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

Emine Dilşat Yeğenoğlu

doi:10.47118/somatbd.1826014

TR EN

Yapay Zeka Tabanlı RNA Dizi Analizi: Algoritmalar ve Uygulamalar

Abstract

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.

Keywords

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

Supporting Institution

Bu yayın herhangi bir kurum tarafından desteklenmemiştir.

Ethical Statement

Çalışma derleme olup, Etik Belgesine gereksinim yoktur.

Thanks

Bu yayında teşekkür bölümüne gereksinim bulunmamaktadır.

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

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

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

Supporting Institution

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

Ethical Statement

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

Thanks

There is no acknowledgement section is required for this article.

References

Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015). Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology, 33(8), 831–838.
Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome Biology, 21(1), 30.
Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq: A Python framework to work with high-throughput sequencing data. Bioinformatics, 31(2), 166–169.
Andrews, S. (2009). FastQC: A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120.
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34(5), 525–527.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., … & Shendure, J. (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature, 566(7745), 496–502.
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., … & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141), 20170387.
Cooper, L., Meier, A., Laporte, M. A., Elser, J. L., Mungall, C., Sinn, B. T., … & Jaiswal, P. (2018). The Planteome database: An integrated resource for reference ontologies, plant genomics and phenomics. Nucleic Acids Research, 46(D1), D1168–D1180.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. Crossa, J., Fritsche-Neto, R., Montesinos-López, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-López, A., &
Bentley, A. R. (2021). The modern plant breeding triangle: Optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Plant Science, 12, 651480.
Dincer, A., Celik, S., Hiranuma, N., & Lee, S. I. (2018). DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia. bioRxiv, 278739.
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., … & Gingeras, T. R. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21.
Fu, S., Wang, A., & Au, K. F. (2019). A comparative evaluation of hybrid error correction methods for error-prone long reads. Genome Biology, 20(1), 26.
González-Rodríguez, V. E., Izquierdo-Bueno, I., Cantoral, J. M., Carbú, M., & Garrido, C. (2024). Artificial intelligence: A promising tool for application in phytopathology. Horticulturae, 10(3), 197.
Greene, C. S., Tan, J., Ung, M., Moore, J. H., & Cheng, C. (2014). Big data bioinformatics. Journal of Cellular Physiology, 229(12), 1896–1900.
Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025). Foundation models in bioinformatics. National Science Review, 12(4), nwaf028.
Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18, 83.
He, J., Cui, B., Liu, P., Meng, X., & Yan, J. (2025). Utilizing machine learning and bioinformatics analysis to identify drought stress responsive genes in wheat. Frontiers in Sustainable Food Systems, 9, 1612009.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44–57.
Jaganathan, K., Panagiotopoulou, S. K., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., … & Farh, K. K. H. (2019). Predicting splicing from primary sequence with deep learning. Cell, 176(3), 535–548.
Jolliffe, I. (2011). Principal component analysis. In International Encyclopedia of Statistical Science (pp. 1094–1096). Springer.
Kim, D., Paggi, J. M., Park, C., Bennett, C., & Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology, 37(8), 907–915.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Li, W. V., & Li, J. J. (2018). An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature Communications, 9(1), 997.
Li, X., Wang, K., Lyu, Y., Pan, H., Zhang, J., Stambolian, D., … & Li, M. (2020). Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nature Communications, 11(1), 2338.
Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930.
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053–1058.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal, 17(1), 10–12.
McQueen, J. B. (1967). Some methods of classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281–297).
Michael, T. P., & VanBuren, R. (2020). Building near-complete plant genomes. Current Opinion in Plant Biology, 54, 26–33.
Montesinos-López, O. A., Montesinos-López, A., Pérez-Rodríguez, P., Barrón-López, J. A., Martini, J. W., Fajardo-Flores, S. B., … & Crossa, J. (2021). A review of deep learning applications for genomic selection. BMC Genomics, 22(1), 19.
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14(4), 417–419.
Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T. C., Mendell, J. T., & Salzberg, S. L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology, 33(3), 290–295.
Petricka, J. J., Winter, C. M., & Benfey, P. N. (2012). Control of Arabidopsis root development. Annual Review of Plant Biology, 63(1), 563–590.
Rasheed, A., & Xia, X. (2019). From markers to genome-based breeding in wheat. Theoretical and Applied Genetics, 132(3), 767–784.
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021). Using machine learning approaches for multi-omics data analysis: A review. Biotechnology Advances, 49, 107739.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144).
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
Ryu, K. H., Huang, L., Kang, H. M., & Schiefelbein, J. (2019). Single-cell RNA sequencing resolves molecular relationships among individual plant cells. Plant Physiology, 179(4), 1444–1456.
Shahan, R., Hsu, C. W., Nolan, T. M., Cole, B. J., Taylor, I. W., Greenstreet, L., … & Ohler, U. (2022). A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants. Developmental Cell, 57(4), 543–560.
Shinozaki, Y., Nicolas, P., Fernandez-Pozo, N., Ma, Q., Evanich, D. J., Shi, Y., … & Rose, J. K. C. (2018). High-resolution spatiotemporal transcriptome mapping of tomato fruit development and ripening. Nature Communications, 9(1), 364.
Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A. (2018). Deep learning for plant stress phenotyping: Trends and future perspectives. Trends in Plant Science, 23(10), 883–898.
Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2), 110–124.
Tan, J., Hammond, J. H., Hogan, D. A., & Greene, C. S. (2016). ADAGE-based integration of publicly available Pseudomonas aeruginosa gene expression data with denoising autoencoders illuminates microbe–host interactions. mSystems, 1(1), e00128–15.
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., … & Burge, C. B. (2008). Alternative isoform regulation in human tissue transcriptomes. Nature, 456(7221), 470–476.
Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63.
Wingett, S. W., & Andrews, S. (2018). FastQ Screen: A tool for multi-genome mapping and quality control. F1000Research, 7, 1338.
Wolfe, C. J., Kohane, I. S., & Butte, A. J. (2005). Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinformatics, 6, 227.
Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K., … & Frey, B. J. (2015). The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218), 1254806.
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology, 16(5), 284–287.
Zhou, H., Hu, Y., Zheng, Y., Li, J., Peng, J., Hu, J., … & Wang, Z. (2024). A foundation language model to decipher diverse regulation of RNAs. bioRxiv, 2024–10.
Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10), 931–934.
Zhu, X., & Goldberg, A. (2009). Introduction to semi-supervised learning. Morgan & Claypool publishing.

