The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
Open accessAbstract
Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.
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Cites
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
Keller, A., Nesvizhskii, A. I., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,830
Self-citationA Statistical Model for Identifying Proteins by Tandem Mass Spectrometry
Nesvizhskii, A. I., Keller, A., Kolker, E., & Aebersold, R. (2003) · Analytical Chemistry · doi:10.1021/ac0341261 · Cited by 4,809
Self-citationSkyline: an open source document editor for creating and analyzing targeted proteomics experiments
MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M., Finney, G. L., Frewen, B., Kern, R., Tabb, D. L., Liebler, D. C., & MacCoss, M. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq054 · Cited by 4,697
A cross-platform toolkit for mass spectrometry and proteomics
Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2377 · Cited by 4,093
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S., & Ralser, M. (2020) · Nature Methods · doi:10.1038/s41592-019-0638-x · Cited by 3,343
Self-citationTANDEM: matching proteins with tandem mass spectra
Craig, R., & Beavis, R. C. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth092 · Cited by 2,536
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424
MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D., & Nesvizhskii, A. I. (2017) · Nature Methods · doi:10.1038/nmeth.4256 · Cited by 2,402
Self-citationComet: An open‐source MS / MS sequence database search tool
Eng, J. K., Jahan, T. A., & Hoopmann, M. R. (2013) · PROTEOMICS · doi:10.1002/pmic.201200439 · Cited by 1,474
Cited by
Guzman, U. H., Martinez-Val, A., Ye, Z., Damoc, E., Arrey, T. N., Pashkova, A., Renuse, S., Denisov, E., Petzoldt, J., Peterson, A. C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02099-7 · Cited by 346
CURTAIN—A unique web-based tool for exploration and sharing of MS-based proteomics data
Phung, T. K., Berndsen, K., Shastry, R., Phan, T. L. C. H. B., Muqit, M. M. K., Alessi, D. R., & Nirujogi, R. S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312676121 · Cited by 18
Gel-Based Sample Fractionation with SP3-Purification for Top-Down Proteomics
Takemori, A., Sugiyama, N., Kline, J. T., Fornelli, L., & Takemori, N. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00941 · Cited by 12
The fitness cost of spurious phosphorylation
Bradley, D., Hogrebe, A., Dandage, R., Dubé, A. K., Leutert, M., Dionne, U., Chang, A., Villén, J., & Landry, C. R. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00200-7 · Cited by 10
Deuterium labeling enables proteome-wide turnover kinetics analysis in cell culture
Alamillo, L., Ng, D. C. M., Currie, J., Black, A., Pandi, B., Manda, V., Pavelka, J., Schaal, P., Travers, J. G., McKinsey, T. A., et al. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101104 · Cited by 6
Metadata
- Authors
- Yu, F., Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X., Demichev, V., & Nesvizhskii, A. I.
- Venue
- Nature Communications
- Published
- 2023-07-12
- Volume
- 14
- Issue
- 1
- Pages
- 4154-4154
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4384028715
- Cited by
- 292
- References
- 79
- FWCI
- 25.63
- Type
- journal article
- Language
- en
- Open access
- gold
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- Yu, F., Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X., Demichev, V., & Nesvizhskii, A. I. (2023). Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nature Communications. https://doi.org/10.1038/s41467-023-39869-5
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