The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines
Open accessAbstract
Protein quantification at proteome‐wide scale is an important aim, enabling insights into fundamental cellular biology and serving to constrain experiments and theoretical models. While proteome‐wide quantification is not yet fully routine, many datasets approaching proteome‐wide coverage are becoming available through biophysical and MS techniques. Data of this type can be accessed via a variety of sources, including publication supplements and online data repositories. However, access to the data is still fragmentary, and comparisons across experiments and organisms are not straightforward. Here, we describe recent updates to our database resource “PaxDb” (Protein Abundances Across Organisms). PaxDb focuses on protein abundance information at proteome‐wide scope, irrespective of the underlying measurement technique. Quantification data is reprocessed, unified, and quality‐scored, and then integrated to build a meta‐resource. PaxDb also allows evolutionary comparisons through precomputed gene orthology relations. Recently, we have expanded the scope of the database to include cell‐line samples, and more systematically scan the literature for suitable datasets. We report that a significant fraction of published experiments cannot readily be accessed and/or parsed for quantitative information, requiring additional steps and efforts. The current update brings PaxDb to 414 datasets in 53 organisms, with (semi‐) quantitative abundance information covering more than 300 000 proteins.
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
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Self-citationProteomeXchange provides globally coordinated proteomics data submission and dissemination
Vizcaíno, J. A., Deutsch, E. W., Wang, R., Csordas, A., Reisinger, F., Ríos, D., Dianes, J. A., Sun, Z., Farrah, T., Bandeira, N., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2839 · Cited by 2,729
A draft map of the human proteome
Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) · Nature · doi:10.1038/nature13302 · Cited by 2,198
The Proteomics Identifications (PRIDE) database and associated tools: status in 2013
Vizcaíno, J. A., Côté, R. G., Csordas, A., Dianes, J. A., Fabregat, A., Foster, J. M., Griss, J., Alpi, E., Birim, M., Contell, J., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1262 · Cited by 1,918
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820
Activities at the Universal Protein Resource (UniProt)
(2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1140 · Cited by 1,735
Proteoform: a single term describing protein complexity
The Consortium for Top Down Proteomics, Smith, L. M., & Kelleher, N. L. (2013) · Nature Methods · doi:10.1038/nmeth.2369 · Cited by 1,528
Next-generation proteomics: towards an integrative view of proteome dynamics
Altelaar, A. F. M., Munoz, J., & Heck, A. J. R. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3356 · Cited by 782
Uberon, an integrative multi-species anatomy ontology
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-1-r5 · Cited by 732 · cited 1×
“Tissues and cell-lines are annotated with controlled vocabularies; in the case of tissues we use the Uberon ontology [20], which natively allows cross-species comparisons of homologous tissues/organs.”
MethodOpen Source System for Analyzing, Validating, and Storing Protein Identification Data
Craig, R., Cortens, J. P., & Beavis, R. C. (2004) · Journal of Proteome Research · doi:10.1021/pr049882h · Cited by 694
Cited by
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749
Self-citationSzklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1074 · Cited by 8,056
Self-citationThe protein expression profile of ACE2 in human tissues
Hikmet, F., Méar, L., Edvinsson, Å., Micke, P., Uhlén, M., & Lindskog, C. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209610 · Cited by 1,016
The functional landscape of the human phosphoproteome
Ochoa, D., Jarnuczak, A. F., Viéitez, C., Gehre, M., Soucheray, M., Mateus, A., Kleefeldt, A. A., Hill, A., Garcia-Alonso, L., Stein, F., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0344-3 · Cited by 491
The nitrogen cost of photosynthesis
Evans, J. R., & Clarke, V. C. (2019) · Journal of Experimental Botany · doi:10.1093/jxb/ery366 · Cited by 438
Metadata
- Authors
- Wang, M., Herrmann, C. J., Simonovic, M., Szklarczyk, D., & von Mering, C.
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2015-03-12
- Volume
- 15
- Issue
- 18
- Pages
- 3163-3168
- ISSN
- 1615-9853 (ISSN-L)
- 1615-9861
- OpenAlex
- W2064295826
- Cited by
- 537
- References
- 29
- FWCI
- 14.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines. https://app.cochat.ai/papers/10.1002/pmic.201400441
- The paper
- Wang, M., Herrmann, C. J., Simonovic, M., Szklarczyk, D., & von Mering, C. (2015). Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines. PROTEOMICS. https://doi.org/10.1002/pmic.201400441
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.
