MassIVE MSV000086175

Imported Reanalysis Dataset Public PXD021388

Detection of SARS-CoV-2 in nasal swabs using MALDI-MS

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Description

Detection of SARS-CoV-2 using RT-PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT-PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples. [doi:10.25345/C56F4C] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: COVID-19 ; Sars-cov-2

Contact

Principal Investigators:
(in alphabetical order) 		Leonardo Santos, Universidad de Talca, Chile
Submitting User: mwang87

Publications

Nachtigall FM, Pereira A, Trofymchuk OS, Santos LS.
Detection of SARS-CoV-2 in nasal swabs using MALDI-MS.
Nat Biotechnol. 2020 Oct;38(10):1168-1173. Epub 2020 Jul 30.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.