Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmation

Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmation

Abstract Background Orthogonal confirmation of variants identified by next-generation sequencing (NGS) is routinely performed in many clinical laboratories to improve assay specificity. However, confirmatory testing of all clinically significant variants increases both turnaround time and operating...

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Bibliographic Details
Main Authors: Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner
Format: Article
Language:English
Published: BMC 2025-08-01
Series:BMC Genomics
Subjects:
Next generation sequencing
Sanger confirmation
Machine learning
Clinical decision-support tool
Online Access:https://doi.org/10.1186/s12864-025-11889-z
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https://doi.org/10.1186/s12864-025-11889-z

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