Better targets, faster trials, cleaner submissions.

AI programs in pharma stall on the data side, not the model side.

A modern data layer should take the disparate data you already have. Public and licensed references such as TCGA, GTEx and Open Targets should be mounted beside your data, so no model starts from a blank screen, and every feature matrix, response definition and result should carry its provenance.

That is what DNAnexus does for your translational and AI teams.

Bioinformatics and data science teams spend most of their time wrangling data rather than doing science. Give them back the week.

The access controls your governance team already enforces should not be the reason data stays locked up.

On DNAnexus, a central team can open data to the whole company safely: permissions and sub-environments are tiered by access level, so consent that differs from trial to trial or therapeutic area to therapeutic area is honored without building a separate environment for each.

When a biomarker is ready to become a companion diagnostic, the handoff happens on the same platform the leading diagnostics labs already run on.

Trusted Research Environments on DNAnexus give every collaborator a governed place to work inside your boundary.

An environment is stood up in days, in the region your data must stay in, with no egress; an acquired company can be working on your data within the same window.

Licensed real-world datasets arrive ingested, modeled and queryable from day one, and each quarterly clinical or sequencing refresh lands in the same model without re-ingestion.

This is the same technology behind the UK Biobank Research Analysis Platform and the UK Biobank Pharma Proteomics Project, where thirteen biopharma companies work on one dataset.

Consortium scale is where it started.

On DNAnexus the audit trail is a by-product of doing the work. Every job carries immutable QC and provenance from the first query, so the project workspace is the reproducibility package.

Analysis plans stay alive: the common data model specification and the SAP sit in shared notebooks linked to the code that implements them, so plan and execution stay verifiably in sync.

GxP and 21 CFR Part 11 readiness, HIPAA, GDPR and FedRAMP Moderate come as standard, and validated environments with turnkey computer system validation documentation cut the IQ/OQ effort you would otherwise carry yourself.