Publications

In the press

Method of the Year 2021.
Nature Methods (2022)

Interview.
Deutschlandfunk Kultur (2021)

Proteomic analysis with MaxDIA.
Nature Methods (2021)

When computational pipelines go ‘clank’.
Nature Methods (2020)

A dream of single-cell proteomics.
Nature Methods (2019)

Clinical Proteomics and Computational Biochemistry.
Technology Networks (2019)

Interview.
Technology Networks (2019)

20 years of Nature Biotechnology research tools.
Nature Biotechnology (2016)

find more on our Press Page

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2022:

Cox, J. (2022) Prediction of peptide mass spectral libraries with machine learning. Nature Biotechnology, doi: 10.1038/s41587-022-01424-w.

Yilmaz, S., Busch, F., Nagaraj, N. and Cox, J. (2022) Accurate and automated high-coverage identification of chemically cross-linked peptides with MaxLynx. Analytical Chemistry, doi:10.1021/acs.analchem.1c03688.

Hamzeiy, H., Ferretti, D., Robles, M.S. and Cox, J. (2022) Perseus plugin Metis for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs. Cell Reports Methods, doi:10.1016/j.crmeth.2022.100198.

Sinitcyn, P., Gerwien, M. and Cox, J. (2022) MaxQuant Module for the Identification of Genomic Variants Propagated into Peptides. In: Geddes-McAlister, J. (eds) Proteomics in Systems Biology. Methods in Molecular Biology, vol 2456. Humana, New York, NY. doi:10.1007/978-1-0716-2124-0_23.

Frohn, B., Härtel, T., Cox, J. and Schwille, P. (2022) Tracing back variations in archaeal ESCRT-based cell division to protein domain architectures. PLOS One, doi:10.1371/journal.pone.0266395.

Lin, M.H., Wu, P.S., Wong, T.H., Lin, I.Y., Lin, J., Cox, J. and Yu, S.H. (2022) Benchmarking differential expression, imputation and quantification methods for proteomics data. Briefings in Bioinformatics, doi: 10.1093/bib/bbac138.

Trulsson, F., Akimov, V., Robu, M., van Overbeek, N., Pérez Berrocal, D.A., Shah, R.G., Cox, J., Shah, G.M., Blagoev, B. and Vertegaal, A.C.O. (2022) Deubiquitinating enzymes and the proteasome regulate preferential sets of ubiquitin substrates. Nature Communications 13, 2736, doi: 10.1038/s41467-022-30376-7.

Gatto, L., Aebersold, R., Cox, J. et al. (2022) Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments. bioRxiv, doi:10.48550/arXiv.2207.10815.

Huffman, R.G., Leduc, A., Wichmann, C., Di Gioia, M., Borriello, F., Specht, H., Derks, J., Khan, S., Emmott, E., Petelski, A.A., Perlman, D.H., Cox. J., Zanoni, I. and Slavov, N. (2022) Prioritized single-cell proteomics reveals molecular and functional polarization across primary macrophages. bioRxiv, doi: 10.1101/2022.03.16.484655.

Jayavelu, A.K., Wolf, S., Buettner, F. et al. (2022) The Proteogenomic Subtypes of Acute Myeloid Leukemia. Cancer Cell 40, 301-317, doi:10.1016/j.ccell.2022.02.006.

2021:

Sinitcyn, P., Hamzeiy, H., Salinas Soto, F., Itzhak, D., McCarthy, F., Wichmann, C., Steger, M., Ohmayer, U., Distler, U., Kaspar-Schoenefeld, S., Prianichnikov, N., Yılmaz, S., Rudolph, J.D., Tenzer, S., Perez-Riverol, Y., Nagaraj, N., Humphrey, S.J. and Cox, J. (2021) MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nature Biotechnology 39, 1563-1573, doi:10.1038/s41587-021-00968-7.

Gutenbrunner, P., Kyriakidou, P., Welker, F. and Cox, J. (2021) Spectrum graph-based de-novo sequencing algorithm MaxNovo achieves high peptide identification rates in collisional dissociation MS/MS spectra. bioRxiv, https://doi.org/10.1101/2021.09.04.458985.

Traube, F.R., Özdemir, D., Sahin, H., Scheel, C., Glück, A.F., Geserich, A.S., Oganesian, S., Kostidis, S., Iwan, K., Rahimoff, R., Giorgio, G., Müller, M., Spada, F., Biel, M., Cox, J., Giera, M., Michalakis, S. and Carell, T. (2021) Redirected nuclear glutamate dehydrogenase supplies Tet3 with α-ketoglutarate in neurons. Nat Commun 12, 4100 (2021). doi:10.1038/s41467-021-24353-9.

2020:

Prianichnikov, N., Koch, H., Koch, S., Lubeck, M., Heilig, R., Brehmer, S., Fischer, R. and Cox, J. (2020) MaxQuant software for ion mobility enhanced shotgun proteomics. Mol. Cell. Proteomics 19, 1058-69, doi:10.1074/mcp.TIR119.001720.

Yu, S.H., Kyriakidou, P. and Cox, J. (2020) Isobaric matching between runs and novel PSM-level normalization in MaxQuant strongly improve reporter ion-based quantification. J. Proteome Res. 19, 3945-54, doi:10.1021/acs.jproteome.0c00209.

Yu, S.H., Ferretti, D., Schessner, J.P., Rudolph, J.D., Borner, G.H.H and Cox, J. (2020) Expanding the Perseus Software for Omics Data Analysis With Custom Plugins. Curr. Protoc. Bioinformatics 71, e105, doi:10.1002/cpbi.105.

