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A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification

BACKGROUND: Glycosylation is an important modification to proteins that plays a significant role in biological processes. Glycan structures are characterized by liquid chromatography (LC) combined with mass spectrometry (MS), but data interpretation of LC/MS and MS/MS data can be time-consuming and...

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Autores principales: Dhingra, Ashna, Schaeffer, Zayla, Majewska Nepomuceno, Natalia I., Au, Jennifer, Ahn, Joomi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276461/
https://www.ncbi.nlm.nih.gov/pubmed/37330473
http://dx.doi.org/10.1186/s12859-023-05346-5
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author Dhingra, Ashna
Schaeffer, Zayla
Majewska Nepomuceno, Natalia I.
Au, Jennifer
Ahn, Joomi
author_facet Dhingra, Ashna
Schaeffer, Zayla
Majewska Nepomuceno, Natalia I.
Au, Jennifer
Ahn, Joomi
author_sort Dhingra, Ashna
collection PubMed
description BACKGROUND: Glycosylation is an important modification to proteins that plays a significant role in biological processes. Glycan structures are characterized by liquid chromatography (LC) combined with mass spectrometry (MS), but data interpretation of LC/MS and MS/MS data can be time-consuming and arduous when analyzed manually. Most of glycan analysis requires dedicated glycobioinformatics tools to process MS data, identify glycan structure, and display the results. However, software tools currently available are either too costly or heavily focused on academic applications, limiting their use within the biopharmaceutical industry for implementing the standardized LC/MS glycan analysis in high-throughput manner. Additionally, few tools provide the capability to generate report-ready annotated MS/MS glycan spectra. RESULTS: Here, we present a MATLAB-based app, GlyKAn AZ, which can automate data processing, glycan identification, and customizable result displays in a streamlined workflow. MS1 and MS2 mass search algorithms along with glycan databases were developed to confirm the fluorescent labeled N-linked glycan species based on accurate mass. A user-friendly graphical user interface (GUI) streamlines the data analysis process, making it easy to implement the software tool in biopharmaceutical analytical laboratories. The databases provided with the app can be expanded through the Fragment Generator functionality which automatically identifies fragmentation patterns for new glycans. The GlyKAn AZ app can automatically annotate the MS/MS spectra, yet this data display feature remains flexible and customizable by users, saving analysts’ time in generating individual report-ready spectra figures. This app accepts both OrbiTrap and matrix-assisted laser desorption/ionization–time of flight (MALDI–TOF) MS data and was successfully validated by identifying all glycan species that were previously identified manually. CONCLUSIONS: The GlyKAn AZ app was developed to expedite glycan analysis while maintaining a high level of accuracy in positive identifications. The app’s customizable user inputs, polished figures and tables, and unique calculated outputs set it apart from similar software and greatly improve the current manual analysis workflow. Overall, this app serves as a tool for streamlining glycan identification for both academic and industrial needs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05346-5.
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spelling pubmed-102764612023-06-18 A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification Dhingra, Ashna Schaeffer, Zayla Majewska Nepomuceno, Natalia I. Au, Jennifer Ahn, Joomi BMC Bioinformatics Software BACKGROUND: Glycosylation is an important modification to proteins that plays a significant role in biological processes. Glycan structures are characterized by liquid chromatography (LC) combined with mass spectrometry (MS), but data interpretation of LC/MS and MS/MS data can be time-consuming and arduous when analyzed manually. Most of glycan analysis requires dedicated glycobioinformatics tools to process MS data, identify glycan structure, and display the results. However, software tools currently available are either too costly or heavily focused on academic applications, limiting their use within the biopharmaceutical industry for implementing the standardized LC/MS glycan analysis in high-throughput manner. Additionally, few tools provide the capability to generate report-ready annotated MS/MS glycan spectra. RESULTS: Here, we present a MATLAB-based app, GlyKAn AZ, which can automate data processing, glycan identification, and customizable result displays in a streamlined workflow. MS1 and MS2 mass search algorithms along with glycan databases were developed to confirm the fluorescent labeled N-linked glycan species based on accurate mass. A user-friendly graphical user interface (GUI) streamlines the data analysis process, making it easy to implement the software tool in biopharmaceutical analytical laboratories. The databases provided with the app can be expanded through the Fragment Generator functionality which automatically identifies fragmentation patterns for new glycans. The GlyKAn AZ app can automatically annotate the MS/MS spectra, yet this data display feature remains flexible and customizable by users, saving analysts’ time in generating individual report-ready spectra figures. This app accepts both OrbiTrap and matrix-assisted laser desorption/ionization–time of flight (MALDI–TOF) MS data and was successfully validated by identifying all glycan species that were previously identified manually. CONCLUSIONS: The GlyKAn AZ app was developed to expedite glycan analysis while maintaining a high level of accuracy in positive identifications. The app’s customizable user inputs, polished figures and tables, and unique calculated outputs set it apart from similar software and greatly improve the current manual analysis workflow. Overall, this app serves as a tool for streamlining glycan identification for both academic and industrial needs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05346-5. BioMed Central 2023-06-17 /pmc/articles/PMC10276461/ /pubmed/37330473 http://dx.doi.org/10.1186/s12859-023-05346-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Dhingra, Ashna
Schaeffer, Zayla
Majewska Nepomuceno, Natalia I.
Au, Jennifer
Ahn, Joomi
A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification
title A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification
title_full A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification
title_fullStr A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification
title_full_unstemmed A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification
title_short A MATLAB-based app to improve LC–MS/MS data analysis for N-linked glycan peak identification
title_sort matlab-based app to improve lc–ms/ms data analysis for n-linked glycan peak identification
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276461/
https://www.ncbi.nlm.nih.gov/pubmed/37330473
http://dx.doi.org/10.1186/s12859-023-05346-5
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