The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Mass spectrometry-based proteomics: existing capabilities and future directions
Open accessAbstract
Mass spectrometry (MS)-based proteomics is emerging as a broadly effective means for identification, characterization, and quantification of proteins that are integral components of the processes essential for life. Characterization of proteins at the proteome and sub-proteome (e.g., the phosphoproteome, proteoglycome, or degradome/peptidome) levels provides a foundation for understanding fundamental aspects of biology. Emerging technologies such as ion mobility separations coupled with MS and microchip-based-proteome measurements combined with MS instrumentation and chromatographic separation techniques, such as nanoscale reversed phase liquid chromatography and capillary electrophoresis, show great promise for both broad undirected and targeted highly sensitive measurements. MS-based proteomics increasingly contribute to our understanding of the dynamics, interactions, and roles that proteins and peptides play, advancing our understanding of biology on a systems wide level for a wide range of applications including investigations of microbial communities, bioremediation, and human health.
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
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Bioconductor: 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 12,172
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
Glioma stem cells promote radioresistance by preferential activation of the DNA damage response
Bao, S., Wu, Q., McLendon, R. E., Hao, Y., Shi, Q., Hjelmeland, A. B., Dewhirst, M. W., Bigner, D. D., & Rich, J. N. (2006) · Nature · doi:10.1038/nature05236 · Cited by 6,379
Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons
Karas, M., & Hillenkamp, F. (1988) · Analytical Chemistry · doi:10.1021/ac00171a028 · Cited by 5,637
Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
STRING 8--a global view on proteins and their functional interactions in 630 organisms
Jensen, L. J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn760 · Cited by 2,474
Mass Spectrometry and Protein Analysis
Domon, B., & Aebersold, R. (2006) · Science · doi:10.1126/science.1124619 · Cited by 2,181
Protein biomarker discovery and validation: the long and uncertain path to clinical utility
Rifai, N., Gillette, M. A., & Carr, S. A. (2006) · Nature Biotechnology · doi:10.1038/nbt1235 · Cited by 1,867
An Automated Multidimensional Protein Identification Technology for Shotgun Proteomics
Wolters, D. A., Washburn, M. P., & Yates, J. R. (2001) · Analytical Chemistry · doi:10.1021/ac010617e · Cited by 1,819
Cited by
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
MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
Dorfer, V., Pichler, P., Stranzl, T., Stadlmann, J., Taus, T., Winkler, S., & Mechtler, K. (2014) · Journal of Proteome Research · doi:10.1021/pr500202e · Cited by 490
Vowinckel, J., Zelezniak, A., Kibler, A., Bruderer, R., Muelleder, M., Reiter, L., & Ralser, M. (2016) · doi:10.1101/073478 · Cited by 81
Cilento, E. M., Jin, L., Stewart, T., Shi, M., Sheng, L., & Zhang, J. (2019) · Journal of Neurochemistry · doi:10.1111/jnc.14635 · Cited by 60
Zhao, Y., Sun, L., Champion, M. M., Knierman, M. D., & Dovichi, N. J. (2014) · Analytical Chemistry · doi:10.1021/ac500092q · Cited by 56
Dang, X., Singh, A., Spetman, B. D., Nolan, K. D., Isaacs, J. S., Dennis, J. H., Dalton, S., Marshall, A. G., & Young, N. L. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00414 · Cited by 41
Yang, L., Chumsae, C., Kaplan, J. B., Moulton, K. R., Wang, D., Lee, D. H., & Zhou, Z. S. (2017) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.7b00354 · Cited by 27
Liu, X., Dong, Z., & Timp, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-22305-x · Cited by 7
Metadata
- Authors
- Angel, T. E., Aryal, U. K., Hengel, S. M., Baker, E. S., Kelly, R. T., Robinson, E. W., & Smith, R. D.
- Venue
- Chemical Society Reviews
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2012
- Volume
- 41
- Issue
- 10
- Pages
- 3912
- ISSN
- 0306-0012 (ISSN-L)
- 1460-4744
- OpenAlex
- W1998261260
- Cited by
- 441
- References
- 89
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- green
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.
