Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning Article Swipe
Jakub Horvath
,
Pavel Jedlička
,
Marie Krátká
,
Zdeněk Kubát
,
Eduard Kejnovský
,
Matej Lexa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.06.11.598549
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.06.11.598549
Background Long terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key compounds, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs. Results We used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting - GBC), (ii) hybrid CNN-LSTM models, and (iii) a pre-trained transformer-based model (DNABERT) using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the CNN-LSTM hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. SHAP positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box, and TG..CA patterns around both LTR edges. Conclusions Our results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5’- and 3’-edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.
Related Topics
Concepts
Retrotransposon
Long terminal repeat
Artificial intelligence
Computational biology
Genome
Biology
Computer science
Machine learning
Random forest
Genetics
Gene
Transposable element
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.06.11.598549
- https://www.biorxiv.org/content/biorxiv/early/2024/06/14/2024.06.11.598549.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399709512
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399709512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.06.11.598549Digital Object Identifier
- Title
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Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learningWork title
- Type
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-06-14Full publication date if available
- Authors
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Jakub Horvath, Pavel Jedlička, Marie Krátká, Zdeněk Kubát, Eduard Kejnovský, Matej LexaList of authors in order
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https://doi.org/10.1101/2024.06.11.598549Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/06/14/2024.06.11.598549.full.pdfDirect link to full text PDF
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/06/14/2024.06.11.598549.full.pdfDirect OA link when available
- Concepts
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Retrotransposon, Long terminal repeat, Artificial intelligence, Computational biology, Genome, Biology, Computer science, Machine learning, Random forest, Genetics, Gene, Transposable elementTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 168, 187, 204 |
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| abstract_inverted_index.0.85 | 180 |
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| abstract_inverted_index.valuable | 162 |
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| abstract_inverted_index.(F1=0.89) | 195 |
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| abstract_inverted_index.analysis. | 211 |
| abstract_inverted_index.centrally | 239 |
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| abstract_inverted_index.subjected | 208 |
| abstract_inverted_index.(F1=0.74). | 203 |
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