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Volume 20, Issue 6
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

The human genome is densely populated with repetitive DNA sequences that play crucial roles in genomic functions and structures but are also implicated in over 40 human diseases. The computational challenge of identifying and characterizing these repeats is significant due to the complexity and size of the genome, which are overwhelming traditional algorithms.

Methods

To address these challenges, we propose GenRepAI, a deep learning framework to navigate and analyze genomic suffix trees. GenRepAI employs supervised machine learning classifiers trained on labeled datasets of repeat annotations and unsupervised anomaly detection to identify novel repeat sequences. The models are trained using convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and vision transformers to classify and annotate repeats within the human genome.

Results

GenRepAI is designed to comprehensively profile repeats that underlie various neurological diseases, allowing researchers to identify pathogenic expansions. The framework will integrate into existing genomic analysis pipelines, with the capability to screen patient genomes and highlight potential causal variants for further validation.

Conclusion

GenRepAI is set to become a foundational tool in genomics, leveraging artificial intelligence to enhance the characterization of repetitive sequences. It promises significant advancements in the molecular diagnosis of repeat expansion disorders and contributes to a deeper understanding of genomic structure and function, with broad applications in personalized medicine.

© 2025 Bentham Science Publishers
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2025-07-01
2025-05-28

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/content/journals/cbio/10.2174/0115748936303435240702112205
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GenRepAI: Utilizing Artificial Intelligence to Identify Repeats in Genomic Suffix Trees
Curr Bioinform 20, 522 (2025); https://doi.org/10.2174/0115748936303435240702112205
/content/journals/cbio/10.2174/0115748936303435240702112205
/content/journals/cbio/10.2174/0115748936303435240702112205
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  • Article Type:     Research Article
Keyword(s): artificial intelligence; deep learning framework; Genomic suffix trees; genomics; molecular diagnosis; repetitive DNA sequences

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