Transposable Elements

BONNET, Amandine; CASIER, Karine; CARRÉ, Clément; TEYSSET, Laure; LESAGE, Pascale

doi:10.1002/9781394264902.ch3

Function and Evolution of Repeated DNA Sequences 2023

DOI: 10.1002/9781394264902.ch3

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Transposable Elements

Amandine BONNET,

Karine CASIER,

Clément CARRÉ

et al.

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2024

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Cited by 1 publication

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NeuralTE: an accurate approach for Transposable Element superfamily classification with multi-feature fusion

Hu,

Xu,

Gao

et al. 2024

Preprint

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Motivation: Classifying Transposable Elements (TEs) at the superfamily level offers deeper insights into species variation and evolution. Recent advancements in third-generation sequencing technologies have made a large number of genomes from non-model species becoming available. However, existing TE classification methods suffer from several limitations, including the necessity to train multiple hierarchical classification models, the incapacity to perform classification at the superfamily level, and deficiencies in both accuracy and robustness. Therefore, there is an urgent need for an accurate TE classification method to improve genome annotation. Results: In this study, we develop NeuralTE, a deep learning method designed to classify transposons at the superfamily level. To achieve accurate TE classification, we identify various structural features of transposons, and use different combinations of k-mers for terminal repeats and internal sequences to uncover distinct patterns. Evaluation on all transposons from Repbase shows that NeuralTE outperforms existing deep learning, machine learning, and homology-based methods in classifying TEs. Testing on the transposons from novel species highlights the superior performance of NeuralTE compared to existing methods, achieving an F1-score of 0.8903, a 7.67% improvement over the state-of-the-art method RepeatClassifier. We also conduct TE annotation experiments on rice using different classification tools, and the results show that NeuralTE achieves annotations nearly identical to the gold standard, highlighting its robustness and accuracy in classifying transposons.

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NeuralTE: an accurate approach for Transposable Element superfamily classification with multi-feature fusion

Hu,

Xu,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

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Transposable Elements

Cited by 1 publication

References 287 publications

NeuralTE: an accurate approach for Transposable Element superfamily classification with multi-feature fusion

NeuralTE: an accurate approach for Transposable Element superfamily classification with multi-feature fusion

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