The DOI matches its Crossref record; 161 of the 161 references it cites were found in a registry or an index.
Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology
Open accessAbstract
Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Mass-spectrometry remains the only method of profiling the neuroproteome with a throughput and resolving power comparable to other functional genomic methods25.”
From Decoding neuroproteomics: integrating the genome, translatome and functional anatomy (2014) · cites this paper as [25]
Citations
Cites
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
Self-citationBioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
The language of covalent histone modifications
Strahl, B. D., & Allis, C. D. (2000) · Nature · doi:10.1038/47412 · Cited by 8,679
Universal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352
Self-citationPerkins, D. N., Pappin, D., Creasy, D. M., & Cottrell, J. S. (1999) · Electrophoresis · doi:10.1002/(sici)1522-2683(19991201)20:18 · Cited by 8,264
Electrospray Ionization for Mass Spectrometry of Large Biomolecules
Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., & Whitehouse, C. M. (1989) · Science · doi:10.1126/science.2675315 · Cited by 7,634
Self-citationMass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Self-citationEng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,474
Cited by
Sorted by citations
Protein Analysis by Shotgun/Bottom-up Proteomics
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Tracking cancer drugs in living cells by thermal profiling of the proteome
Savitski, M. M., Reinhard, F. B. M., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., Molina, D. M., Jafari, R., Dovega, R. B., Klaeger, S., et al. (2014) · Science · doi:10.1126/science.1255784 · Cited by 1,187
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979
Self-citationPlasma Proteome Profiling to Assess Human Health and Disease
Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.015 · Cited by 734
Self-citation
Metadata
- Authors
- Cox, J., & Mann, M.
- Affiliations
- Max Planck Institute of Biochemistry (DE)
- University of Copenhagen (DK)
- Venue
- Annual Review of Biochemistry
- Publisher
- Annual Reviews
- Published
- 2011-07-07
- Volume
- 80
- Issue
- 1
- Pages
- 273-299
- ISSN
- 0066-4154 (ISSN-L)
- 1545-4509
- OpenAlex
- W2106074866
- Cited by
- 685
- References
- 161
- Citations by year
- 2026: 18 · 2025: 29 · 2024: 15 · 2023: 37 · 2022: 23 · 2021: 37 · 2020: 36 · 2019: 39 · 2018: 29 · 2017: 48 · 2016: 47 · 2015: 68 · 2014: 69 · 2013: 83 · 2012: 85
- FWCI
- 6.97
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- Annual Review of Biochemistry (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- European Commission
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- quantitative proteomics, high-resolution mass spectrometry, systems biology, peptide identification, protein quantification, post-translational modification, proteome dynamics, protein localization, protein-protein interaction, proteomics data analysis
- MeSH terms
- Chromatography, Liquid, Humans, Peptides, Protein Processing, Post-Translational, Proteins, Proteome, Proteomics, Systems Biology, Tandem Mass Spectrometry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology. https://app.cochat.ai/papers/10.1146/annurev-biochem-061308-093216
- The paper
- Cox, J., & Mann, M. (2011). Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology. Annual Review of Biochemistry. https://doi.org/10.1146/annurev-biochem-061308-093216