Details

Primary Language

English

Subjects

Precision Agriculture Technologies, Agricultural Engineering (Other)

Journal Section

Review

Authors

Emine Dilşat Yeğenoğlu ^*
Türkiye

Publication Date

December 31, 2025

Submission Date

November 18, 2025

Acceptance Date

December 17, 2025

Published in Issue

Year 2025 Volume: 2 Number: 40

DOI

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

IZ

https://izlik.org/JA84GC96DN

APA

Yeğenoğlu, E. D. (2025). Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications. Soma Meslek Yüksekokulu Teknik Bilimler Dergisi, 2(40), 16-29. https://doi.org/10.47118/somatbd.1826014

AMA

1.Yeğenoğlu ED. Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications. Soma MYO Teknik Bilimler Dergisi. 2025;2(40):16-29. doi:10.47118/somatbd.1826014

Chicago

Yeğenoğlu, Emine Dilşat. 2025. “Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications”. Soma Meslek Yüksekokulu Teknik Bilimler Dergisi 2 (40): 16-29. https://doi.org/10.47118/somatbd.1826014.

EndNote

Yeğenoğlu ED (December 1, 2025) Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications. Soma Meslek Yüksekokulu Teknik Bilimler Dergisi 2 40 16–29.

IEEE

[1]E. D. Yeğenoğlu, “Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications”, Soma MYO Teknik Bilimler Dergisi, vol. 2, no. 40, pp. 16–29, Dec. 2025, doi: 10.47118/somatbd.1826014.

ISNAD

Yeğenoğlu, Emine Dilşat. “Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications”. Soma Meslek Yüksekokulu Teknik Bilimler Dergisi 2/40 (December 1, 2025): 16-29. https://doi.org/10.47118/somatbd.1826014.

JAMA

1.Yeğenoğlu ED. Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications. Soma MYO Teknik Bilimler Dergisi. 2025;2:16–29.

MLA

Yeğenoğlu, Emine Dilşat. “Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications”. Soma Meslek Yüksekokulu Teknik Bilimler Dergisi, vol. 2, no. 40, Dec. 2025, pp. 16-29, doi:10.47118/somatbd.1826014.

Vancouver

1.Emine Dilşat Yeğenoğlu. Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications. Soma MYO Teknik Bilimler Dergisi. 2025 Dec. 1;2(40):16-29. doi:10.47118/somatbd.1826014

Yapay Zeka Tabanlı RNA Dizi Analizi: Algoritmalar ve Uygulamalar

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

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

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite