@article{Mehta_Carvalho_Rajczewski-Catch_the_Wave-2022,
author = {Mehta, Subina and Carvalho, Valdemir M. and Rajczewski, 
          Andrew T. and Pible, Olivier and Grüning, Björn A. and 
          Johnson, James E. and Wagner, Reid and Armengaud, Jean and 
          Griffin, Timothy J. and Jagtap, Pratik D.},
title = {Catching the {Wave}: {Detecting} {Strain}-{Specific} 
         {SARS}-{CoV}-2 {Peptides} in {Clinical} {Samples} 
         {Collected} during {Infection} {Waves} from {Diverse} 
         {Geographical} {Locations}},
journal = {Viruses},
year = {2022},
doi = {10.3390/v14102205},
volume = {14},
user = {backofen},
pmid = {36298760},
pages = {},
number = {10},
issn = {1999-4915},
abstract = {The Coronavirus disease 2019 (COVID-19) pandemic caused by 
            the severe acute respiratory syndrome coronavirus 2 
            (SARS-CoV-2) resulted in a major health crisis worldwide 
            with its continuously emerging new strains, resulting in new 
            viral variants that drive "waves" of infection. PCR or 
            antigen detection assays have been routinely used to detect 
            clinical infections; however, the emergence of these newer 
            strains has presented challenges in detection. One of the 
            alternatives has been to detect and characterize 
            variant-specific peptide sequences from viral proteins using 
            mass spectrometry (MS)-based methods. MS methods can 
            potentially help in both diagnostics and vaccine development 
            by understanding the dynamic changes in the viral proteome 
            associated with specific strains and infection waves. In 
            this study, we developed an accessible, flexible, and 
            shareable bioinformatics workflow that was implemented in 
            the Galaxy Platform to detect variant-specific peptide 
            sequences from MS data derived from the clinical samples. We 
            demonstrated the utility of the workflow by characterizing 
            published clinical data from across the world during various 
            pandemic waves. Our analysis identified six SARS-CoV-2 
            variant-specific peptides suitable for confident detection 
            by MS in commonly collected clinical samples.}
}