Single-cell data combined with phenotypes improves variant interpretation

Single-cell data combined with phenotypes improves variant interpretation

Abstract Background Whole genome sequencing offers significant potential to improve the diagnosis and treatment of rare diseases by enabling the identification of thousands of rare, potentially pathogenic variants. Existing variant prioritisation tools can be complemented by approaches that incorpor...

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Bibliographic Details
Main Authors: Timothy Chapman, Timo Lassmann
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Genomics
Subjects:
Rare disease
Variant prioritisation
Machine learning
Random forest
Interpretable models
Whole Genome sequencing
Online Access:https://doi.org/10.1186/s12864-025-11711-w
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https://doi.org/10.1186/s12864-025-11711-w

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