A Transformers-based framework for refinement of genetic variants

TL;DR

This paper introduces a deep learning framework using Transformers to improve the accuracy of genetic variant refinement in sequencing data.

Contribution

The novel contribution is a generalizable Transformer-based framework for variant refinement that integrates with standard pipelines and improves over heuristic filters.

Findings

The framework achieved 89.26% accuracy and a ROC AUC of 0.88 when trained on 2 million variants.

VariantTransformer improved baseline filtering accuracy by 4%–10% across tested samples.

It outperformed traditional filters and approached the accuracy of state-of-the-art AI-based callers like DeepVariant.

Abstract

Accurate variant calling refinement is crucial for distinguishing true genetic variants from technical artifacts in high-throughput sequencing data. While heuristic filtering and manual review are common approaches for refining variants, manual review is time-consuming, and heuristic filtering often lacks optimal solutions, especially for low-coverage data. Traditional variant calling methods often struggle with accuracy, especially in regions of low read coverage, leading to false-positive or false-negative calls. Advances in artificial intelligence, particularly deep learning, offer promising solutions for automating this refinement process. Here, we present a Transformers-based framework for genetic variant refinement that leverages self-attention to model dependencies among variant features and directly processes VCF files, enabling seamless integration with standard pipelines such as BCFTools and GATK4. Trained on 2 million variants from the GIAB (v4.2.1) sample HG003, the framework achieved 89.26% accuracy and a ROC AUC of 0.88. Across the tested samples, VariantTransformer improved baseline filtering accuracy by 4%–10%, demonstrating consistent gains over the default caller filters. When integrated into conventional variant calling pipelines, VariantTransformer outperformed traditional heuristic filters and, through refinement of existing caller outputs, approached the accuracy achieved by state-of-the-art AI-based variant callers such as DeepVariant, despite not operating as a standalone caller. By positioning this work as a flexible and generalizable framework rather than a single-use model, we highlight the underexplored potential of Transformers for variant refinement in genomics. This study contributes a blueprint for adapting Transformer architectures to a wide range of genomic quality control and filtering tasks. Code is available at: https://github.com/Omar-Abd-Elwahab/VariantTransformer.

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