Identification of SARS-CoV-2 origin: Using Ngrams, principal component analysis and Random Forest algorithm

• Main Authors: Hamoucha El Boujnouni, Mohamed Rahouti, Mohamed El Boujnouni
• Format: Article
• Language: English
• Published: Elsevier 2021-01-01
• Series: Informatics in Medicine Unlocked
• Subjects: Bioinformatics; Genomes; SARS-CoV-2; COVID-19; Ngrams; Principal component analysis
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352914821000678

COVID-19 is an infectious disease caused by the newly discovered SARS-CoV-2 virus. This virus causes a respiratory tract infection, symptoms include dry cough, fever, tiredness and in more severe cases, breathing difficulty. SARS-CoV-2 is an extremely contagious virus that is spreading rapidly all over the world and the scientific community is working tirelessly to find an effective treatment. This paper aims to determine the origin of this virus by comparing its nucleic acid sequence with all members of the coronaviridae family. This study uses a new approach based on the combination of three powerful techniques which are: Ngrams (For text categorization), Principal Component Analysis (For dimensionality reduction) and Random Forest algorithm (For supervised classification). The experimental results have shown that a large set of SARS-CoV-2 genomes, collected from different locations around the world, present significant similarities to those found in pangolins. This finding confirms some previous results obtained by other methods, which also suggest that pangolins should be considered as possible hosts in the emergence of the new coronavirus.