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Biological data cluster analysis engine
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Arcite aims to be a general purpose biological data processing engine written in scala and based on Akka.

The purpose of Arcite is to enable scientists to run analysis on data produced by laboratory equipments like microrray scanner, NGS sequencer, etc.


This project should still be seen as Alpha or Proof of Concept (POC). There are many aspects that should be improved and probably done differently. However, Arcite can still be used almost in a productive setting as every step is traceable.

Key concepts and ideas

Arcite is a pipeline processing engine based on its own concept of a "transform". A transform is a unique operation from a state A to a state B (e.g. the transformation from multiple files produced by a machine to a unique matrix, or the production of a normalized matrix from raw data).

A transform is performed by a worker that is running on a node of the cluster. Workers are usually able to perform a few different transforms, usually in the same domain (e.g. preprocessing of NGS data). A worker can contain any dependencies that the transforms need (version of an OS, python, java, R, scala, spark, Bioconductor libraries, etc.).

The Arcite core modules take care of scheduling the workers on the cluster. If a transform fails and is not able to recover itself (e.g. because of an exception in some Python code) it should have no real effect on the whole Arcite cluster which will continue to run without trouble. If nothing comes back from the worker, the core will assume an exception has happened and will consider the worker to be dead (after a defined time out). It will eventually try to restart an instance if none for this worker type would be available anymore.

In Arcite, everything is final and therefore immutable. A transform produces something that is immutable and there is no way back. That is one of the guarantee for reproducibility.

Once a transform has been completed, an experiment definition cannot be changed, it would have to be recreated. The reason is simple, as soon as something would have been produced with the data (e.g. even a simple matrix given values for X samples with Y features), changing the experiment definition might totally change the results.


Arcite is written in Scala using the akka library. It should be deployed to a cluster of machines running docker. It's initial deployment target is DCOS but adapting it to another system like Kubernetes should not be difficult.

Building Arcite

To build Arcite you need sbt and an easy way to get it is to install sdkman (


Arcite needs a home mapped in the docker container. There are some examples of DCOS Json config files.

To be able to run as a cluster, three instances of Arcite have to be started at least:

  • the rest-api (example in cluster-rest.json): it will enable to serve Rest requests
  • the backend management :manages the cluster worker nodes
  • the helper functions : manages the experiments, their dependencies, etc.

There example of configuration files for each one for DCOS in /dcos

The rest end point has to be configured in the Arcite web application

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