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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.00659 |
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| _version_ | 1866917738674388992 |
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| author | Abdelwahab, Omar Torkamaneh, Davoud |
| author_facet | Abdelwahab, Omar Torkamaneh, Davoud |
| contents | Variant calling refinement is crucial for distinguishing true genetic variants from technical artifacts in high-throughput sequencing data. Manual review is time-consuming while heuristic filtering often lacks optimal solutions. Traditional variant calling methods often struggle with accuracy, especially in regions of low read coverage, leading to false-positive or false-negative calls. Here, we introduce VariantTransformer, a Transformer-based deep learning model, designed to automate variant calling refinement directly from VCF files in low-coverage data (10-15X). VariantTransformer, trained on two million variants, including SNPs and short InDels, from low-coverage sequencing data, achieved an accuracy of 89.26% and a ROC AUC of 0.88. When integrated into conventional variant calling pipelines, VariantTransformer outperformed traditional heuristic filters and approached the performance of state-of-the-art AI-based variant callers like DeepVariant. Comparative analysis demonstrated VariantTransformer's superiority in functionality, variant type coverage, training size, and input data type. VariantTransformer represents a significant advancement in variant calling refinement for low-coverage genomic studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00659 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Refinement of genetic variants needs attention Abdelwahab, Omar Torkamaneh, Davoud Genomics Variant calling refinement is crucial for distinguishing true genetic variants from technical artifacts in high-throughput sequencing data. Manual review is time-consuming while heuristic filtering often lacks optimal solutions. Traditional variant calling methods often struggle with accuracy, especially in regions of low read coverage, leading to false-positive or false-negative calls. Here, we introduce VariantTransformer, a Transformer-based deep learning model, designed to automate variant calling refinement directly from VCF files in low-coverage data (10-15X). VariantTransformer, trained on two million variants, including SNPs and short InDels, from low-coverage sequencing data, achieved an accuracy of 89.26% and a ROC AUC of 0.88. When integrated into conventional variant calling pipelines, VariantTransformer outperformed traditional heuristic filters and approached the performance of state-of-the-art AI-based variant callers like DeepVariant. Comparative analysis demonstrated VariantTransformer's superiority in functionality, variant type coverage, training size, and input data type. VariantTransformer represents a significant advancement in variant calling refinement for low-coverage genomic studies. |
| title | Refinement of genetic variants needs attention |
| topic | Genomics |
| url | https://arxiv.org/abs/2408.00659 |