| Texto completo | |
| Autor(es): |
Cardoso, Silvana C. S.
;
Domingues, Douglas S.
;
Paschoal, Alexandre R.
;
Fischer, Carlos N.
;
Cerri, Ricardo
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, CIBCB 2024; v. N/A, p. 8-pg., 2024-01-01. |
| Resumo | |
Transposable Elements (TEs) are genetic sequences that can relocate within the genome, promoting genetic diversity. In eukaryotes, TEs are classified into classes, subclasses, orders, superfamilies, families, and subfamilies. LTR retrotransposons (LTR-RT) constitute an order in this taxonomy. The main objective of this study is to investigate the classification of LTR retrotransposons at the superfamily level. Predictive Bi-Clustering Trees (PBCTs) were used to predict interactions between LTR-RT sequences and conserved protein domains to achieve this. Two datasets were used to investigate the relationships among different superfamilies. The first dataset contained LTR retrotransposon sequences assigned to Copia, Gypsy, and Bel-Pao superfamilies, while the second dataset included consensus sequences of the conserved domains for each superfamily. Thus, the PBCT decision tree tests could relate to the LTR-RT sequence and conserved domain attributes. In the classification process, interaction is interpreted as either the presence or absence of a domain in a given LTR-RT sequence. The sequence is then classified into the superfamily with the most predicted domains. Precision-recall curves were adopted as evaluation metrics for the method, and its performance was compared to some of the most commonly used models in the task of transposable element classification. The experiments conducted on D. melanogaster and A. thaliana showed that PBCTs are promising and comparable to other methods, especially in classifying the Gypsy superfamily. (AU) | |
| Processo FAPESP: | 22/02981-8 - Detecção de novidade em fluxos contínuos de dados multirrótulo |
| Beneficiário: | Ricardo Cerri |
| Modalidade de apoio: | Auxílio à Pesquisa - Projeto Inicial |