The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Open accessAbstract
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods. A deep learning-based software tool, DIA-NN, enables deep proteome analysis from data generated using fast chromatographic approaches and data-independent acquisition mass spectrometry.
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Mass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334
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mProphet: automated data processing and statistical validation for large-scale SRM experiments
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Deep Visual Proteomics defines single-cell identity and heterogeneity
Mund, A., Coscia, F., Kriston, A., Hollandi, R., Kovács, F., Brunner, A.-D., Migh, E., Schweizer, L., Santos, A., Bzorek, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01302-5 · Cited by 562
Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
Messner, C. B., Demichev, V., Wendisch, D., Michalick, L., White, M., Freiwald, A., Textoris-Taube, K., Vernardis, S. I., Egger, A.-S., Kreidl, M., et al. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.012 · Cited by 561
Self-citationUltra‐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
Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona
Blume, J. E., Manning, W. C., Troiano, G., Hornburg, D., Figa, M., Hesterberg, L., Platt, T. L., Zhao, X., Cuaresma, R. A., Everley, P. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17033-7 · Cited by 452
Frankenfield, A. M., Ni, J., Ahmed, M., & Hao, L. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00145 · Cited by 321
Metadata
- Authors
- Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S., & Ralser, M.
- Venue
- Nature Methods
- Published
- 2019-11-25
- Volume
- 17
- Issue
- 1
- Pages
- 41-44
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2991106430
- Cited by
- 3,343
- References
- 8
- FWCI
- 29.23
- Type
- journal article
- Language
- en
- Open access
- green
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- Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S., & Ralser, M. (2020). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. https://doi.org/10.1038/s41592-019-0638-x
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