The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data
Open accessAbstract
Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECAN to build a plasma proteome library from DIA data and to query known sequence variants.
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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
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
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
Self-citationBeausoleil, S. A., Villén, J., Gerber, S. A., Rush, J., & Gygi, S. P. (2006) · Nature Biotechnology · doi:10.1038/nbt1240 · Cited by 1,577
Ting, Y. S., Egertson, J. D., Payne, S. H., Kim, S., MacLean, B., Käll, L., Aebersold, R., Smith, R. D., Noble, W. S., & MacCoss, M. J. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.047035 · Cited by 175
Self-citationRapid empirical discovery of optimal peptides for targeted proteomics
Stergachis, A. B., MacLean, B., Lee, K., Stamatoyannopoulos, J. A., & MacCoss, M. J. (2011) · Nature Methods · doi:10.1038/nmeth.1770 · Cited by 110
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Searle, B. C., Pino, L. K., Egertson, J. D., Ting, Y. S., Lawrence, R. T., MacLean, B. X., Villén, J., & MacCoss, M. J. (2018) · Nature Communications · doi:10.1038/s41467-018-07454-w · Cited by 522
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Ultra-fast proteomics with Scanning SWATH
Messner, C. B., Demichev, V., Bloomfield, N., Yu, J. S. L., White, M., Kreidl, M., Egger, A.-S., Freiwald, A., Ivosev, G., Wasim, F., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00860-4 · Cited by 308
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
Pino, L. K., Just, S. C., MacCoss, M. J., & Searle, B. C. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.p119.001913 · Cited by 284
Self-citationBioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280
Fert-Bober, J., Murray, C. I., Parker, S. J., & Van Eyk, J. E. (2018) · Circulation Research · doi:10.1161/circresaha.118.310966 · Cited by 45
Metadata
- Authors
- Ting, Y. S., Egertson, J. D., Bollinger, J. G., Searle, B. C., Payne, S. H., Noble, W. S., & MacCoss, M. J.
- Venue
- Nature Methods
- Published
- 2017-08-07
- Volume
- 14
- Issue
- 9
- Pages
- 903-908
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2743861588
- Cited by
- 190
- References
- 6
- FWCI
- 9.21
- Type
- journal article
- Language
- en
- Open access
- green
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