2024
Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning
HORVÁTH, Jakub; Pavel JEDLIČKA; Marie KRÁTKÁ; Zdeněk KUBÁT; Eduard KEJNOVSKÝ et al.Základní údaje
Originální název
Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning
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Vydání
BIODATA MINING, ENGLAND, SpringerNature, 2024, 1756-0381
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Velká Británie a Severní Irsko
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 6.100
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14330/24:00137851
Organizační jednotka
Fakulta informatiky
UT WoS
EID Scopus
Klíčová slova anglicky
Eukaryote;Repeat;Transposable elements;Deep learning;CNN-LSTM;DNABERT;Sequence analysis;Regulatory mechanisms;Transcription factor binding sites;TFBS;SHAP score
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 26. 8. 2025 14:01, Mgr. Marie Novosadová Šípková, DiS.
Anotace
V originále
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 sequence modules, 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), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model 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 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. 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 regulatory sequence, and TG..CA nucleotide 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.
Návaznosti
| CZ.02.01.01/00/22_008/0004581, interní kód MU |
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| EH22_008/0004581, projekt VaV |
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| GA21-00580S, projekt VaV |
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| GA24-11400S, projekt VaV |
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| MUNI/A/1642/2023, interní kód MU |
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