks, motif tables |
| Clinical Data | Trial, cohort, survey or surveillance datasets | Study design, sample size, regression and survival modelling | Analysis plan, model outputs, CONSORT/STROBE tables, forest plots |
| Public Data | No data — just a research question | Dataset discovery, uniform reprocessing, meta-analysis | Curated inventory, reprocessed matrices, evidence summary |
| Machine Learning | Tabular, omics or imaging data with labels | Baseline testing, model development, validation, interpretation | Benchmarked metrics, ROC and calibration curves, trained model |
Beyond single-project analysis.
Annual agreements giving institutions standing analysis capacity at a reduced rate, with priority scheduling and training days included. Read more.
Quarterly workshops in bioinformatics, biostatistics and data science for students, staff and research groups. Read more.
Advice at protocol stage on platform, depth, controls, sample size and analysis strategy — before you spend money on data generation. Read more.
