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BioLib is the platform for distributing your bioinformatics application.

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Build With BioLib

Reach Users Worldwide

BioLib provides a one-stop-shop for distributing bioinformatics tools, making it easy to reach users and monetize models, while safeguarding your intellectual property.

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Access Users

With BioLib, it is easy for users to find, purchase, and use your applications.

Commercialize Models-icon

Commercialize Models

BioLib unlocks a new way of collaboration, enabling you to commercialize proprietary models and tools.

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Protect Intellectual Property

With BioLib's privacy-preserving compute technology, no-one, but you can access your proprietary data.

Get Started

Publishing Your Apps

What You Need

All you need to build your application is some executable code and a BioLib account. Currently, BioLib supports Python, Rust, and C/C++. If you do not have any source code in one of the supported language, you can try creating an app with the example covered in this blog post. If you do not have an account yet, you can sign up here.

The Resulting Application

Your finished project will be an application that runs in the web-browser. End-users of your app will be able to run the app without installing anything, writing any code, or worrying about dependencies. You can keep the app completely private, share it with just your colleagues, or make the app publicly available for the world.

Analytics as a Service

BioLib is the first platform to offer trusted execution for the analysis of biological data. For developers of proprietary tools and models, this means that models, tools, and data can be monetized without exposing intellectual property.

Using BioLib's zero-knowledge compute platform, companies, organizations, and individuals, can offer analytics as a service, without users, or even the platform administrators, being able to access the analytics providers' proprietary code.

BioLib Diagram

Protecting data

A trusted execution environment allow data holders and analytics providers to collaborate without compromising sensitive data or proprietary models. Data is preprocessed and encrypted client-side. No-one but the client ever has access to the unencrypted data or result. No-one but the analysis provider ever has access to their proprietary models.