Unannotated Genes in Genomics: Challenges, Opportunities, and AI Solutions

The rapid proliferation of next-generation sequencing (NGS) technologies has generated an unprecedented volume of genomic data, yet a substantial fraction of these sequenced genomes remains functionally uncharacterized, a phenomenon collectively termed " genomic dark matter." Unannotated genes, including hypothetical proteins (HPs), orphan and de novo genes, small open reading frames (smORFs), and non-canonical ORFs (ncORFs), constitute 40–60% of bacterial genomes, approximately 30–35% of the human proteome, and up to 43% of metagenomic protein clusters. These uncharacterized sequences represent a critical bottleneck in translating genomic data into biological insight and biotechnological innovation. This review provides a comprehensive examination of the categories of unannotated genes, the systemic challenges that perpetuate the annotation gap, and the diverse biotechnological opportunities these sequences harbor across plant, animal, microbial, medical, and industrial domains. Critically, we evaluate the transformative role of artificial intelligence (AI) in bridging this gap, encompassing protein structure prediction tools such as AlphaFold2 and ESMFold, protein and genome language models including ESM2 and DNABERT-2, deep learning-based functional inference frameworks, and high-throughput experimental validation platforms such as CRISPR perturbomics and transposon-insertion sequencing (TIS). We argue that an integrative, AI-driven approach to functional genomics is not merely advantageous but essential for realizing the full potential of the genomic revolution.