A Modular Open Source Framework for Genomic Variant Calling

TL;DR

This paper introduces a modular, open-source framework that integrates DeepVariant into DeepChem, enhancing genomic variant calling accuracy with a CNN-based pipeline suitable for bioinformatics and drug discovery applications.

Contribution

It presents a novel, extensible variant calling pipeline within DeepChem that leverages DeepVariant's CNN architecture for improved genetic variation detection.

Findings

Enhanced variant detection accuracy

Modular pipeline integrated into DeepChem

Supports realignment and candidate detection stages

Abstract

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.