Use Case
The same project. Two very different experiences.
One composite, illustrative scenario — walked through the full request-to-analysis lifecycle twice: once the way it typically happens without FedOMIX, once the way it happens with FedOMIX on both sides of every step.
DEMO – Aarhus Regional Genetics Institute is a fictional, composite institution invented for this page — not a real customer, and not a real project. Nothing below describes an actual engagement.
The scenario: a 40-sample rare-disease cohort needs whole-genome sequencing, plus RNA-seq on a subset of those same samples — two service types, from two different providers. What follows is that one project, followed stage by stage, twice.
Stage By Stage
Request through analysis, side by side.
01 · Request & Quoting
The same cohort details get re-typed into two separate provider portals and email threads — one for the WGS lab, one for the RNA-seq lab — with no shared record of what was actually asked for.
01 · Request & Quoting
One structured request, both service types, submitted once — routed to the matching providers automatically instead of retyped per portal.
02 · Compare & Select Providers
Comparing providers means separately emailing each one for capability and turnaround, then collating the replies into a spreadsheet by hand.
02 · Compare & Select Providers
Providers are compared side by side, by technology, capacity and turnaround, in the same view the request was submitted from.
03 · Contracting
Two separate quote documents, two separate contract threads — neither one tied back to the original request in any single record.
03 · Contracting
Quotes come back attached to the same request; accepting one moves the order forward, both providers' orders visible in the same dashboard.
04 · Sample Registration
The WGS lab's LIMS uses its own sample ID scheme; the RNA-seq lab uses a different one. Someone manually cross-references the same 40 samples by hand to keep them straight.
04 · Sample Registration
One standardized sample manifest, one ID per sample, shared by both providers — no re-keying, no manual cross-referencing.
05 · Tracking
Status means emailing the project coordinator at each lab and waiting for a reply — twice, since there are two providers on this project.
05 · Tracking
A live dashboard shows both orders side by side, updated the moment either provider changes a status.
06 · QC & Rework
The RNA-seq provider's QC flags degraded material in 6 of the 40 samples — but it surfaces days later as a PDF emailed to whoever happened to be on the thread. What happens next gets sorted out over a scattered email and phone chain, with no clear owner and no record of what was decided.
06 · QC & Rework
The QC failure appears immediately as a structured flag on the affected samples. A rework ticket opens automatically, tied to the original order and the exact 6 samples — assigned, tracked through re-extraction and resubmission, with the full history visible to both sides.
07 · Delivery
Each provider sends data its own way — one through a client-portal login, one through a manual file-transfer email — landing in two different places with no unified acknowledgment.
07 · Delivery
Both providers' deliverables land in the institute's own storage, each acknowledged and audit-logged, in the same place.
08 · Handoff to Analysis
The analysis team receives two disconnected sets of files and two QC reports in two different formats — reconciling them into one comparable view is manual work before analysis can even start.
08 · Handoff to Analysis
Structured, comparable QC sits alongside both delivered datasets in one place — the analysis team starts from a single coherent record instead of reconciling two.
The Cost Of Friction
Not just slower — more to manage.
What used to take a chain of emails and separate portals across several days now happens as one structured request, one shared sample manifest, and one live view of status and QC — including the moment something goes wrong. No invented numbers here — this is the same story above, viewed by what it actually costs a team: time, manual effort, and attention pulled away from the science.
Time
Spread across days of waiting on replies and re-checking status by email, across two separate providers.
Time
Handled inside one sitting — submit, track, and see updates as they happen, from either provider.
Manual Effort
The same cohort details, sample IDs and status checks re-typed and cross-referenced by hand across two systems.
Manual Effort
Entered once, shared automatically with both providers and everyone tracking the project.
Visibility & Stress
No single place to check — status means chasing whoever happens to be on the email thread that day.
Visibility & Stress
One dashboard, one clear owner at every step, visible to everyone on the project — nothing to chase.
Where The Effort Goes
Coordinator and lab time spent on admin and reconciliation — chasing status, fixing mismatched IDs — instead of the science.
Where The Effort Goes
That time goes back into running the project and reviewing results — not managing the paperwork around it.
Use Case
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