Artificial Intelligence in Gene Annotation: Current Applications, Challenges, and Future Prospects

Gene annotation is a critical process in genomics that involves the description of not only the position but also the function of an encoded element of a genome. In general, this provides biological context to sequence data, enabling an advanced level of understanding of genetic information. This is important in areas aligned with genetic engineering, studies of diseases, and evolution. Through ML and DL methodologies, AI enhances functional annotation and gene prediction effectively and accurately. This review focuses on AI in genomic research and assesses its effectiveness compared to traditional annotation tools. Using Escherichia coli as the representative model organism, the study focuses on a systematic approach of gene prediction using web Augustus with functional annotation using DeepGOPlus, an artificial intelligence tool, instead of the conventional BLAST-based annotation using the UniProt database. The study researches the extent of GO term coverage, the specificity of the annotations, and the concordance among these various tools. Artificial intelligence is highly beneficial owing to its speed, scalability, and proficiency in annotating intricate or poorly defined genomic areas. Notable instances include DeepGOPlus, which has demonstrated enhanced coverage by suggesting new terms that were frequently missed by earlier traditional tools. Notwithstanding these, AI tools face challenges such as dependence on high-quality training data, concerns about interpretability, and the need for biological validation to support the predictions. This review emphasizes the transformative impact that artificial intelligence brings to the field of gene annotation by presenting novel applications in many fields, including personalized medicine and synthetic biology, in which traditional methods suffer from severe limitations.