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Making proteomics data accessible and reusable: Current state of proteomics databases and repositories
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Compared to other data‐intensive disciplines such as genomics, public deposition and storage of MS‐based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.
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Cites
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
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
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 1×
“The iBAQ algorithm [98, 99], based on spectral counting, is now implemented in MaxQuant and can also be used to estimate protein quantification values.”
MethodA 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
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
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
Lanucara, F., Holman, S. W., Gray, C. J., & Eyers, C. E. (2014) · Nature Chemistry · doi:10.1038/nchem.1889 · Cited by 901
Michalski, A., Damoc, E., Hauschild, J.-P., Lange, O., Wieghaus, A., Makarov, A., Nagaraj, N., Cox, J., Mann, M., & Horning, S. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.011015 · Cited by 832
The quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816
mProphet: automated data processing and statistical validation for large-scale SRM experiments
Reiter, L., Rinner, O., Picotti, P., Hüttenhain, R., Beck, M., Brusniak, M.-Y., Hengartner, M. O., & Aebersold, R. (2011) · Nature Methods · doi:10.1038/nmeth.1584 · Cited by 532
Research works printed but not linked: 124
- 10.1038/nrg3356 · DOI · Paper page
- 10.1016/j.jprot.2013.06.033 · DOI
- 10.1038/nmeth.2732 · DOI · Paper page
- 10.1021/pr700840y · doi:10.1021/pr700840y
- 10.1002/elps.201000677 · doi:10.1002/elps.201000677
- 10.1093/nar/gks1068 · DOI · Paper page
- 10.1016/j.bbapap.2013.02.032 · DOI
- 10.1093/database/bas004 · DOI
- 10.1038/nbt0709-579 · DOI
- 10.1016/j.clinbiochem.2012.10.025 · DOI
- 10.1002/pmic.201000553 · DOI · Paper page
- 10.1586/epr.11.54 · DOI
- 10.1016/j.copbio.2012.10.013 · DOI
- 10.1021/pr500572z · DOI
- 10.1021/pr9004794 · DOI · Paper page
- 10.1002/pmic.201100562 · doi:10.1002/pmic.201100562
- 10.1038/nmeth.2011 · DOI
- 10.1038/nmeth.2767 · DOI · Paper page
- 10.1002/pmic.200900216 · DOI
- 10.1021/pr049882h · DOI · Paper page
- 10.1021/pr4010037 · DOI
- 10.1093/nar/gks1262 · DOI · Paper page
- 10.1002/pmic.201100515 · DOI · Paper page
- 10.1089/omi.2014.0061 · DOI
- 10.1093/nar/gkn701 · DOI
- 10.1038/nbt0709-600 · DOI
- 10.1007/978-1-60761-987-1_8 · DOI
- 10.1038/nbt.2839 · DOI · Paper page
- 10.1007/978-1-60761-977-2_14 · DOI
- 10.1016/j.jprot.2010.06.008 · DOI
- 10.1002/pmic.200800553 · DOI
- 10.1002/pmic.200700152 · DOI
- The UniProtConsortium, 2014, Activities at the Universal Protein Resource (UniProt), Nucl. Acids Res., 42, D191, 10.1093/nar/gkt1140 · DOI · Paper page
- 10.1021/pr300830v · DOI · Paper page
- 10.1126/scisignal.2001839 · DOI
- 10.1074/mcp.R110.000133 · DOI · Paper page
- 10.1074/mcp.M111.014381 · DOI · Paper page
- 10.1074/mcp.O113.028506 · DOI
- 10.1038/nbt1031 · DOI · Paper page
- 10.1002/pmic.200900375 · DOI · Paper page
- 10.1074/mcp.R111.015040 · DOI
- Griss J., 2014, The mzTab data exchange format: communicating MS‐based proteomics and metabolomics experimental results to a wider audience, Mol. Cell. Proteomics, mcp.O113.036681
- 10.1016/j.bbapap.2013.02.017 · DOI
- 10.1093/nar/gkq331 · DOI
- 10.1038/nbt.2112 · DOI
- 10.1074/mcp.O112.021543 · DOI
- 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2 · DOI · Paper page
- 10.1093/bioinformatics/bth092 · DOI · Paper page
- 10.1002/pmic.201400120 · DOI
- 10.1093/nar/gkt1196 · DOI · Paper page
- 10.1038/nmeth.2343 · DOI
- 10.1038/nmeth1108-913 · DOI · Paper page
- 10.1021/pr301012j · DOI
- 10.1186/gb-2004-6-1-r9 · DOI · Paper page
- 10.1016/j.ymeth.2011.01.007 · DOI
- 10.1038/nmeth.1408 · DOI · Paper page
- 10.1038/embor.2008.56 · DOI · Paper page
- 10.1038/nbt1275 · DOI · Paper page
- 10.1021/ac025747h · DOI · Paper page
- 10.1021/ac0341261 · DOI · Paper page
- 10.1007/978-1-61779-068-3_23 · DOI
- 10.1074/mcp.M500279-MCP200 · DOI · Paper page
- 10.1038/nmeth.2212 · DOI · Paper page
- 10.1038/nbt.2152 · DOI · Paper page
- 10.1074/mcp.M800032-MCP200 · DOI · Paper page
- 10.1186/1471-2105-12-78 · DOI
- 10.1186/1471-2105-7-176 · DOI · Paper page
- 10.1021/pr8001244 · DOI · Paper page
- 10.1073/pnas.0811066106 · DOI · Paper page
- 10.1021/pr400294c · DOI
- 10.1074/mcp.M113.031260 · DOI
- 10.1074/mcp.M111.010199 · DOI
- 10.1074/mcp.M000136-MCP201 · DOI
- 10.1074/mcp.M111.010017 · DOI
- 10.1021/ac048788h · DOI · Paper page
- 10.1074/mcp.M111.015768 · DOI
- 10.1021/pr0602085 · DOI · Paper page
- 10.1002/rcm.1992 · DOI · Paper page
- 10.1038/75556 · DOI · Paper page
- 10.1093/nar/28.1.27 · DOI · Paper page
- 10.1093/nar/gkq968 · DOI · Paper page
- 10.1006/geno.2002.6748 · DOI · Paper page
- 10.1021/pr101065j · DOI · Paper page
- 10.1038/nature11848 · DOI · Paper page
- 10.1093/nar/gkr1177 · DOI
- 10.1021/pr400884c · DOI
- 10.1016/j.jprot.2011.05.009 · DOI
- 10.1093/database/baq020 · DOI · Paper page
- 10.1007/978-1-60761-977-2_4 · DOI
- 10.1093/nar/gkq973 · DOI · Paper page
- 10.1093/nar/gkn892 · DOI · Paper page
- 10.1039/C1MB05340J · DOI · Paper page
- 10.1002/0471250953.bi0121s41 · DOI
- 10.1016/1044-0305(94)80016-2 · DOI · Paper page
- 10.1161/CIRCRESAHA.113.301151 · DOI
- 10.1039/c0ib00078g · DOI
- 10.3389/fpls.2012.00123 · DOI
- 10.1093/bioinformatics/btm417 · doi:10.1093/bioinformatics/btm417
- UniProt C., 2014, Activities at the Universal Protein Resource (UniProt), Nucl. Acids Res., 42, D191, 10.1093/nar/gkt1140 · DOI · Paper page
- 10.1093/nar/gkr1122 · DOI · Paper page
- 10.1074/mcp.O113.034181 · DOI · Paper page
- 10.1126/science.1137614 · DOI · Paper page
- 10.1126/science.317.5843.1324 · DOI
- 10.1021/pr900349r · DOI · Paper page
- 10.1371/journal.pone.0013181 · DOI · Paper page
- 10.1074/mcp.A110.006387-1 · doi:10.1074/mcp.a110.006387-1
- 10.1074/mcp.M112.019760 · DOI
- 10.1038/nmeth.2106 · DOI
- 10.1016/j.jprot.2014.04.018 · DOI
- 10.1089/omi.2011.0143 · DOI
- 10.1074/mcp.M111.008490 · DOI
- 10.1021/pr070461k · DOI
- 10.1002/pmic.200700761 · DOI
- 10.1007/s00216-012-5940-8 · DOI
- 10.1002/pmic.201000602 · DOI
- 10.1002/pmic.201100363 · DOI
- 10.1186/1471-2105-12-70 · DOI
- 10.1002/pmic.201000595 · DOI · Paper page
- 10.1002/pmic.201000704 · DOI
- 10.1074/mcp.M113.031203 · DOI
- 10.1002/pmic.201300118 · DOI
- 10.1016/j.jprot.2013.01.019 · DOI
- 10.1002/pmic.201200514 · DOI
- 10.1093/nar/gkr937 · DOI
Cited by
Manzoni, C., Kia, D. A., Vandrovcova, J., Hardy, J., Wood, N. W., Lewis, P. A., & Ferrari, R. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbw114 · Cited by 804
Wang, M., Herrmann, C. J., Simonovic, M., Szklarczyk, D., & von Mering, C. (2015) · PROTEOMICS · doi:10.1002/pmic.201400441 · Cited by 537
The diverse and expanding role of mass spectrometry in structural and molecular biology
Lössl, P., van de Waterbeemd, M., & Heck, A. J. (2016) · The EMBO Journal · doi:10.15252/embj.201694818 · Cited by 222
Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing
(2018) · doi:10.1021/acs.jproteome.8b00359 · Cited by 85
On behalf of the Brotman Baty Institute Mutational Scanning Working Group, Gelman, H., Dines, J. N., Berg, J., Berger, A. H., Brnich, S., Hisama, F. M., James, R. G., Rubin, A. F., Shendure, J., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0698-7 · Cited by 81
Metadata
- Authors
- Perez‐Riverol, Y., Alpi, E., Wang, R., Hermjakob, H., & Vizcaíno, J. A.
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2015-03
- Volume
- 15
- Issue
- 5-6
- Pages
- 930-950
- ISSN
- 1615-9853 (ISSN-L)
- 1615-9861
- OpenAlex
- W2073022274
- Cited by
- 200
- References
- 17
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- European Commission (260558, 262067)
- Wellcome Trust (WT101477MA)
- Biotechnology and Biological Sciences Research Council (BB/K01997X/1, BB/I00095X/1)
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