msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level
In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metas...
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creator | Demeulemeester, Nina Gébelin, Marie Caldi Gomes, Lucas Lingor, Paul Carapito, Christine Martens, Lennart Clement, Lieven |
description | In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.
[Display omitted]
•Msqrob2PTM is a novel statistical tool to detect differentially used PTMs & peptidoforms.•PTM abundances are corrected for parent protein abundance by a normalisation strategy.•The workflow is freely available on GitHub.•Tested on different datasets and compared to MSstatsPTM.•Reproducible & transparent workflow applicable to many different experimental designs.
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doi_str_mv | 10.1016/j.mcpro.2023.100708 |
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[Display omitted]
•Msqrob2PTM is a novel statistical tool to detect differentially used PTMs & peptidoforms.•PTM abundances are corrected for parent protein abundance by a normalisation strategy.•The workflow is freely available on GitHub.•Tested on different datasets and compared to MSstatsPTM.•Reproducible & transparent workflow applicable to many different experimental designs.
The era of open-modification search engines in LC-MS/MS-based proteomics has expanded the detection of post-translational modifications (PTMs). However, statistical methods for PTM-level quantification and differential analysis are lacking. To address this, we introduce msqrob2PTM, offering differential usage analysis at the PTM and peptidoform level. The workflow provides an additional normalization to correct for parent protein abundance. Demonstrating efficacy on simulated datasets and PTM-rich biological data, msqrob2PTM outperforms existing methods and uniquely provides output at the peptidoform level. Accessible at https://github.com/statOmics/msqrob2PTMpaper.</description><identifier>ISSN: 1535-9476</identifier><identifier>EISSN: 1535-9484</identifier><identifier>DOI: 10.1016/j.mcpro.2023.100708</identifier><identifier>PMID: 38154689</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>differential abundance ; differential peptidoform analysis ; differential PTM analysis ; differential usage ; mass spectrometry based proteomics ; peptidoform ; posttranslational modification ; proteomics data analysis ; proteomics software</subject><ispartof>Molecular & cellular proteomics, 2024-02, Vol.23 (2), p.100708-100708, Article 100708</ispartof><rights>2023 The Authors</rights><rights>Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>2023 The Authors 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c365t-d5103d58edb451cea154b44549c06ff9f4b7248b0ffe903e678ac5b369c93d873</cites><orcidid>0000-0001-9362-7096 ; 0000-0002-0079-319X ; 0000-0002-8290-2432 ; 0000-0002-9050-4370 ; 0000-0003-4277-658X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10875266/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10875266/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38154689$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Demeulemeester, Nina</creatorcontrib><creatorcontrib>Gébelin, Marie</creatorcontrib><creatorcontrib>Caldi Gomes, Lucas</creatorcontrib><creatorcontrib>Lingor, Paul</creatorcontrib><creatorcontrib>Carapito, Christine</creatorcontrib><creatorcontrib>Martens, Lennart</creatorcontrib><creatorcontrib>Clement, Lieven</creatorcontrib><title>msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level</title><title>Molecular & cellular proteomics</title><addtitle>Mol Cell Proteomics</addtitle><description>In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.
[Display omitted]
•Msqrob2PTM is a novel statistical tool to detect differentially used PTMs & peptidoforms.•PTM abundances are corrected for parent protein abundance by a normalisation strategy.•The workflow is freely available on GitHub.•Tested on different datasets and compared to MSstatsPTM.•Reproducible & transparent workflow applicable to many different experimental designs.
