Expansions of tandem repeats (TRs) cause approximately 60 monogenic diseases. We expect that the discovery of additional pathogenic repeat expansions will narrow the diagnostic gap in many diseases. A growing number of TR expansions are being identified, and interpreting them is a challenge. We present RExPRT (Repeat EXpansion Pathogenicity pRediction Tool), a machine learning tool for distinguishing pathogenic from benign TR expansions. Our results demonstrate that an ensemble approach classifies TRs with an average precision of 93% and recall of 83%. RExPRT’s high precision will be valuable in large-scale discovery studies, which require prioritization of candidate loci for follow-up studies.
CITATION STYLE
Fazal, S., Danzi, M. C., Xu, I., Kobren, S. N., Sunyaev, S., Reuter, C., … Aguiar-Pulido, V. (2024). RExPRT: a machine learning tool to predict pathogenicity of tandem repeat loci. Genome Biology, 25(1). https://doi.org/10.1186/s13059-024-03171-4
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