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Overview of the Cytobank API

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Cytobank offers a RESTful JSON API for connecting any software application with basic and advanced functionality of the Cytobank Cloud. View the Cytobank API documentation to get started.

 

Background

API stands for application programming interface. An API is the set of instructions and available options for how two pieces of software can interact. Just as a graphical user interface (GUI) offers a visual interface for humans to interact with a software application, an API offers a non-graphical interface that other software applications can use to interact with a particular software application. Prior to the launch of the Cytobank API, the only way to interact with Cytobank was via a web browser. Now, the Cytobank cloud can be accessed and used by any script or application. Regardless of the language used -- R, Python, Matlab, Java, Ruby, Perl, etc. -- data and configurations can be programmatically pulled from or pushed to Cytobank via the API.

 

The value of the Cytobank API

Read our blog post: Example Cytobank API Workflows to Empower Research Organizations.

The value of the Cytobank API is realized in the flexibility it confers for connecting Cytobank to any other non-Cytobank pipelines, workflows, systems, divisions, or data stores. The possibilities are vast, but some examples include:

  • Run any algorithm on data stored in Cytobank: The list of algorithms to help analyze data is growing continuously. Use the Cytobank API coupled to any algorithm to script the retrieval, processing, and reupload of results back to Cytobank.

  • Data and attachment upload: Write a program that sends data to the Cytobank cloud automatically. This could be used for uploading large amounts of data in a less supervised fashion, or for linking custom analysis results back into a Cytobank experiment.

  • Compliance: Create a script that scans a computer for FCS files then checks to make sure they are safely in Cytobank, and reports if they aren't. No data left behind.

  • Connecting cross-disciplinary workflows: Allow bioinformaticians, statisticians, and the rest of the organizational orchestra to script the consumption of data produced and initially analyzed by biologists. Afterwards, attach the custom analysis results back to the Cytobank Experiment.

  • Connecting information systems: Make transfers of data and information from Cytobank into ELNs and centralized repositories an automated process.


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