Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences
Juliana Carneiro Gomes, Aras Ismael Masood, Leandro Honorato de S. Silva, Janderson Ferreira, Agostinho Freire, Allana Rocha, Letícia Castro Portela de Oliveira, Nathália Regina Cauás da Silva, Bruno José Torres Fernandes, Wellington Pinheiro dos Santos
Abstract
The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the rapid depletion of public health resources in most countries. RT-PCR is Covid-19's reference diagnostic method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach and represented by co-occurrence matrices. This technique eliminates multiple sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. When comparing SARS-CoV-2 with virus families with similar symptoms, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP classifier and 30% overlap. When SARS-CoV-2 is compared to other coronaviruses and healthy human DNA sequences, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify DNA sequences for SARS-CoV-2 with greater specificity and sensitivity.