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PatientMatcher: a customizable Python-based open-source tool for matching undiagnosed rare disease patients via the MatchMaker Exchange network
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  • Chiara Rasi,
  • Daniel Nilsson,
  • Måns Magnusson,
  • Nicole Lesko,
  • Kristina Lagerstedt-Robinson,
  • Anna Wedell,
  • Anna Lindstrand,
  • Valtteri Wirta,
  • Henrik Stranneheim
Chiara Rasi
Science for Life Laboratory

Corresponding Author:[email protected]

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Daniel Nilsson
Karolinska Institute
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Måns Magnusson
Karolinska Institute
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Nicole Lesko
Karolinska Universitetssjukhuset
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Kristina Lagerstedt-Robinson
Karolinska University Hospital
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Anna Wedell
Karolinska Institute
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Anna Lindstrand
Karolinska University Hospital
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Valtteri Wirta
Science for Life Laboratory
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Henrik Stranneheim
Science for Life Laboratory
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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 production 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.
29 Sep 2021Submitted to Human Mutation
30 Sep 2021Submission Checks Completed
30 Sep 2021Assigned to Editor
07 Oct 2021Reviewer(s) Assigned
26 Oct 2021Review(s) Completed, Editorial Evaluation Pending
12 Nov 2021Editorial Decision: Revise Minor
27 Jan 20221st Revision Received
31 Jan 2022Submission Checks Completed
31 Jan 2022Assigned to Editor
06 Feb 2022Review(s) Completed, Editorial Evaluation Pending
08 Feb 2022Editorial Decision: Revise Minor
14 Feb 20222nd Revision Received
15 Feb 2022Submission Checks Completed
15 Feb 2022Assigned to Editor
15 Feb 2022Review(s) Completed, Editorial Evaluation Pending
18 Feb 2022Editorial Decision: Accept
22 Feb 2022Published in Human Mutation. 10.1002/humu.24358