Omicsy fits how you work
From individual researchers tackling their first dataset to service companies scaling omics offerings — Omicsy removes the infrastructure barrier, whatever your role or team size.
The data finally arrived. Now you are out of time.
You gave everything to the lab. The pipeline should not be the thing that costs you the publication.
You spent the first two years of your PhD repeating experiments that did not work — troubleshooting protocols, redoing extractions, waiting weeks between sequencing runs only to find the library failed again. By the time the data finally came back clean, your submission deadline was months away and your thesis committee was asking for results.
The sequencing worked. That was supposed to be the hard part. But now you are staring at 60 FASTQ files, a bioinformatics pipeline you have never run, and a three-week HPC queue at your university. The clock that was already running out just accelerated.
With Omicsy, you upload your files, select your pipeline — metagenomics, RNA-seq, microbiome profiling, or others — answer a few guided questions about your experimental design, and run. Results come back in hours, not weeks. And as each step completes, Omicsy explains what it did and why, so you are not just getting numbers — you are learning the analysis while it runs.
Every run also produces publication-ready figures, reproducible methods text, and AI-generated Results summaries written in biological language. Your thesis chapter does not start from scratch. It starts from your data.
Biological insight — even when your bioinformatician moves on
Your lab generates sequencing data regularly. You should not be held hostage by a single person who knows the pipelines.
Your immunology lab just received single-cell RNA-seq data from a clinical trial — twelve patient samples, a major investment of time and grant money. Your postdoc who handled all the bioinformatics accepted an industry offer last month. The data is sitting on a hard drive. Your grant report is due in six weeks.
Your remaining team are expert immunologists, excellent at cell culture, flow cytometry, and clinical data collection. None of them have ever run a bioinformatics pipeline. Hiring a dedicated bioinformatician takes months. Sending the data to a CRO costs thousands and takes weeks per round of analysis.
Omicsy is built for labs in exactly this situation. Your research associate uploads the sequencing outputs, follows a guided single-cell workflow, and within 48 hours the team has UMAP visualisations, cell-type annotations, and differential expression results — with AI summaries written in immunology language, not pipeline jargon.
Role-based workspaces mean you can collaborate with your bioinformatics collaborators externally while your wet-lab team handles the day-to-day analysis. You remain in control of the data and the narrative.
Enterprise-grade infrastructure, without the enterprise contract
You have the skills. You should not have to build and manage the infrastructure stack for every new client.
You left your postdoc to go freelance. You have five concurrent clients — a biotech startup, two university labs, a hospital research unit, and a CRO running a pilot project. Each project is different: metagenomics one month, single-cell the next. Without an institutional HPC account, you've been spending three days per project configuring cloud environments and managing conda dependencies before any actual analysis begins.
The real problem is predictability. A client sends 400 samples instead of 40, and your AWS bill triples. A new project requires a tool version you've never installed. You spend more time on infrastructure than on the biology you were hired to interpret.
With Omicsy, you create a separate workspace for each client, run whichever pipeline the project demands, and track compute costs at the project level — so you know exactly what to invoice. When a client's dataset scales up, Omicsy's cloud infrastructure scales with it. You don't pay for idle capacity.
Every run produces a complete, reproducible methodology record. Your deliverables come with full documentation of what ran, what versions, and what parameters — the kind of rigour clients increasingly require and reviewers expect.
Add omics analysis to your offering — without the infrastructure investment
Your clients want full end-to-end analysis. You should not need a €150,000 server room to deliver it.
You run a bioanalytical services company — sample preparation, library generation, sequencing coordination. Clients are increasingly asking for the full data analysis as part of the contract. Building and maintaining a dedicated HPC cluster would cost over €150,000 before the first run, plus ongoing sysadmin overhead. Your workload is variable: some months you process hundreds of samples, others just a dozen.
Outsourcing the analysis to a third party means another margin layer, slower turnaround, and loss of the direct client relationship. But the infrastructure risk of building in-house is real.
With Omicsy, your team runs the analysis directly on cloud infrastructure that scales precisely to your workload. Busy quarter? Scale up credits. Slow quarter? Pay only for what you use. No idle servers, no fixed hardware costs.
Client data is separated into individual workspaces with full governance and audit trails. Export-ready reports — with visualisations, methodology documentation, and AI-generated summaries — go directly to your clients under your brand. You launch a new revenue stream in days, not quarters.
Bring omics analysis in-house — at a fraction of the outsourcing cost
Your proprietary data should not need to leave your environment to be analysed. And it should not cost €10,000 per project to interpret it.
Your R&D team generates proteomics and metagenomics data from clinical samples and drug candidate screens on a regular cadence. You have been outsourcing all bioinformatics to a CRO at approximately €10,000 per project, with four-to-six-week turnaround times and limited transparency into how the analysis was run. Your scientists are disconnected from the data. Your regulatory team has questions about reproducibility that take weeks to answer.
Building an internal bioinformatics team is an option — but hiring, infrastructure, and tooling investment take 12–18 months before the first analysis runs. You need a faster path.
Omicsy gives your research scientists the ability to run publication-grade, reproducible pipelines directly from their workstations — without building infrastructure. Data stays in your controlled workspace. Every run logs exact tool versions, parameters, and compute environment for full audit traceability.
When your regulatory submission team needs to document the analytical methodology, the answer is already there — version-locked, step-by-step, accessible in the workspace. When your scientists want to run a quick exploratory analysis that would have previously meant a €2,000 outsourcing invoice, they run it themselves in hours.
Your ecology expertise is enough — let the platform handle the bioinformatics
You built the study design, collected the samples, and know what the data should reveal. You should not need a bioinformatics degree to get there.
You study the gut microbiome of migratory birds across five countries. Over three field seasons you have collected 340 samples from 14 species at 10 sites. Your expertise is in ecology, evolution, and animal behaviour — not bioinformatics pipelines. Your first paper was possible because a bioinformatics collaborator happened to have time. That person now has a waiting list.
You have the data. You have the ecological framework to interpret it. What stands between you and a high-impact publication is the computational analysis — and right now that gap feels like an impassable wall of tools, environments, and command-line scripts that have nothing to do with your science.
Omicsy was built for researchers in exactly this position. You upload your 16S amplicon data or whole-genome sequences, follow the guided pipeline workflow with contextual explanations of each step, and Omicsy handles the computation. You receive diversity analyses, taxonomic community profiles, and statistical comparisons — with AI-generated summaries written in ecological language, not pipeline jargon.
Your results come with complete reproducibility metadata — the tool versions, parameters, and workflow documentation that journals increasingly require. You submit your study knowing the methods section is already written, and the analysis is auditable from raw reads to final figures.
Wherever you are in your omics journey
Start with 100 free credits — no credit card, no configuration, no infrastructure. Your first pipeline is minutes away.