The era of open-modification search engines in LC-MS/MS-based proteomics has expanded the detection of post-translational modifications (PTMs). However, statistical methods for PTM-level quantification and differential analysis are lacking. To address this, we introduce msqrob2PTM, offering differential usage analysis at the PTM and peptidoform level. The workflow provides an additional normalization to correct for parent protein abundance. Demonstrating efficacy on simulated datasets and PTM-rich biological data, msqrob2PTM outperforms existing methods and uniquely provides output at the peptidoform level. Accessible at https://github.com/statOmics/msqrob2PTMpaper.</description><subject>differential abundance</subject><subject>differential peptidoform analysis</subject><subject>differential PTM analysis</subject><subject>differential usage</subject><subject>mass spectrometry based proteomics</subject><subject>peptidoform</subject><subject>posttranslational modification</subject><subject>proteomics data analysis</subject><subject>proteomics software</subject><issn>1535-9476</issn><issn>1535-9484</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9Uc1uGyEQRlWrJk37BJUqjr2sCwvsspWqykn6J9mqpSZnxMKQYO0uDmBLeZU-bYmdWs2lJ2Dm-2HmQ-gtJTNKaPNhPRvNJoZZTWpWKqQl8hk6pYKJquOSPz_e2-YEvUppTUhNaCteohMmqeCN7E7R7zHdxdDXq6vlR3zpnYMIU_Z6wPN-O1k9GcB6sk9b10nfAJ5PerhPPuHg8PJXda4TWLyKIUMYvUn4UmeNdcb5FvAqpJyjntKgsw-FiJfBeufN_rl3WMEmextciCNewA6G1-iF00OCN4_nGbr--uXq4nu1-Pntx8V8URnWiFxZQQmzQoLtuaAGdBmt51zwzpDGuc7xvq257EkZoCMMmlZqI3rWdKZjVrbsDH0-6G62_QjWlCGjHtQm-lHHexW0V087k79VN2GnKJGtqJumKLx_VIjhbgspq9EnA8OgJwjbpOqOSFqzsvUCZQeoiSGlCO7oQ4l6iFWt1T5W9RCrOsRaWO_-_eKR8zfHAvh0AEBZ1M5DVMl4KOFZH8FkZYP_r8EfP5O4RA</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Demeulemeester, Nina</creator><creator>Gébelin, Marie</creator><creator>Caldi Gomes, Lucas</creator><creator>Lingor, Paul</creator><creator>Carapito, Christine</creator><creator>Martens, Lennart</creator><creator>Clement, Lieven</creator><general>Elsevier Inc</general><general>American Society for Biochemistry and Molecular Biology</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9362-7096</orcidid><orcidid>https://orcid.org/0000-0002-0079-319X</orcidid><orcidid>https://orcid.org/0000-0002-8290-2432</orcidid><orcidid>https://orcid.org/0000-0002-9050-4370</orcidid><orcidid>https://orcid.org/0000-0003-4277-658X</orcidid></search><sort><creationdate>20240201</creationdate><title>msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level</title><author>Demeulemeester, Nina ; Gébelin, Marie ; Caldi Gomes, Lucas ; Lingor, Paul ; Carapito, Christine ; Martens, Lennart ; Clement, Lieven</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-d5103d58edb451cea154b44549c06ff9f4b7248b0ffe903e678ac5b369c93d873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>differential abundance</topic><topic>differential peptidoform analysis</topic><topic>differential PTM analysis</topic><topic>differential usage</topic><topic>mass spectrometry based proteomics</topic><topic>peptidoform</topic><topic>posttranslational modification</topic><topic>proteomics data analysis</topic><topic>proteomics software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Demeulemeester, Nina</creatorcontrib><creatorcontrib>Gébelin, Marie</creatorcontrib><creatorcontrib>Caldi Gomes, Lucas</creatorcontrib><creatorcontrib>Lingor, Paul</creatorcontrib><creatorcontrib>Carapito, Christine</creatorcontrib><creatorcontrib>Martens, Lennart</creatorcontrib><creatorcontrib>Clement, Lieven</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Molecular & cellular proteomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Demeulemeester, Nina</au><au>Gébelin, Marie</au><au>Caldi Gomes, Lucas</au><au>Lingor, Paul</au><au>Carapito, Christine</au><au>Martens, Lennart</au><au>Clement, Lieven</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level</atitle><jtitle>Molecular & cellular proteomics</jtitle><addtitle>Mol Cell Proteomics</addtitle><date>2024-02-01</date><risdate>2024</risdate><volume>23</volume><issue>2</issue><spage>100708</spage><epage>100708</epage><pages>100708-100708</pages><artnum>100708</artnum><issn>1535-9476</issn><eissn>1535-9484</eissn><abstract>In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.
[Display omitted]
•Msqrob2PTM is a novel statistical tool to detect differentially used PTMs & peptidoforms.•PTM abundances are corrected for parent protein abundance by a normalisation strategy.•The workflow is freely available on GitHub.•Tested on different datasets and compared to MSstatsPTM.•Reproducible & transparent workflow applicable to many different experimental designs.
The era of open-modification search engines in LC-MS/MS-based proteomics has expanded the detection of post-translational modifications (PTMs). However, statistical methods for PTM-level quantification and differential analysis are lacking. To address this, we introduce msqrob2PTM, offering differential usage analysis at the PTM and peptidoform level. The workflow provides an additional normalization to correct for parent protein abundance. Demonstrating efficacy on simulated datasets and PTM-rich biological data, msqrob2PTM outperforms existing methods and uniquely provides output at the peptidoform level. Accessible at https://github.com/statOmics/msqrob2PTMpaper.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>38154689</pmid><doi>10.1016/j.mcpro.2023.100708</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9362-7096</orcidid><orcidid>https://orcid.org/0000-0002-0079-319X</orcidid><orcidid>https://orcid.org/0000-0002-8290-2432</orcidid><orcidid>https://orcid.org/0000-0002-9050-4370</orcidid><orcidid>https://orcid.org/0000-0003-4277-658X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | differential abundance differential peptidoform analysis differential PTM analysis differential usage mass spectrometry based proteomics peptidoform posttranslational modification proteomics data analysis proteomics software |
title | msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level |
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