Deletion variants calling in third-generation sequencing data based on a dual-attention mechanism

TL;DR

This paper introduces DASV, a new method for identifying deletion variants in genomic data using a dual-attention mechanism, which improves accuracy compared to existing tools.

Contribution

DASV introduces a dual-attention mechanism and multi-scale network for more accurate deletion variant calling in third-generation sequencing data.

Findings

DASV achieves a better balance between precision and recall compared to existing tools.

The method improves the F1 score across various genomic datasets.

Gene alignment data is effectively converted into images for improved variant detection.

Abstract

Deletion is a crucial type of genomic structural variation and is associated with numerous genetic diseases. The advent of third-generation sequencing technology has facilitated the analysis of complex genomic structures and the elucidation of the mechanisms underlying phenotypic changes and disease onset due to genomic variants. Importantly, it has introduced innovative perspectives for deletion variants calling. Here we propose a method named Dual Attention Structural Variation (DASV) to analyze deletion structural variations in sequencing data. DASV converts gene alignment information into images and integrates them with genomic sequencing data through a dual attention mechanism. Subsequently, it employs a multi-scale network to precisely identify deletion regions. Compared with four widely used genome structural variation calling tools: cuteSV, SVIM, Sniffles and PBSV, the results demonstrate that DASV consistently achieves a balance between precision and recall, enhancing the F1 score across various datasets. The source code is available at https://github.com/deconvolution-w/DASV.

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