PatientMatcher: A customizable Python‐based open‐source tool for matching undiagnosed rare disease patients via the Matchmaker Exchange network

• Published in: Human mutation Vol. 43; no. 6; pp. 708 - 716
• Main Authors: Rasi, Chiara, Nilsson, Daniel, Magnusson, Måns, Lesko, Nicole, Lagerstedt‐Robinson, Kristina, Wedell, Anna, Lindstrand, Anna, Wirta, Valtteri, Stranneheim, Henrik
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.06.2022; John Wiley and Sons Inc
• Subjects: algorithm; data availability; data integration; Disease; e-mail; equipment design; gene discovery; Genetic analysis; Genetic Association Studies; genetic association study; Genetic disorders; genetic predisposition; Genetic Predisposition to Disease; genetics; genomic API; genomic medicine; Genomics; genotype; Genotypes; human; Humans; Informatics; information dissemination; Matchmaker Exchange; matchmaking; open source software; patient coding; patient information; Patients; Phenotypes; procedures; rare disease; Rare Diseases; software; undiagnosed disease; Undiagnosed Diseases; rare disease; Matchmaker Exchange; gene discovery; genomic API; matchmaking
• ISSN: 1059-7794; 1098-1004; 1098-1004
• DOI: 10.1002/humu.24358

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype–phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by “matching” the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open‐source Python and MongoDB‐based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom‐fit similarity score algorithm and adjustable matching results notifications.