Reproducibility, by default
Every project ships methods documenting each tool and its version. If you can't rerun it, it isn't finished.
Anyone can hand you a results table. The question is whether you can reproduce it, explain it, and stand behind it two years later when a reviewer asks how a number was produced. Everything below is a practice, not a promise—you'll see each one on the service pages themselves.
These aren't aspirations. Each one shows up as something concrete in what we deliver.
Every project ships methods documenting each tool and its version. If you can't rerun it, it isn't finished.
Peer-reviewed, community-standard software chosen for your data type—so reviewers recognise the methods.
If your data can't answer your question, we say so before you pay—not after the invoice clears.
Predictions are labelled as predictions. Research findings aren't dressed up as clinical conclusions.
We support manuscripts; we don't ghostwrite them. Authorship follows ICMJE criteria and stays with your team.
No platform affiliations or referral arrangements. Our recommendation is the one we'd make for ourselves.
A clear boundary protects you. Knowing what a partner won't do is as useful as knowing what they will.
The hardest thing to buy in bioinformatics is a straight answer. Predictions get quoted as certainties, research signatures get described as clinical tests, and "we can do that" is easier to say than "your cohort is too small." We've tried to build every page on this site so the limits are stated as plainly as the capabilities—read any service or research area page and you'll find the caveats sitting right next to the claims.
Consistent across services, from a single analysis to a multi-phase program.
You approve a written scope before anything begins—what we'll do, what you'll receive, and what it costs. If the scope changes mid-project, we re-quote rather than surprise you.
When the work is done, you get results with publication-quality figures and tables, and a methods section documenting every tool and version we used. That last part matters more than it sounds: it's what lets you answer a reviewer, hand the project to a student, or rerun the analysis in three years when someone questions a number.
And if we think a different analysis—or no analysis at all—serves you better, you'll hear that too.
The things worth asking any analysis partner before you commit.
Tell us your organism, data type, and question. You'll get a scope and an estimate—or an honest referral elsewhere.