Yapay Zeka Tabanlı RNA Dizi Analizi: Algoritmalar ve Uygulamalar

Yıl 2025, Cilt: 2 Sayı: 40, 16 - 29, 31.12.2025

Emine Dilşat Yeğenoğlu

https://doi.org/10.47118/somatbd.1826014

Öz

Tekil-okumalı RNA dizileme (RNA-seq), transkriptomun kapsamlı bir şekilde karakterize edilmesini sağlayan devrim niteliğinde bir teknolojidir. Ancak, üretilen verilerin muazzam hacmi ve karmaşıklığı, geleneksel biyoinformatik yöntemlerle tam değerlendirilmesini zorlaştırmaktadır. Yapay Zeka (AI) ve özellikle Derin Öğrenme (DL), bu zorlukların üstesinden gelmek için güçlü bir araç seti sunarak RNA-seq veri analizinin çeşitli aşamalarında devrim yaratmaktadır.
Bu derlemede, kalite kontrol ve veri ön işleme, transkript birleştirme ve kantifikasyon, alternatif uç birleştirme (AS) analizi, diferansiyel gen ekspresyonu (DGE) analizi, gen fonksiyon tahmini ve gen alt tip sınıflandırmasını içeren RNA dizi analizindeki AI uygulamalarını incelenmiştir. Ayrıca, bitki-patojen etkileşimleri, abiyotik stres toleransı (kuraklık, tuzluluk) ve ürün verimi ile kalitesinin iyileştirilmesi gibi tarımsal araştırmalardaki uygulamaları gözden geçirilmiştir. Tek hücreli RNA-seq (scRNA-seq) ve uzun-okuma dizileme gibi yeni teknolojilerdeki AI uygulamaları da vurgulanmakta ve bunların bitki gelişimi ve dayanıklılık mekanizmalarının anlaşılmasına olan katkısı tartışılmaktadır.
Sonuç olarak, AI destekli RNA-seq analizi, hassas tıp, hassas tarım ve temel biyolojik keşif alanlarında yeni ufuklar açan, dönüştürücü bir paradigma olarak yerini almıştır.

Anahtar Kelimeler

Yapay Zeka , Derin Öğrenme , RNA-seq , Transkriptomik , Biyoinformatik , Diferansiyel Gen Ekspresyonu , Tek Hücreli RNA-seq , Makine Öğrenimi , Tarımsal Biyoteknoloji , Hassas Tarım , Abiyotik Stres.

Etik Beyan

Çalışma derleme olup, Etik Belgesine gereksinim yoktur.

Destekleyen Kurum

Bu yayın herhangi bir kurum tarafından desteklenmemiştir.

Teşekkür

Bu yayında teşekkür bölümüne gereksinim bulunmamaktadır.

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

Öz

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.

Anahtar Kelimeler

Artificial Intelligence , Deep Learning , RNA-seq , Transcriptomics , Bioinformatics , Differential Gene Expression , Single-Cell RNA-seq , Machine Learning , Agricultural Biotechnology , Precision Agriculture , Abiotic Stress.

Etik Beyan

An ethical approval statement is not required for this study, as it is a review article.

Destekleyen Kurum

This review did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Teşekkür

There is no acknowledgement section is required for this article.

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