Genomic Data Science
Turning large-scale data into discovery — scalable analysis, statistics, visualisation and reproducible reporting.
What this area is.
Genomic data science is where engineering, statistics and biology meet at cohort scale. We build the data infrastructure and analytical methods that make large studies tractable and reproducible.
From interactive dashboards to rigorous statistical modelling, we turn terabytes into figures, decisions and shareable, reproducible reports.
Tools & technologies
What we do.
Core methods we apply in genomic data science.
Data engineering
Cohort-scale ingestion, harmonisation and storage.
Statistical modelling
Robust inference and machine learning at scale.
Interactive dashboards
Explorable, publication-ready visual analytics.
Reproducible reporting
Notebook- and pipeline-driven, auditable outputs.
Cloud / HPC scaling
Parallelised analysis across large infrastructures.
Method development
New approaches for emerging data types.
From data to insight.
How a genomic data science project flows end to end.
Cohort data
multi-source
Engineer
harmonise · QC
Model
stats / ML
Visualise
dashboards
Report
reproducible docs
Decide
insight & support
Publication-grade figures.
Interactive, live-rendered visualisations used in genomic data science.
Where we go deep.
Cohort-scale analysis
TCGA, cBioPortal and consortium-scale datasets.
Reproducible research
Pipelines and notebooks that others can rerun.
Decision support
Turning analysis into actionable output.
Questions we answer.
A few of the things people ask about genomic data science — and our short answers. Ask CGB-AI for more.
What is genomic data science?
The discipline of making large genomic datasets analysable, reproducible and interpretable — combining engineering, statistics and domain biology.
How do you keep huge analyses reproducible?
Versioned pipelines, captured environments and parameter provenance, so a result can always be regenerated.
Publications in Genomic Data Science.
Drawn from our full record of 173 papers, filtered to this area.
Start a genomic data science project.
Tell us the biological question and the data you have — we will map out an approach.