CRISPRcasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems

Autor(es)

Padilha, Victor A. ; Alkhnbashi, Omer S. ; Shah, Shiraz A. ; de Carvalho, Andre C. P. L. F. ; Backofen, Rolf

• Tipo de documento: Artigo Científico
• Fonte: GIGASCIENCE; v. 9, n. 6, p. 12-pg., 2020-06-01.

Resumo

Background: CRISPR-Cas genes are extraordinarily diverse and evolve rapidly when compared to other prokaryotic genes. With the rapid increase in newly sequenced archaeal and bacterial genomes, manual identification of CRISPR-Cas systems is no longer viable. Thus, an automated approach is required for advancing our understanding of the evolution and diversity of these systems and for finding new candidates for genome engineering in eukaryotic models. Results: We introduce CRISPRcasIdentifier, a new machine learning-based tool that combines regression and classification models for the prediction of potentially missing proteins in instances of CRISPR-Cas systems and the prediction of their respective subtypes. In contrast to other available tools, CRISPRcasIdentifier can both detect cas genes and extract potential association rules that reveal functional modules for CRISPR-Cas systems. In our experimental benchmark on the most recently published and comprehensive CRISPR-Cas system dataset, CRISPRcasIdentifier was compared with recent and state-of-the-art tools. According to the experimental results, CRISPRcasIdentifier presented the best Cas protein identification and subtype classification performance. Conclusions: Overall, our tool greatly extends the classification of CRISPR cassettes and, for the first time, predicts missing Cas proteins and association rules between Cas proteins. Additionally, we investigated the properties of CRISPR subtypes. The proposed tool relies not only on the knowledge of manual CRISPR annotation but also on models trained using machine learning. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 19/21300-9 - Ferramentas de aprendizado de máquina para problemas de bioinformática
• Beneficiário: Victor Alexandre Padilha
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE