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.
No abstract is provided for this article.
No abstract is provided for this article.
The HIV-1 Rev protein, which traffics through nucleolus and shuttles between nucleus and cytoplasm, facilitates export of unspliced and singly spliced viral transcripts containing RRE RNA by the CRM1 export pathway. Inhibitions of the various stages of Rev-mediated RNA transport can arrest HIV-1 transcriptional process. The current understanding to the mechanism of Rev function, Rev-RRE interaction, as well as inhibitors hereof is reviewed.
