RCOVID19: Recurrence-based SARS-CoV-2 features using chaos game representation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. It was first detected in China and was rapidly spread to other countries. Several thousands of whole genome sequences of SARS-CoV-2 have been reported and it is important to compare them and identi...

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Detalles Bibliográficos
Autores principales: Olyaee, Mohammad Hossein, Pirgazi, Jamshid, Khalifeh, Khosrow, Khanteymoori, Alireza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Biochemistry, Genetics and Molecular Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411429/
https://www.ncbi.nlm.nih.gov/pubmed/32835040
http://dx.doi.org/10.1016/j.dib.2020.106144
Descripción
Sumario:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. It was first detected in China and was rapidly spread to other countries. Several thousands of whole genome sequences of SARS-CoV-2 have been reported and it is important to compare them and identify distinctive evolutionary/mutant markers. Utilizing chaos game representation (CGR) as well as recurrence quantification analysis (RQA) as a powerful nonlinear analysis technique, we proposed an effective process to extract several valuable features from genomic sequences of SARS-CoV-2. The represented features enable us to compare genomic sequences with different lengths. The provided dataset involves totally 18 RQA-based features for 4496 instances of SARS-CoV-2.

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