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10.1007/s00216-021-03621-z http://scihub22266oqcxt.onion/10.1007/s00216-021-03621-z 34448030!8390178!34448030     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34448030&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Anal+Bioanal+Chem 2021 ; 413 (29): 7215-7227 Nephropedia Template TP {{tp|p=34448030|t=2021. The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein |pdf=|usr=}}{{34448030}} gab.com Text The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein JO: Anal Bioanal Chem http://www.kidney.de/pget.php?&tw=1&pmid=34448030 #FOAvip Glycosylation analysis of viral glycoproteins contributes significantly to vaccine design and development. Among other benefits, glycosylation analysis allows vaccine developers to assess the impact of construct design or producer cell line choices for vaccine production, and it is a key measure by which glycoproteins that are produced for use in vaccination can be compared to their native viral forms. Because many viral glycoproteins are multiply glycosylated, glycopeptide analysis is a preferrable approach for mapping the glycans, yet the analysis of glycopeptide data can be cumbersome and requires the expertise of an experienced analyst. In recent years, a commercial software product, Byonic, has been implemented in several instances to facilitate glycopeptide analysis on viral glycoproteins and other glycoproteomics data sets, and the purpose of the study herein is to determine the strengths and limitations of using this software, particularly in cases relevant to vaccine development. The glycopeptides from a recombinantly expressed trimeric S glycoprotein of the SARS-CoV-2 virus were first analyzed using an expert-based analysis strategy; subsequently, analysis of the same data set was completed using Byonic. Careful assessment of instances where the two methods produced different results revealed that the glycopeptide assignments from Byonic contained more false positives than true positives, even when the data were assessed using a 1% false discovery rate. The work herein provides a roadmap for removing the spurious assignments that Byonic generates, and it provides an assessment of the opportunity cost for relying on automated assignments for glycopeptide data sets from viral glycoproteins. Twit Text FOAVip The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein ????Anal Bioanal Chem http://www.kidney.de/pget.php?&tw=1&pmid=34448030 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34448030 #FOAvip English Wikipedia <ref>{{Cite pmid|34448030}}</ref> The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein #MMPMID34448030 Go EP; Zhang S; Ding H; Kappes JC; Sodroski J; Desaire H Anal Bioanal Chem 2021[Dec]; 413 (29): 7215-7227 PMID34448030show ga Glycosylation analysis of viral glycoproteins contributes significantly to vaccine design and development. Among other benefits, glycosylation analysis allows vaccine developers to assess the impact of construct design or producer cell line choices for vaccine production, and it is a key measure by which glycoproteins that are produced for use in vaccination can be compared to their native viral forms. Because many viral glycoproteins are multiply glycosylated, glycopeptide analysis is a preferrable approach for mapping the glycans, yet the analysis of glycopeptide data can be cumbersome and requires the expertise of an experienced analyst. In recent years, a commercial software product, Byonic, has been implemented in several instances to facilitate glycopeptide analysis on viral glycoproteins and other glycoproteomics data sets, and the purpose of the study herein is to determine the strengths and limitations of using this software, particularly in cases relevant to vaccine development. The glycopeptides from a recombinantly expressed trimeric S glycoprotein of the SARS-CoV-2 virus were first analyzed using an expert-based analysis strategy; subsequently, analysis of the same data set was completed using Byonic. Careful assessment of instances where the two methods produced different results revealed that the glycopeptide assignments from Byonic contained more false positives than true positives, even when the data were assessed using a 1% false discovery rate. The work herein provides a roadmap for removing the spurious assignments that Byonic generates, and it provides an assessment of the opportunity cost for relying on automated assignments for glycopeptide data sets from viral glycoproteins. |Algorithms[MESH] |Amino Acid Sequence[MESH] |Chromatography, Liquid/methods[MESH] |Glycopeptides/*metabolism[MESH] |Spike Glycoprotein, Coronavirus/chemistry/*metabolism[MESH]The opportunity cost of automated glycopeptide analysis: case study pr(epmc).pdf DeepDyve Pubget Overpricing