Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications

Authors : Emine Dilşat Yeğenoğlu

Pages : 16-29

Doi:10.47118/somatbd.1826014

View : 41 | Download : 137

Publication Date : 2025-12-31

Article Type : Review Paper

Abstract :Single-read RNA sequencing (RNA-seq) is a revolutionary technology that enables the comprehensive characterization of the transcriptome. However, the immense volume and complexity of the data generated make its full evaluation difficult using traditional bioinformatics methods. Artificial Intelligence (AI), especially Deep Learning (DL), offers a powerful set of tools to overcome these challenges, revolutionizing various stages of RNA-seq data analysis. This review compiles the applications of AI in RNA sequence analysis, including quality control and data preprocessing, transcript assembly and quantification, alternative splicing (AS) analysis, differential gene expression (DGE) analysis, gene function prediction, and gene subtype classification. Furthermore, its applications in agricultural research, such as plant-pathogen interactions, abiotic stress tolerance (drought, salinity), and improvement of crop yield and quality, are reviewed. AI applications in emerging technologies like single-cell RNA-seq (scRNA-seq) and long-read sequencing are also highlighted, and their contribution to understanding plant development and resilience mechanisms is discussed. In conclusion, AI-powered RNA-seq analysis is established as a transformative paradigm, opening new horizons in precision medicine, precision agriculture, and fundamental biological discovery.
Keywords : Yapay Zeka, Derin Öğrenme, RNA-seq, Transkriptomik, Biyoinformatik, Diferansiyel Gen Ekspresyonu, Tek Hücreli RNA-seq, Makine Öğrenimi, Tarımsal Biyoteknoloji, Hassas Tarım, Abiyotik Stres.

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