AI in Genomics: From Variant Calling to Multi‐Omics Integration

Artificial intelligence (AI) strategies are revolutionizing genomics by extracting complex patterns that traditional statistical pipelines are likely to miss. This mini-review aims to provide a concise overview of how AI is transforming major genomic technologies including variant calling, gene expression analysis, single-cell transcriptomics, CRISPR-Cas9 optimization, and multi-omics integration. In genome sequencing, machine learning variant callers greatly improve the accuracy and the rate at which single nucleotide and structural variants are called. In bulk RNA-Seq, AI augmented quantification, denoising, and differential expression modules complement the highly established STAR-featureCounts-DESeq2 pipeline, revealing subtle signals in big data sets. In single cell transcriptomics, deep learning approaches enhance batch correction, automate cell type annotation, and track developmental trajectories, hence clarifying cellular heterogeneity. AI-assisted guide RNA design, outcome prediction, and nuclease engineering enable more efficient CRISPR-Cas9 editing, reducing experimental cycles, and off-target effects. Finally, integrated platforms that combine genomic, transcriptomic, epigenomic, proteomic, and metabolomic layers provide an integrative view of cellular regulation and disease mechanisms. The review also covers current limitations, sparsity of data, model bias, privacy, and the need for standardized benchmarks and offers future directions in the form of interpretable models, collaborative learning, and open science practices. Together, these developments render AI an indispensable partner to unravel genomic complexity and accelerate precision medicine applications.