Distributed Multimodal Deep Learning Framework For High-Precision Genomic Variant Calling

Abstract

The rapid growth of next-generation sequencing technologies has transformed ge-nomics into a highly data-intensive discipline, generating unprecedented volumes of biological data and creating new challenges for accurate and scalable genomic anal-ysis. Among the various computational tasks in bioinformatics, variant calling plays a fundamental role in identifying genetic variations associated with diseases, per-sonalized medicine, population genetics, and precision oncology. Although recent deep learning-based methods have significantly improved variant detection accu-racy, many existing approaches rely on a single data representation and often fail to exploit the complementary information available within sequencing data. Fur-thermore, the increasing sensitivity and distributed nature of genomic datasets raise important concerns regarding scalability, privacy, and collaborative analysis. This work presents a distributed multimodal deep learning framework for high-precision genomic variant calling. The proposed approach combines two complemen-tary modalities: pileup tensor representations processed by a Convolutional Neural Network (CNN) and sequence-based representations analyzed by a Transformer en-coder. A gated fusion mechanism is employed to integrate the features extracted from both modalities and generate robust variant classifications. The study also investigates the theoretical foundations of genomic data, reviews state-of-the-art variant calling techniques, and examines distributed computing paradigms and fed-erated learning approaches applicable to genomics. The implemented framework is designed to exploit the strengths of both spatial and sequential representations of genomic information, thereby providing a more comprehensive characterization of genetic variants. In addition, a federated exten-sion is proposed as a future development to enable privacy-preserving collaborative learning across multiple institutions without requiring the exchange of sensitive ge-nomic data. By integrating multimodal deep learning with concepts from distributed comput-ing, this work contributes to the ongoing convergence of artificial intelligence and genomics and provides a foundation for the development of scalable, accurate, and privacy-aware genomic analysis systems. Future extensions of the proposed frame-work may facilitate large-scale collaborative genomic studies and support the ad-vancement of precision medicine.