Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

Rebeca Kawahara; Anastasia Chernykh; Kathirvel Alagesan; Marshall Bern; Weiqian Cao; Robert J. Chalkley; Kai Cheng; Matthew S. Choo; Nathan Edwards; Radoslav Goldman; Marcus Hoffmann; Yingwei Hu; Yifan Huang; Jin Young Kim; Doron Kletter; Benoit Liquet; Mingqi Liu; Yehia Mechref; Bo Meng; Sriram Neelamegham; Terry Nguyen-Khuong; Jonas Nilsson; Adam Pap; Gun Wook Park; Benjamin L. Parker; Cassandra L. Pegg; Josef M. Penninger; Toan K. Phung; Markus Pioch; Erdmann Rapp; Enes Sakalli; Miloslav Sanda; Benjamin L. Schulz; Nichollas E. Scott; Georgy Sofronov; Johannes Stadlmann; Sergey Y. Vakhrushev; Christina M. Woo; Hung Yi Wu; Pengyuan Yang; Wantao Ying; Hui Zhang; Yong Zhang; Jingfu Zhao; Joseph Zaia; Stuart M. Haslam; Giuseppe Palmisano; Jong Shin Yoo; Göran Larson; Kai Hooi Khoo; Katalin F. Medzihradszky; Daniel Kolarich; Nicolle H. Packer; Morten Thaysen-Andersen

doi:10.1038/s41592-021-01309-x

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

Rebeca Kawahara, Anastasia Chernykh, Kathirvel Alagesan, Marshall Bern, Weiqian Cao, Robert J. Chalkley, Kai Cheng, Matthew S. Choo, Nathan Edwards, Radoslav Goldman, Marcus Hoffmann, Yingwei Hu, Yifan Huang, Jin Young Kim, Doron Kletter, Benoit Liquet, Mingqi Liu, Yehia Mechref, Bo Meng, Sriram NeelameghamTerry Nguyen-Khuong, Jonas Nilsson, Adam Pap, Gun Wook Park, Benjamin L. Parker, Cassandra L. Pegg, Josef M. Penninger, Toan K. Phung, Markus Pioch, Erdmann Rapp, Enes Sakalli, Miloslav Sanda, Benjamin L. Schulz, Nichollas E. Scott, Georgy Sofronov, Johannes Stadlmann, Sergey Y. Vakhrushev, Christina M. Woo, Hung Yi Wu, Pengyuan Yang, Wantao Ying, Hui Zhang, Yong Zhang, Jingfu Zhao, Joseph Zaia, Stuart M. Haslam, Giuseppe Palmisano, Jong Shin Yoo, Göran Larson, Kai Hooi Khoo, Katalin F. Medzihradszky, Daniel Kolarich, Nicolle H. Packer, Morten Thaysen-Andersen

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Abstract

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Original language	English
Pages (from-to)	1304-1316
Number of pages	13
Journal	Nature Methods
Volume	18
Issue number	11
DOIs	https://doi.org/10.1038/s41592-021-01309-x
State	Published - Nov 2021

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Kawahara, R., Chernykh, A., Alagesan, K., Bern, M., Cao, W., Chalkley, R. J., Cheng, K., Choo, M. S., Edwards, N., Goldman, R., Hoffmann, M., Hu, Y., Huang, Y., Kim, J. Y., Kletter, D., Liquet, B., Liu, M., Mechref, Y., Meng, B., ... Thaysen-Andersen, M. (2021). Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis. Nature Methods, 18(11), 1304-1316. https://doi.org/10.1038/s41592-021-01309-x

@article{e8d871e590204a22af04a8c2bc05fe02,

title = "Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis",

abstract = "Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved {\textquoteleft}high-coverage{\textquoteright} and {\textquoteleft}high-accuracy{\textquoteright} glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.",

author = "Rebeca Kawahara and Anastasia Chernykh and Kathirvel Alagesan and Marshall Bern and Weiqian Cao and Chalkley, {Robert J.} and Kai Cheng and Choo, {Matthew S.} and Nathan Edwards and Radoslav Goldman and Marcus Hoffmann and Yingwei Hu and Yifan Huang and Kim, {Jin Young} and Doron Kletter and Benoit Liquet and Mingqi Liu and Yehia Mechref and Bo Meng and Sriram Neelamegham and Terry Nguyen-Khuong and Jonas Nilsson and Adam Pap and Park, {Gun Wook} and Parker, {Benjamin L.} and Pegg, {Cassandra L.} and Penninger, {Josef M.} and Phung, {Toan K.} and Markus Pioch and Erdmann Rapp and Enes Sakalli and Miloslav Sanda and Schulz, {Benjamin L.} and Scott, {Nichollas E.} and Georgy Sofronov and Johannes Stadlmann and Vakhrushev, {Sergey Y.} and Woo, {Christina M.} and Wu, {Hung Yi} and Pengyuan Yang and Wantao Ying and Hui Zhang and Yong Zhang and Jingfu Zhao and Joseph Zaia and Haslam, {Stuart M.} and Giuseppe Palmisano and Yoo, {Jong Shin} and G{\"o}ran Larson and Khoo, {Kai Hooi} and Medzihradszky, {Katalin F.} and Daniel Kolarich and Packer, {Nicolle H.} and Morten Thaysen-Andersen",

