Software availability and installation

PatientMatcher is open-source and available on GitHub (https://github.com/Clinical-Genomics/patientMatcher). The software is distributed under the MIT license (https://github.com/Clinical-Genomics/patientMatcher/blob/master/LICENSE) and we encourage all interested parties to use and modify its code according to their needs. The main GitHub repository is curated by Clinical Genomics, but we look forward to establishing a collaborative environment where other users could help improving the code, adding or simply requesting new useful features.
The simplest way to run and test the server is to use the up-to-date container image with a basic software installation that can be pulled from Docker Hub (https://hub.docker.com/repository/docker/clinicalgenomics/patientmatcher). On the GitHub pages of the repository, we also provide instructions and support files to test PatientMatcher with real data without needing to install any software, except Docker. For this purpose, we compiled a multi-container Docker Compose file that, when launched by a single command from the terminal, provides a complete setup of the server, including a running instance of MongoDB containing the 50 benchmarking patients spanning 22 disorders described in Buske et al.(J. Buske et al., 2015). This server represents a standalone MME instance, ready to accept HTTP requests on localhost and port 9020. For development and testing reasons we have also created a more sophisticated Docker Compose setup, with an MME server connected to another two MME nodes (other instances of PatientMatcher), both containing demo patient data. This file is available under the /containers folder in the GitHub page of the PatientMatcher software. Deploying the software in a production environment using the official Docker image file could be achieved using Kubernetes (https://kubernetes.io/) or via Podman (https://podman.io/) system services. Another tested way to deploy the software is installing it from the Python Package Index (PyPI) using the Python installer Pip. In this case it is recommended to operate in a virtual environment, such as Conda (https://docs.conda.io/) after installing Python 3.6+. All these options, together with other server settings, are extensively described on the software GitHub pages.