Proteome2virus: Shotgun mass spectrometry data analysis pipeline for virus identification
Shotgun proteomics is a generic method enabling detection of multiple viral species in one assay. The reliable and accurate identification of these viral species by analyzing peptides from MS-spectra is a challenging task. The aim of this study was to develop an easy accessible proteome analysis app...
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Published in: | Journal of clinical virology plus Vol. 3; no. 2; p. 100147 |
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Main Authors: | , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier Ltd
01-06-2023
Elsevier |
Subjects: | |
Online Access: | Get full text |
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Summary: | Shotgun proteomics is a generic method enabling detection of multiple viral species in one assay. The reliable and accurate identification of these viral species by analyzing peptides from MS-spectra is a challenging task. The aim of this study was to develop an easy accessible proteome analysis approach for the identification of viruses that cause respiratory and gastrointestinal infections.
For this purpose, a shotgun proteomics based method and a web application, ‘proteome2virus’, were developed. Identified peptides were searched in a database comprising proteomic data of 46 viruses known to be infectious to humans.
The method was successfully tested for cultured viruses and eight fecal samples consisting of ten different viral species from seven different virus families, including SARS-CoV-2. The samples were prepared with two different sample preparation methods and were measured with two different mass spectrometers.
The results demonstrate that the developed web application is applicable to different MS data sets, generated from two different instruments, and that with this approach a high variety of clinically relevant viral species can be identified. This emphasizes the potential and feasibility for the diagnosis of a wide range of viruses in clinical samples with a single shotgun proteomics analysis. |
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ISSN: | 2667-0380 2667-0380 |
DOI: | 10.1016/j.jcvp.2023.100147 |