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Bayesian Integrative Detection of Structural Variations With False Discovery Rate Control

Sheng Lian et al.

Biometrical Journal2026https://doi.org/10.1002/bimj.70128article
ABDC B
Weight
0.50

Abstract

Recent advances in long-read sequencing technologies have empowered the detection of structural variations (SVs) associated with genetic diseases. Despite the availability of numerous SV callers and efforts to merge SVs from multiple tools, there remains limited research on quantifying the confidence levels for the reported results. In this work, we propose a Bayesian integration model that combines SV calls from different tools. Notably, we introduce an approach for false discovery rate (FDR) control and provide a quantitative measure for the merged SVs. Our model can handle cases where certain tools lack quality scores, showcasing its flexibility in incorporating additional tools. Through extensive simulation studies, we evaluate the performance of our method under various conditions, demonstrating the FDR estimation accuracy and improved F1 score. Furthermore, we validate our model using simulated human genome sequencing data and the HG002 dataset.

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https://doi.org/https://doi.org/10.1002/bimj.70128

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@article{sheng2026,
  title        = {{Bayesian Integrative Detection of Structural Variations With False Discovery Rate Control}},
  author       = {Sheng Lian et al.},
  journal      = {Biometrical Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/bimj.70128},
}

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Bayesian Integrative Detection of Structural Variations With False Discovery Rate Control

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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