Bioinformatics, AI & Scientific Computing
Where biology meets exacting software.
Nuclyra designs applied AI, bioinformatics pipelines, and genomic data intelligence systems for genomics and computational biology teams — labs, biotechs, and researchers who need software built around the science, not just the data.
Software written by people who read papers, not just tickets.
Our engineers and computational biologists work in the same sprint. Pipelines respect assay design, models are validated against real biological ground truth, and every system is designed for auditability—not just a demo day.
Security, governance, and compliance considerations are built in from day one. You see the pipeline itself—not a black box.
More about us
Six disciplines, one computational company.
Bioinformatics services, AI for life sciences, in silico drug discovery, plasmid sequencing, species identification, and scientific software development — the full stack a modern life-sciences team needs, from raw sequencing reads to production-grade platforms.
Applied AI
Domain-specific applied AI for life sciences: machine learning systems built around your data, scientific constraints, and real-world workflows.
Learn more BIOBioinformatics Pipeline
End-to-end bioinformatics pipeline development, transforming raw biological and sequencing data into reproducible, analysis-ready results with consistent, traceable workflows.
Learn more DISIn Silico Drug Discovery
Molecular modeling, docking, virtual screening, and computational chemistry to accelerate early-stage target validation and candidate evaluation.
Learn more PLSPlasmid Sequencing
Whole-plasmid verification from raw reads to an annotated, reference-checked map — confirming every construct is exactly what was designed before it goes into an experiment or a manufacturing run.
Learn more SIDSpecies Identification
DNA-based species identification from barcoding, metabarcoding or whole-genome data — with curated reference databases, confidence-scored calls and an audit trail for every result.
Learn more WEBScientific Web Applications
Custom scientific software development: research portals, LIMS integrations, and internal tools engineered around your team's workflows and data.
Learn moreNot a slide deck. A working system you own.
Every engagement ends with tangible, transferable assets — not a one-off report that goes stale the day we leave.
Production codebases you own
Every repository, container and infrastructure-as-code file transfers to you outright — no vendor lock-in, ever.
Full documentation & handoff
Architecture decisions, runbooks and a recorded walkthrough, so your team can operate the system without us in the room.
Validated, monitored models
Every model ships with a validation report and production monitoring — not just a notebook that worked once.
Audit-ready traceability
Access logs, data lineage and control documentation mapped to the frameworks your reviewers actually check.
Dashboards & internal tools
The interfaces your team actually uses day to day — connected live to your data, never a static export.
Ongoing support & SLAs
Optional retainer and response-time agreements once the system is live, so it stays reliable long after launch.
We didn’t want to be the vendor who hands back a black box. We wanted to be the team still in the room when the paper gets published.
The difference shows up after the demo ends.
Most of what separates a good engagement from a bad one only becomes visible six months in.
Traditional computational vendor
- Hands back a model or pipeline with little to no documentation
- Security and compliance addressed late in the delivery cycle
- Specializes in one stage, hands you off at the next
- Limited direct access to the technical team
- Pricing built around a generic retainer, regardless of your stage
Nuclyra
- Full documentation and a recorded handoff walkthrough, every time
- Security and compliance designed in from the architecture stage
- Works across the full timeline, from discovery through production
- Direct access to the computational biologists and engineers building your system
- Engagements scoped to your actual stage, budget and grant or funding cycle
Selected engagements. Real problems. Production outcomes.
Client names are withheld under NDA — the challenges, approach and results are not.
Nextflow-based engineering of a custom 16S metagenomics pipeline
Extending nf-core/ampliseq into a fully automated 16S amplicon workflow with structured outputs and client-ready microbiome reports.
Read the case study DISComputational screening and molecular interaction profiling for drug discovery
Virtual screening, molecular docking, and structural interaction profiling to prioritize therapeutic candidates using binding-energy and interaction data.
Read the case study BIOIntegrated whole exome sequencing analysis and variant annotation
End-to-end WES workflow from alignment through variant calling to clinical annotation against ClinVar, gnomAD and functional prediction databases.
Read the case studyPurpose-built for regulated, research-heavy fields.
Wherever genomics, computational rigor, and scientific literacy all matter.
Pharma & Biotech
Discovery-to-development infrastructure, from target identification to analysis-ready data systems.
Research Institutions
Pipelines and platforms built around grant cycles.
Healthcare & Diagnostics
Secure, traceable systems for patient-facing data workflows.
Startups & Spinouts
Lean computational infrastructure for early-stage science.
Before you reach out.
You do—fully, from day one. Every repository, container, and trained model transfers to you outright. We believe your team should retain control of what we build together.
Most projects begin with a short technical discovery phase. Once scope, data access, and priorities are confirmed, we can provide a delivery plan, timeline, and proposal before development begins.
Yes. We can operate as an extension of your scientific or engineering team—from workflow design and data processing through deployment, documentation, and handoff.
Access control, encryption and audit logging are designed into the architecture from the start, not added afterward, across every engagement that touches sensitive data.
Yes. Most clients move to an ongoing retainer or SLA once a system launches, covering monitoring, minor iteration and response-time guarantees.
Regularly. We can lead a project end to end, or take an advisory or embedded role that plugs directly into a team you already have.
Notes from inside the engagements.
Field notes on bioinformatics, applied AI, compliance and the decisions that hold up once a system reaches production.
Security Isn’t a Phase: Designing Access Control for Genomic Data From Day One
Retrofitting access control onto a genomic data platform is slower and riskier than building it in from the schema up. Here’s our approach.
Inside a Federated Genomics Platform: Lessons From a 40-Lab Consortium
Forty independent labs, one dataset, zero centralized raw data. Here’s what we learned building a federated genomics platform at that scale.
Five Questions to Ask Before You Trust an AI Model With Biological Data
A model with 95% accuracy on a holdout set can still be dangerously wrong in production. Here’s what we ask before we trust one.
Have a dataset, a hypothesis, or a deadline?
Tell us what you’re building. We’ll reply with a technical point of view within one business day.