Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

Abstract

One of the reasons for the fast spread of SARS-CoV-2 is the lack of accuracy in detection tools in the clinical field. Molecular techniques, such as quantitative real-time RT-PCR and nucleic acid sequencing methods, are widely used to identify pathogens. For this particular virus, however, they have an overall unsatisfying detection rate, due to its relatively recent emergence and still not completely understood features. In addition, SARS-CoV-2 is remarkably similar to other Coronaviruses, and it can present with other respiratory infections, making identification even harder. To tackle this issue, we propose an assisted detection test, combining molecular testing with deep learning. The proposed approach employs a state-of-the-art deep convolutional neural network, able to automatically create features starting from the genome sequence of the virus.Experiments on data from the Novel Coronavirus Resource (2019nCoVR) show that the proposed approach is able to correctly classify SARS-CoV-2, distinguishing it from other coronavirus strains, such as MERS-CoV, HCoV-NL63, HCoV-OC43, HCoV-229E, HCoV-HKU1, and SARS-CoV regardless of missing information and errors in sequencing (noise). From a dataset of 553 complete genome non-repeated sequences that vary from 1,260 to 31,029 bps in length, the proposed approach classifies the different coronaviruses with an average ac-: bioRxiv preprint curacy of 98.75% in a 10-fold cross-validation, identifying SARS-CoV-2 with an AUC of 98%, specificity of 0.9939 and sensitivity of 1.00 in a binary classification. Then, using the same basis, we classify SARS-CoV-2 from 384 complete viral genome sequences with human host, that contain the gene ORF1ab from the NCBI with a 10-fold accuracy of 98.17% , a specificity of 0.9797 and sensitivity of 1.00. Furthermore, an in-depth analysis of the results allow us to identify base pairs sequences that are unique to SARS-CoV-2 and do not appear in other virus strains, that could then be used as a base for designing new primers and examined by experts to extract further insights. These preliminary results seem encouraging enough to identify deep learning as a promising research venue to develop assisted detection tests for SARS-CoV-2. At this end the interaction between viromics and deep learning, will hopefully help to solve global infection problems. In addition, we offer our code and processed data to be used for diagnostic purposes by medical doctors, virologists and scientists involved in solving the SARS-CoV-2 pandemic. As more data become available we will update our system.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

“…Of 2696 titles, 85 studies were retained for abstract and full text screening. Twenty seven studies describing 31 prediction models met the inclusion criteria and were selected for data extraction and critical appraisal 789101112181920212223242526272829303132333435363738…”

“…Twenty five studies used data on patients with covid-19 from China (supplementary table 1), one study used data on patients from Italy,31 and one study used international data (United States, United Kingdom, and China, among others) 35. Based on 18 of the 25 studies that reported study dates, data were collected between 8 December 2019 and 15 March 2020.…”

“…Five studies made their source code available on GitHub 811343538. Ten studies did not include any usable equation, format, or reference for use or validation of their prediction model.…”

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

“…The guarantors had full access to all the data in the study, take responsibility for the integrity of the data and the accuracy of the data analysis, and had final responsibility for the decision to submit for publication. 35 unclear low low high Meng, Wang, et al 12 high low high high Song, Xu, et al 30 high unclear low high Yu, Shao, et al 24 unclear unclear unclear high…”

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

“…To further improve the accuracy of COVID-19 detection, the authors in [84] leverage a deep convolutional neural network that is able to automatically extract features starting from the genome sequence of the virus. Experiments using the 2019nCoVR dataset show a correct classification performance in detecting SARS-CoV-2, and distinguishing it from other coronavirus strains, such as MERS-CoV, SARS-CoV.…”