# Bayesian Integrative Detection of Structural Variations With False Discovery Rate Control

## TL;DR

This paper introduces a Bayesian model to integrate and improve the accuracy of detecting genetic structural variations while controlling false discoveries.

## Contribution

The novel Bayesian integration model introduces FDR control and handles SV calls without quality scores.

## Key findings

- The model improves F1 score and accurately estimates FDR in simulations.
- Validation on HG002 dataset confirms the model's effectiveness in real sequencing data.

## 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.