AgriVariant: Variant Effect Prediction using DeepChem-Variant for Precision Breeding in Rice

• URL: http://arxiv.org/abs/2602.17747v1
• Date: Thu, 19 Feb 2026 14:03:37 GMT
• Title: AgriVariant: Variant Effect Prediction using DeepChem-Variant for Precision Breeding in Rice
• Authors: Ankita Vaishnobi Bisoi, Bharath Ramsundar,
• Abstract summary: AgriVariant is an end-to-end pipeline for variant-effect prediction in rice (Oryza sativa)<n>Our approach integrates deep learning-based variant calling (DeepChem-Variant) with custom plant genomics annotation.<n>We validate the pipeline through targeted mutations in stress-response genes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predicting functional consequences of genetic variants in crop genes remains a critical bottleneck for precision breeding programs. We present AgriVariant, an end-to-end pipeline for variant-effect prediction in rice (Oryza sativa) that addresses the lack of crop-specific variant-interpretation tools and can be extended to any crop species with available reference genomes and gene annotations. Our approach integrates deep learning-based variant calling (DeepChem-Variant) with custom plant genomics annotation using RAP-DB gene models and database-independent deleteriousness scoring that combines the Grantham distance and the BLOSUM62 substitution matrix. We validate the pipeline through targeted mutations in stress-response genes (OsDREB2a, OsDREB1F, SKC1), demonstrating correct classification of stop-gained, missense, and synonymous variants with appropriate HIGH / MODERATE / LOW impact assignments. An exhaustive mutagenesis study of OsMT-3a analyzed all 1,509 possible single-nucleotide variants in 10 days, identifying 353 high-impact, 447 medium-impact, and 709 low-impact variants - an analysis that would have required 2-4 years using traditional wet-lab approaches. This computational framework enables breeders to prioritize variants for experimental validation across diverse crop species, reducing screening costs and accelerating development of climate-resilient crop varieties.

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