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GeneYenta: A PhenotypeBased Rare Disease Case Matching Tool Based on Online Dating Algorithms for the Acceleration of Exome Interpretation
Author(s) -
Gottlieb Michael M.,
Arenillas David J.,
Maithripala Savanie,
Maurer Zachary D.,
TarailoGraovac Maja,
Armstrong Linlea,
Patel Millan,
Karnebeek Clara,
Wasserman Wyeth W.
Publication year - 2015
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22772
Subject(s) - annotation , matching (statistics) , phenotype , weighting , biology , set (abstract data type) , computer science , computational biology , clinical phenotype , exome sequencing , machine learning , data mining , bioinformatics , genetics , gene , mathematics , medicine , statistics , radiology , programming language
Advances in next‐generation sequencing (NGS) technologies have helped reveal causal variants for genetic diseases. In order to establish causality, it is often necessary to compare genomes of unrelated individuals with similar disease phenotypes to identify common disrupted genes. When working with cases of rare genetic disorders, finding similar individuals can be extremely difficult. We introduce a web tool, GeneYenta, which facilitates the matchmaking process, allowing clinicians to coordinate detailed comparisons for phenotypically similar cases. Importantly, the system is focused on phenotype annotation, with explicit limitations on highly confidential data that create barriers to participation. The procedure for matching of patient phenotypes, inspired by online dating services, uses an ontologybased semantic case matching algorithm with attribute weighting. We evaluate the capacity of the system using a curated reference data set and 19 clinician entered cases comparing four matching algorithms. We find that the inclusion of clinician weights can augment phenotype matching.