The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
Open accessAbstract
The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.
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Perez-Riverol, Y., Csordas, A., Bai, J., Bernal-Llinares, M., Hewapathirana, S., Kundu, D. J., Inuganti, A., Griss, J., Mayer, G., Eisenacher, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1106 · Cited by 6,693
A Direct Approach to False Discovery Rates
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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-citationDIA-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-citationGillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877
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-citationD’Hulst, G., Soro-Arnaiz, I., Masschelein, E., Veys, K., Fitzgerald, G., Smeuninx, B., Kim, S., Deldicque, L., Blaauw, B., Carmeliet, P., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13889-6 · Cited by 2,310
Data‐independent acquisition‐based SWATH ‐ MS for quantitative proteomics: a tutorial
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Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation
Brunner, A., Thielert, M., Vasilopoulou, C., Ammar, C., Coscia, F., Mund, A., Hoerning, O. B., Bache, N., Apalategui, A., Lubeck, M., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110798 · Cited by 544
Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
Yu, F., Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X., Demichev, V., & Nesvizhskii, A. I. (2023) · Nature Communications · doi:10.1038/s41467-023-39869-5 · Cited by 292
Self-citationSkowronek, P., Thielert, M., Voytik, E., Tanzer, M. C., Hansen, F. M., Willems, S., Karayel, O., Brunner, A.-D., Meier, F., & Mann, M. (2022) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2022.100279 · Cited by 281
Natural proteome diversity links aneuploidy tolerance to protein turnover
Muenzner, J., Trébulle, P., Agostini, F., Zauber, H., Messner, C. B., Steger, M., Kilian, C., Lau, K., Barthel, N., Lehmann, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07442-9 · Cited by 81
Self-citationSuhre, K., Venkataraman, G. R., Guturu, H., Halama, A., Stephan, N., Thareja, G., Sarwath, H., Motamedchaboki, K., Donovan, M. K. R., Siddiqui, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45233-y · Cited by 57
Metadata
- Authors
- Demichev, V., Szyrwiel, L., Yu, F., Teo, G. C., Rosenberger, G., Niewienda, A., Ludwig, D., Decker, J., Kaspar-Schoenefeld, S., Lilley, K. S., Mülleder, M., Nesvizhskii, A. I., & Ralser, M.
- Venue
- Nature Communications
- Published
- 2022-07-08
- Volume
- 13
- Issue
- 1
- Pages
- 3944-3944
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4284897468
- Cited by
- 441
- References
- 42
- FWCI
- 29.51
- Type
- journal article
- Language
- en
- Open access
- gold
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- Demichev, V., Szyrwiel, L., Yu, F., Teo, G. C., Rosenberger, G., Niewienda, A., Ludwig, D., Decker, J., Kaspar-Schoenefeld, S., Lilley, K. S., Mülleder, M., Nesvizhskii, A. I., & Ralser, M. (2022). dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nature Communications. https://doi.org/10.1038/s41467-022-31492-0
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