note = "Funding Information: Rosa Viner and Sergei Snovida (Thermo Fisher Scientific) are thanked for providing high-quality LC-MS/MS data. Krishnatej Nishtala is thanked for aiding the data analysis. Catherine Hayes, Julien Mariethoz and Frederique Lisacek are thanked for informatics assistance. RK was supported by an Early Career Fellowship (Cancer Institute NSW ECF181259). DK was supported by an Australian Research Council Future Fellowship (FT160100344). GP was funded by FAPESP (n° 2018/15549-1). MT-A was supported by a Macquarie University Safety Net Grant. DK and NHP were supported by the Australian Research Council Centre of Excellence in Nanoscale Biophotonics (CE140100003). Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = nov,

doi = "10.1038/s41592-021-01309-x",

language = "English",

volume = "18",

pages = "1304--1316",

journal = "Nature Methods",

issn = "1548-7091",

number = "11",

}

Kawahara, R, Chernykh, A, Alagesan, K, Bern, M, Cao, W, Chalkley, RJ, Cheng, K, Choo, MS, Edwards, N, Goldman, R, Hoffmann, M, Hu, Y, Huang, Y, Kim, JY, Kletter, D, Liquet, B, Liu, M, Mechref, Y, Meng, B, Neelamegham, S, Nguyen-Khuong, T, Nilsson, J, Pap, A, Park, GW, Parker, BL, Pegg, CL, Penninger, JM, Phung, TK, Pioch, M, Rapp, E, Sakalli, E, Sanda, M, Schulz, BL, Scott, NE, Sofronov, G, Stadlmann, J, Vakhrushev, SY, Woo, CM, Wu, HY, Yang, P, Ying, W, Zhang, H, Zhang, Y, Zhao, J, Zaia, J, Haslam, SM, Palmisano, G, Yoo, JS, Larson, G, Khoo, KH, Medzihradszky, KF, Kolarich, D, Packer, NH & Thaysen-Andersen, M 2021, 'Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis', Nature Methods, vol. 18, no. 11, pp. 1304-1316. https://doi.org/10.1038/s41592-021-01309-x

TY - JOUR

T1 - Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

AU - Kawahara, Rebeca

AU - Chernykh, Anastasia

AU - Alagesan, Kathirvel

AU - Bern, Marshall

AU - Cao, Weiqian

AU - Chalkley, Robert J.

AU - Cheng, Kai

AU - Choo, Matthew S.

AU - Edwards, Nathan

AU - Goldman, Radoslav

AU - Hoffmann, Marcus

AU - Hu, Yingwei

AU - Huang, Yifan

AU - Kim, Jin Young

AU - Kletter, Doron

AU - Liquet, Benoit

AU - Liu, Mingqi

AU - Mechref, Yehia

AU - Meng, Bo

AU - Neelamegham, Sriram

AU - Nguyen-Khuong, Terry

AU - Nilsson, Jonas

AU - Pap, Adam

AU - Park, Gun Wook

AU - Parker, Benjamin L.

AU - Pegg, Cassandra L.

AU - Penninger, Josef M.

AU - Phung, Toan K.

AU - Pioch, Markus

AU - Rapp, Erdmann

AU - Sakalli, Enes

AU - Sanda, Miloslav

AU - Schulz, Benjamin L.

AU - Scott, Nichollas E.

AU - Sofronov, Georgy

AU - Stadlmann, Johannes

AU - Vakhrushev, Sergey Y.

AU - Woo, Christina M.

AU - Wu, Hung Yi

AU - Yang, Pengyuan

AU - Ying, Wantao

AU - Zhang, Hui

AU - Zhang, Yong

AU - Zhao, Jingfu

AU - Zaia, Joseph

AU - Haslam, Stuart M.

AU - Palmisano, Giuseppe

AU - Yoo, Jong Shin

AU - Larson, Göran

AU - Khoo, Kai Hooi

AU - Medzihradszky, Katalin F.

AU - Kolarich, Daniel

AU - Packer, Nicolle H.

AU - Thaysen-Andersen, Morten

N1 - Funding Information: Rosa Viner and Sergei Snovida (Thermo Fisher Scientific) are thanked for providing high-quality LC-MS/MS data. Krishnatej Nishtala is thanked for aiding the data analysis. Catherine Hayes, Julien Mariethoz and Frederique Lisacek are thanked for informatics assistance. RK was supported by an Early Career Fellowship (Cancer Institute NSW ECF181259). DK was supported by an Australian Research Council Future Fellowship (FT160100344). GP was funded by FAPESP (n° 2018/15549-1). MT-A was supported by a Macquarie University Safety Net Grant. DK and NHP were supported by the Australian Research Council Centre of Excellence in Nanoscale Biophotonics (CE140100003). Publisher Copyright: © 2021, The Author(s).

PY - 2021/11

Y1 - 2021/11

N2 - Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

AB - Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

UR - http://www.scopus.com/inward/record.url?scp=85118374178&partnerID=8YFLogxK

U2 - 10.1038/s41592-021-01309-x

DO - 10.1038/s41592-021-01309-x

M3 - Article

C2 - 34725484

AN - SCOPUS:85118374178

SN - 1548-7091

VL - 18

SP - 1304

EP - 1316

JO - Nature Methods

JF - Nature Methods

IS - 11

ER -

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

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