Rhapsody: predicting the pathogenicity of human missense variants

Luca Ponzoni, Daniel Peñaherrera, Zoltán N. Oltvai, İvet Bahar

Bioinformatics 2020 article cited by 101

Source Open-access PDF More in Medicine & Health

importance 6/10 partially solved Genomics and Rare Diseases clinical genomics stage: research open-source model

Problem

Predicting whether human missense variants are pathogenic remains difficult for clinical molecular diagnostics.

Why it matters

Pathogenicity predictions guide diagnosis of genetic disease in clinical settings.

What the AI did

Rhapsody, a machine learning method using sequence coevolution plus structure- and dynamics-based features, automates pathogenicity assessment.

Results

Outperformed well-established and/or advanced prediction tools on a benchmark of about 20,000 annotated variants.

Impact

Peer-reviewed tool with 101 citations indicating research uptake; clinical adoption not stated in source.

Record

Venue	Bioinformatics
Published	2020
Work type	article
Authors	Luca Ponzoni, Daniel Peñaherrera, Zoltán N. Oltvai, İvet Bahar
Citations	101 (OpenAlex, at import time)
References	67
Model or method	Rhapsody (machine learning method)
Organization	not stated in source
Primary source	https://doi.org/10.1093/bioinformatics/btaa127
Open access	hybrid
Keywords	missense variants, pathogenicity, machine learning, protein dynamics, clinical diagnostics

Importance rating

6 / 10

significant — thousands to millions affected

Pathogenicity prediction of missense variants guides clinical molecular diagnostics of genetic disease; benchmarked on ~20,000 variants but population impact not stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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