# A Modular Open Source Framework for Genomic Variant Calling

arXiv: https://arxiv.org/abs/2411.11513 · HTML: https://mlsanity.com/paper/2411.11513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](https://mlsanity.com/paper/1703.10603/) (2017)

## Built on by (most cited first, 1 of 1)

- [AgriVariant: Variant Effect Prediction using DeepChem-Variant for Precision Breeding in Rice](https://mlsanity.com/paper/2602.17747/) (2026)

## Abstract (arXiv)

Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs) and insertions or deletions (indels). This paper presents an enhancement to DeepChem, a widely used open-source drug discovery framework, through the integration of DeepVariant. In particular, we introduce a variant calling pipeline that leverages DeepVariant's convolutional neural network (CNN) architecture to improve the accuracy and reliability of variant detection. The implemented pipeline includes stages for realignment of sequencing reads, candidate variant detection, and pileup image generation, followed by variant classification using a modified Inception v3 model. Our work adds a modular and extensible variant calling framework to the DeepChem framework and enables future work integrating DeepChem's drug discovery infrastructure more tightly with bioinformatics pipelines.