The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Proteomics: the move to mixtures
Abstract
Proteomics can be defined as the systematic analysis of proteins for their identity, quantity and function. In contrast to a cell's static genome, the proteome is both complex and dynamic. Proteome analysis is most commonly accomplished by the combination of two‐dimensional gel electrophoresis (2DE) and mass spectrometry (MS). However, this technique is under scrutiny because of a failure to detect low‐abundance proteins from the analysis of whole cell lysates. Alternative approaches integrate a diversity of separation technologies and make use of the tremendous peptide separation and sequencing power provided by MS/MS. When liquid chromatography is combined with tandem mass spectrometry (LC/MS/MS) and applied to the direct analysis of mixtures, many of the limitations of 2DE for proteome analysis can be overcome. This tutorial addresses current approaches to identify and characterize large numbers of proteins and measure dynamic changes in protein expression directly from complex protein mixtures (total cell lysates). Copyright © 2001 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999) · Nature Biotechnology · doi:10.1038/13690 · Cited by 4,683
Cited by
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., & Gygi, S. P. (2003) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,647
Self-citationMass spectrometry–based proteomics turns quantitative
Ong, S.-E., & Mann, M. (2005) · Nature Chemical Biology · doi:10.1038/nchembio736 · Cited by 1,572
Large-scale characterization of HeLa cell nuclear phosphoproteins
Beausoleil, S. A., Jedrychowski, M., Schwartz, D., Elias, J. E., Villén, J., Li, J., Cohn, M. A., Cantley, L. C., & Gygi, S. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0404720101 · Cited by 1,375
Self-citationAnticancer sulfonamides target splicing by inducing RBM39 degradation via recruitment to DCAF15
Han, T., Goralski, M., Gaskill, N., Capota, E., Kim, J., Ting, T. C., Xie, Y., Williams, N. S., & Nijhawan, D. (2017) · Science · doi:10.1126/science.aal3755 · Cited by 762
Metadata
- Authors
- Peng, J., & Gygi, S. P.
- Venue
- Journal of Mass Spectrometry
- Publisher
- Wiley
- Published
- 2001-10
- Volume
- 36
- Issue
- 10
- Pages
- 1083-1091
- ISSN
- 1076-5174 (ISSN-L)
- 1096-9888
- OpenAlex
- W2027167256
- Cited by
- 648
- References
- 1
- FWCI
- 20.23
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.