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

Clinical Snapshot

Key Findings

• P Value: Not reported — no statistical testing is performed within the review itself

• Effect Size: Not reported — no pooled effect sizes are calculated or presented

• Primary Outcome: No primary outcome formally defined or quantitatively reported. The review narratively describes AI-driven improvements across variant calling accuracy, RNA-seq quantification, single-cell batch correction and cell type annotation, CRISPR guide RNA design efficiency, and multi-omics data integration.

• Nnt Or Sensitivity: Not applicable — no NNT, sensitivity, specificity, or hazard ratio data are synthesised. Individual tool performance metrics (e.g., variant caller accuracy rates) are referenced narratively but not pooled or formally compared.

• Confidence Interval: Not reported — no confidence intervals are provided for any claim

Clinical Application

Feasibility of implementing AI genomics tools varies substantially by domain. Research-grade tools such as DeepVariant (variant calling), Seurat/scVI (single-cell analysis), and multi-omics integration platforms are available open-source but require significant bioinformatics infrastructure, computational resources, and domain expertise. Clinical deployment requires regulatory clearance, laboratory accreditation, and clinical validation studies not addressed in this review. In the Australian context, genomic medicine is advancing through the Australian Genomics Health Alliance and Medicare-funded genomic testing pathways. The TGA regulates software as a medical device (SaMD), meaning AI-based genomic diagnostic tools would require TGA conformity assessment before clinical deployment. The RACGP and RACP have not yet issued specific guidelines on AI-assisted genomic interpretation in primary or specialist care. The National Pathology Accreditation Advisory Council (NPAAC) standards for genomic testing would apply to any clinical implementation. PBS listing of genomic tests remains limited, and the health economic case for AI-enhanced genomic pipelines in Australian public health settings is not addressed by this review. Researchers at Australian genomics centres (e.g., Garvan Institute, WEHI, AGHA) are active in several of the domains reviewed, but this paper does not engage with Australian-specific datasets or regulatory frameworks. Broadly applicable to research and translational genomics contexts involving human disease characterisation, oncogenomics, rare disease diagnosis, and precision medicine pipeline development. Not directly applicable to routine clinical practice without further prospective validation of specific AI tools in clinical cohorts.

Abstract

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.

References

1. 1.Sultana, H., Mohanty, S., Solomon, A. D., Iqbal, M. Y., Wani, A. K., Kumar, V., & Khattri, A. (2026). AI in genomics: From variant calling to multi-omics integration. BioEssays: News and Reviews in Molecular, Cellular and Developmental Biology. Advance online publication. https://doi.org/10.1002/bies.70160