The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data
Open accessAbstract
Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, presence of protein domains or membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
Self-citationMapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Self-citationKEGG for representation and analysis of molecular networks involving diseases and drugs
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322
Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast
de Godoy, L. M. F., Olsen, J. V., Cox, J., Nielsen, M. L., Hubner, N. C., Fröhlich, F., Walther, T. C., & Mann, M. (2008) · Nature · doi:10.1038/nature07341 · Cited by 873
Self-citationQuantitative proteomics reveals subset-specific viral recognition in dendritic cells
Luber, C. A., Cox, J., Lauterbach, H., Fancke, B., Selbach, M., Tschopp, J., Akira, S., Wiegand, M., Hochrein, H., O'Keeffe, M., et al. (2010) · Immunity · doi:10.1016/j.immuni.2010.01.013 · Cited by 576
Self-citationPan, C., Kumar, C., Bohl, S., Klingmueller, U., & Mann, M. (2009) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800258-mcp200 · Cited by 561 · cited 1×
“When applied to ratios of protein abundances the method described here is similar to the quantile-based enrichment calculations introduced by Pan et al. [29] There the distribution of protein ratios was subdivided into bins and then all categories were tested for being enriched in these bins.”
MethodSelf-citationDefining the transcriptome and proteome in three functionally different human cell lines
Lundberg, E., Fagerberg, L., Klevebring, D., Matic, I., Geiger, T., Cox, J., Älgenäs, C., Lundeberg, J., Mann, M., & Uhlen, M. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.106 · Cited by 373
Self-citation
Cited by
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Self-citationL-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity
Geiger, R., Rieckmann, J. C., Wolf, T., Basso, C., Feng, Y., Fuhrer, T., Kogadeeva, M., Picotti, P., Meissner, F., Mann, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.031 · Cited by 1,775
Self-citationKulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719
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-citationAn atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637
Self-citationWidespread Proteome Remodeling and Aggregation in Aging C. elegans
Walther, D. M., Kasturi, P., Zheng, M., Pinkert, S., Vecchi, G., Ciryam, P., Morimoto, R. I., Dobson, C. M., Vendruscolo, M., Mann, M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.03.032 · Cited by 569
Self-citationMills, R. J., Titmarsh, D. M., Koenig, X., Parker, B. L., Ryall, J. G., Quaife-Ryan, G. A., Voges, H. K., Hodson, M. P., Ferguson, C., Drowley, L., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707316114 · Cited by 478
Robles, M. S., Cox, J., & Mann, M. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004047 · Cited by 383
Self-citationChen, Y.-J., Roumeliotis, T. I., Chang, Y.-H., Chen, C.-T., Han, C.-L., Lin, M.-H., Chen, H.-W., Chang, G.-C., Chang, Y.-L., Wu, C.-T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.012 · Cited by 382
The Coming Age of Complete, Accurate, and Ubiquitous Proteomes
Mann, M., Kulak, N. A., Nagaraj, N., & Cox, J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.01.029 · Cited by 350
Self-citationCano-Gamez, K., Soskic, B., Roumeliotis, T. I., So, E., Smyth, D. J., Baldrighi, M., Willé, D., Nakic, N., Esparza-Gordillo, J., Larminie, C. G. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15543-y · Cited by 274
Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease
Bader, J. M., Geyer, P. E., Müller, J. B., Strauss, M. T., Koch, M., Leypoldt, F., Koertvelyessy, P., Bittner, D., Schipke, C. G., Incesoy, E. I., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199356 · Cited by 286
Self-citationSchiller, H. B., Fernandez, I. E., Burgstaller, G., Schaab, C., Scheltema, R. A., Schwarzmayr, T., Strom, T. M., Eickelberg, O., & Mann, M. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156123 · Cited by 261
Self-citationProtein synthesis rate is the predominant regulator of protein expression during differentiation
Kristensen, A. R., Gsponer, J., & Foster, L. J. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.47 · Cited by 217
The ciliary membrane‐associated proteome reveals actin‐binding proteins as key components of cilia
Kohli, P., Höhne, M., Jüngst, C., Bertsch, S., Ebert, L. K., Schauss, A. C., Benzing, T., Rinschen, M. M., & Schermer, B. (2017) · The EMBO Reports · doi:10.15252/embr.201643846 · Cited by 145
Cuadrado, E., van den Biggelaar, M., de Kivit, S., Chen, Y.-Y., Slot, M., Doubal, I., Meijer, A., van Lier, R. A. W., Borst, J., & Amsen, D. (2018) · Immunity · doi:10.1016/j.immuni.2018.04.008 · Cited by 132
Rinschen, M. M., Gödel, M., Grahammer, F., Zschiedrich, S., Helmstädter, M., Kretz, O., Zarei, M., Braun, D. A., Dittrich, S., Pahmeyer, C., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.04.059 · Cited by 105
“This algorithm can be used to visualize statistically significant distributions of protein annotations in a 2D space between different ‘‘omics’’ datasets ht tp :// do c. re ro .c h (Cox and Mann, 2012).”
