The emergence and rapid spread of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)is a major and urgent threat to global health. Upon infection, patients exhibit several significant symptoms including fever, cough, diarrhea which are more severe in adults with chronic illness, and shortness of breath[1].
Currently, High-throughput sequencing methods have been deployed to identify and classify SARS-CoV-2 variants[2]. However, the design of primers and probes limits these methods to identify only known variants and require continuous update to cater for new species or strains that are clinically relevant. High-throughput sequencing provides an unbiased identification of virus molecules present in samples and can be used for systematic classification of SARS-CoV-2 variants. However, this approach requires large scaled reference databases of variants to compare against resulting to considerable computing requirements. Robust techniques such as machine learning (ML) have shown potential for diagnosing, containing and therapeutic monitoring of many diseases. ML based tools can identify and extract the important sequence features for sequence classification in a computationally efficient manner. Such, have been successfully used previously in solving sequence classification problems and some have been applied in identification and classification of SARS-CoV-2. Accurate classification will eventually reduce the huge burden on the limited health system while providing efficient and timely monitoring of transmission and spread patters of SARS-CoV-2.In this work we intend to employ Convolutional Neural Network machine learning architecture on publicly genomics dataset to classify SARS-CoV-2 variants. CNN models are an advancement of machine learning which are technically advanced due to their automated feature extraction.These methods produce excellent results using the concepts of Natural Language Processing(NLP). CNN has been used extensively for DNA classification,protein structure prediction and specifically viral classification.
To develop a deep learnng model that accurately classifies SARS-CoV-2 variants
COVID-19 has brought with its immense burden to the healthcare system globally. Currently, vaccination is the only available control measure for control and spread of COVID-19. Accurate classification will enable effective monitoring and tracking of SARS-CoV-2 variants and improve current control and management of the pandemic.
Below is a workflow of the methods used in this study
Classification of SARS-Cov-2 variants will make use of publicly available representative spike-protein sequence data downloaded from the GISADI Database.
Python libraries will be used in data processing and exploratory anaysis before feeding to the model. The input features will be the DNA sequence, while the output feature will be the class of SARS-CoV-2 variant. One hot encoding will be used to transform the output feature values into a binary matrix. The dataset will be split into training and testing datasets.
ML algorithms do make use of numerical data and since DNA sequences are in categorical form, some form of conversion using readly available tools will be implemented. In this study, one-hot encoding is used to convert the k-merized sequence data into numerical form.
The resulting dataset is fed into a CNN model for feature extraction and classification. CNN uses convolutionary layers to automatically extract features from a dataset as opposed to other models which require the user to manually extract important features. CNN contains several layers, one input, several hidden and one output layer and each layer contains several neurons and each neuron contains several parameters.
Architecture of the proposed CNN machine learning model.
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