Towards Efficient and Accurate SARS-CoV-2 Genome Sequence Typing Based on Supervised Learning Approaches

Abstract

Despite the active development of SARS-CoV-2 surveillance methods (e.g., Nextstrain, GISAID, Pangolin), the global emergence of various SARS-CoV-2 viral lineages that potentially cause antiviral and vaccine failure has driven the need for accurate and efficient SARS-CoV-2 genome sequence classifiers. This study presents an optimized method that accurately identifies the viral lineages of SARS-CoV-2 genome sequences using existing schemes. For Nextstrain and GISAID clades, a template matching-based method is proposed to quantify the differences between viral clades and to play an important role in classification evaluation. Furthermore, to improve the typing accuracy of SARS-CoV-2 genome sequences, an ensemble model that integrates a combination of machine learning-based methods (such as Random Forest and Catboost) with optimized weights is proposed for Nextstrain, Pangolin, and GISAID clades. Cross-validation is applied to optimize the parameters of the machine learning-based method and the weight settings of the ensemble model. To improve the efficiency of the model, in addition to the one-hot encoding method, we have proposed a nucleotide site mutation-based data structure that requires less computational resources and performs better in SARS-CoV-2 genome sequence typing. Based on an accumulated database of >1 million SARS-CoV-2 genome sequences, performance evaluations show that the proposed system has a typing accuracy of 99.879%, 97.732%, and 96.291% for Nextstrain, Pangolin, and GISAID clades, respectively. A single prediction only takes an average of <20 ms on a portable laptop. Overall, this study provides an efficient and accurate SARS-CoV-2 genome sequence typing system that benefits current and future surveillance of SARS-CoV-2 variants.

Epidemiological and clinical features of COVID-19 inpatients in Changsha, China: A retrospective study from 2020 to 2022

“…Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally and caused a significant global health crisis [ 1 ], but its treatment strategies are still under development [ 2 , 3 ]. Since the high prevalence and infectivity of SARS-CoV-2, massive viral variants have been detected using the whole genome sequencing [ 4 , 5 ]. However, most of the adaptive mutations in the SARS-CoV-2 genome have caused no phenotypic effects and only a few variants result in significant phenotypic changes, including those defined as variants of concern (VOCs): Alpha (B.1.1.7) [ 6 ], Beta (B.1.351) [ 7 ], Gamma (P.1) [ 8 ], Delta (B.1.617.2 and AY lineages) [ 9 ], and Omicron (B.1.1.529 or BA lineages) [ 10 ].…”