MethodMitochondrial membrane proteins and VPS35 orchestrate selective removal of mtDNA
Sen, A., Kallabis, S., Gaedke, F., Jüngst, C., Boix, J., Nüchel, J., Maliphol, K., Hofmann, J., Schauss, A. C., Krüger, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34205-9 · Cited by 102
Determinants and Regulation of Protein Turnover in Yeast
Martin-Perez, M., & Villén, J. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.008 · Cited by 99
Li, P., Seneviratne, C. J., Alpi, E., Vizcaino, J. A., & Jin, L. (2015) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00543-15 · Cited by 95
A framework for ultra-low-input spatial tissue proteomics
Makhmut, A., Qin, D., Fritzsche, S., Nimo, J., König, J., & Coscia, F. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.003 · Cited by 67
Label-free quantitative proteomic analysis of the YAP/TAZ interactome
Kohli, P., Bartram, M. P., Habbig, S., Pahmeyer, C., Lamkemeyer, T., Benzing, T., Schermer, B., & Rinschen, M. M. (2014) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00339.2013 · Cited by 61
Gerold, G., Meissner, F., Bruening, J., Welsch, K., Perin, P. M., Baumert, T. F., Vondran, F. W., Kaderali, L., Marcotrigiano, J., Khan, A. G., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.06.063 · Cited by 60
Self-citationPKC downregulation upon rapamycin treatment attenuates mitochondrial disease
Martin-Perez, M., Grillo, A. S., Ito, T. K., Valente, A. S., Han, J., Entwisle, S. W., Huang, H. Z., Kim, D., Yajima, M., Kaeberlein, M., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00319-x · Cited by 45
Kuras, M., Woldmar, N., Kim, Y., Hefner, M., Malm, J., Moldvay, J., Döme, B., Fillinger, J., Pizzatti, L., Gil, J., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00850 · Cited by 36
Salovska, B., Li, W., Di, Y., & Liu, Y. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.0c04293 · Cited by 35
Barbosa, I. C. R., De Bellis, D., Flückiger, I., Bellani, E., Grangé-Guerment, M., Hématy, K., & Geldner, N. (2023) · Nature Communications · doi:10.1038/s41467-023-37265-7 · Cited by 28
Kaulich, E., Waselenchuk, Q., Fürst, N., Desch, K., Mosbacher, J., Ciirdaeva, E., Juengling, M., Tushev, G., Langer, J., & Schuman, E. M. (2024) · doi:10.1101/2024.08.05.606570 · Cited by 22
Montuori, G., Tu, F., Qin, D., Schmargon, R., Rodriguez-Fos, E., Helmsauer, K., Hui, H., Mandal, S., Purshouse, K., Fankhänel, L., et al. (2025) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-1738 · Cited by 15
Velasquez, E., Szadai, L., Zhou, Q., Kim, Y., Pla, I., Sanchez, A., Appelqvist, R., Oskolas, H., Marko‐Varga, M., Lee, B., et al. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.466 · Cited by 13
Weiblen, C., Kits, K. D., Kleiner, M., Sauvageau, D., & Stein, L. Y. (2025) · mBio · doi:10.1128/mbio.01533-25 · Cited by 8
Phosphoregulation of the novel hemi-arrestin MAPK scaffold Sms1 prevents untimely mating
Sieber, B., Merlini, L., Li, W., Besomi, M., Michon, L., Gordon-Lennox, S., & Martin, S. G. (2026) · Nature Communications · doi:10.1038/s41467-026-70631-9 · Cited by 5
Cosentino, C., Klein, R., Menoud, V., Guay, C., Aiello, E., Auddino, S., Di Giuseppe, G., Ciccarelli, G., Galli, A., Alabiso, F., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72641-z · Cited by 1
Metadata
- Authors
- Cox, J., & Mann, M.
- Venue
- BMC Bioinformatics
- Published
- 2012-11
- Volume
- 13
- Issue
- S16
- Pages
- S12-S12
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2124091237
- Cited by
- 626
- References
- 10
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- gold
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.
