eprintid: 6097 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/60/97 datestamp: 2023-03-01 23:30:12 lastmod: 2023-03-01 23:30:12 status_changed: 2023-03-01 23:30:12 type: article metadata_visibility: show creators_name: Bakkas, Jamal creators_name: Hanine, Mohamed creators_name: Chekry, Abderrahman creators_name: Gounane, Said creators_name: de la Torre Díez, Isabel creators_name: Lipari, Vivian creators_name: Martínez López, Nohora Milena creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: vivian.lipari@uneatlantico.es creators_id: nohora.martinez@uneatlantico.es creators_id: title: SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations ispublished: pub subjects: uneat_bm divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: ontology; genome structure; SARS-CoV-2; mutation; lineage abstract: Mutations allow viruses to continuously evolve by changing their genetic code to adapt to the hosts they infect. It is an adaptive and evolutionary mechanism that helps viruses acquire characteristics favoring their survival and propagation. The COVID-19 pandemic declared by the WHO in March 2020 is caused by the SARS-CoV-2 virus. The non-stop adaptive mutations of this virus and the emergence of several variants over time with characteristics favoring their spread constitute one of the biggest obstacles that researchers face in controlling this pandemic. Understanding the mutation mechanism allows for the adoption of anticipatory measures and the proposal of strategies to control its propagation. In this study, we focus on the mutations of this virus, and we propose the SARSMutOnto ontology to model SARS-CoV-2 mutations reported by Pango researchers. A detailed description is given for each mutation. The genes where the mutations occur and the genomic structure of this virus are also included. The sub-lineages and the recombinant sub-lineages resulting from these mutations are additionally represented while maintaining their hierarchy. We developed a Python-based tool to automatically generate this ontology from various published Pango source files. At the end of this paper, we provide some examples of SPARQL queries that can be used to exploit this ontology. SARSMutOnto might become a ‘wet bench’ machine learning tool for predicting likely future mutations based on previous mutations. date: 2023 publication: Viruses volume: 15 number: 2 pagerange: 505 id_number: doi:10.3390/v15020505 refereed: TRUE issn: 1999-4915 official_url: http://doi.org/10.3390/v15020505 access: open language: en citation: Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Mutations allow viruses to continuously evolve by changing their genetic code to adapt to the hosts they infect. It is an adaptive and evolutionary mechanism that helps viruses acquire characteristics favoring their survival and propagation. The COVID-19 pandemic declared by the WHO in March 2020 is caused by the SARS-CoV-2 virus. The non-stop adaptive mutations of this virus and the emergence of several variants over time with characteristics favoring their spread constitute one of the biggest obstacles that researchers face in controlling this pandemic. Understanding the mutation mechanism allows for the adoption of anticipatory measures and the proposal of strategies to control its propagation. In this study, we focus on the mutations of this virus, and we propose the SARSMutOnto ontology to model SARS-CoV-2 mutations reported by Pango researchers. A detailed description is given for each mutation. The genes where the mutations occur and the genomic structure of this virus are also included. The sub-lineages and the recombinant sub-lineages resulting from these mutations are additionally represented while maintaining their hierarchy. We developed a Python-based tool to automatically generate this ontology from various published Pango source files. At the end of this paper, we provide some examples of SPARQL queries that can be used to exploit this ontology. SARSMutOnto might become a ‘wet bench’ machine learning tool for predicting likely future mutations based on previous mutations. metadata Bakkas, Jamal; Hanine, Mohamed; Chekry, Abderrahman; Gounane, Said; de la Torre Díez, Isabel; Lipari, Vivian; Martínez López, Nohora Milena y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, nohora.martinez@uneatlantico.es, SIN ESPECIFICAR (2023) SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations. Viruses, 15 (2). p. 505. ISSN 1999-4915 document_url: http://repositorio.unic.co.ao/id/eprint/6097/1/viruses-15-00505-v2.pdf