Welker, F., Ramos-Madrigal, J., Gutenbrunner, P. et al. (2020) The dental proteome of Homo antecessor. Nature 580 (7802), 235-238, doi:10.1038/s41586-020-2153-8.

Trentini, D.B., Pecoraro, M., Tiwary, S. et al. (2020) Role for ribosome-associated quality control in sampling proteins for MHC class I-mediated antigen presentation. PNAS 117, 4099-4108. doi:10.1073/pnas.1914401117.

Kjell, J., Fischer-Sternjak, J., Thompson, A.J.. et al. (2020) Defining the adult neural stem cell niche proteome identifies key regulators of adult neurogenesis. Cell stem cell 26, 277-293. doi:10.1073/pnas.1914401117.

Leitner, A., Bonvin, A.M.J.J., Borchers, C.H.. et al. (2020) Towards Increased Reliability, Transparency and Accessibility in Crosslinking Mass Spectrometry. Structure 28, 1259-68, doi:10.1016/j.str.2020.09.011.

Cox, J. and Krupa, G (2020) Advanced Computational Methods Drive Large-Scale Data Analysis in 4D-Proteomics: MaxQuant software enables high-throughput proteomics by streamlining the analysis of raw data. Genetic Engineering & Biotechnology News 40, 22-23, doi:10.1089/gen.40.12.07.

2019:

Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., and Cox, J. (2019) High quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat. Methods, doi:10.1038/s41592-019-0427-6.

Rudolph, J.D. and Cox, J. (2019) A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis. J. Proteome Res. 18, 2052–2064, doi:10.1021/acs.jproteome.8b00927.

Welker, F., Ramos-Madrigal, J., Kuhlwilm, M. et al. (2019) Enamel proteome shows that Gigantopithecus was an early diverging pongine. Nature. doi:10.1038/s41586-019-1728-8.

Frottin, F., Schueder, F., Tiwary, S., Gupta, R., Körner, R., Schlichthaerle, T., Cox, J., Jungmann, R., Hartl, F.U. and Hipp, M.S. (2019) The nucleolus functions as a phase-separated protein quality control compartment. Science, doi:10.1126/science.aaw9157.

Brüning, F. et al. (2019) Sleep-wake cycles drive daily dynamics of synaptic phosphorylation. Science 11, 366, doi:10.1126/science.aav3617.

Mordret, E., Yehonadav, A., Barnabas, G.D., Cox, J., Dahan, O., Geiger, T., Lindner, A.B. and Pilpel, Y. (2019) Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors. Molecular Cell 75, 427-441. https://doi.org/10.1101/255943.

Perez-Riverol, Y. et al. (2019) The PRIDE database and related tools and resources in 2019: Improving support for quantification data. Nucleic Acids Research. doi:10.1093/nar/gky1106.

Iacobucci, C. et al. (2019) First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study. Anal. Chem. 91, 6953-6961, doi:10.1021/acs.analchem.9b00658.

Wichmann, C., Meier, F., Virreira Winter, S., Brunner, A.D., Cox, J. and Mann, M. (2019) MaxQuant.Live enables global targeting of more than 25,000 peptides. Mol. Cell. Proteomics 18, 982-994, doi:10.1074/mcp.TIR118.001131.

2018:

Sinitcyn, P., Rudolph, J.D. and Cox, J. (2018). Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data. Annual Review of Biomedical Data Science 1, 207-234.

Sinitcyn, P., Tiwary, S., Rudolph, J.D., Gutenbrunner, P., Wichmann, C., Yilmaz, S., Hamzeiy, H. and Cox, J. (2018). MaxQuant goes Linux. Nature Methods 15, 401.

Tyanova, S. and Cox, J. (2018) Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research. Cancer systems biology, 133.

Cappellini, E., Prohaska, A., Racimo, F., Welker, F., Winther Pedersen, M., Allentoft, M.E., de Barros Damgaard, P., Gutenbrunner, P., Dunne, J., Hammann, S., Roffet-Salque, M., Ilardo, M., Moreno-Mayar, J.V., Wang, Y., Sikora, M., Vinner, L., Cox, J., Evershed, R.P. and Willerslev, E. (2018). Ancient Biomolecules and Evolutionary Inference. Annual Review of Biochemistry 87, 1029.

Overmyer, K.A., Tyanova, S., Hebert, A.S., Westphall, M.S., Cox, J. and Coon, J.J. (2018) Multiplexed proteome analysis with neutron-encoded stable isotope labeling in cells and mice. Nature protocols 13, 293.

Shurtleff, M.J., Itzhak, D.N., Hussmann, J.A., Schirle Oakdale, N.T., Costa, E.A., Jonikas, M., Weibezahn, J., Popova, K.D., Jan, C.H., Sinitcyn, P., Vembar, S.S., Hernandez, H., Cox, J., Burlingame, A.L., Brodsky, J., Frost, A., Borner, G.H.H. and Weissman, J.S. (2018) The ER membrane protein complex interacts cotranslationally to enable biogenesis of multipass membrane proteins. eLife 7:e37018.

Iglesias-Gato, D., Thysell, E., Tyanova, S., Crnalic, S., Santos, A., Lima, T.S., Geiger, T., Cox, J., Widmark, A., Bergh, A., Mann, M., Flores-Morales, A. and Wikström, P. (2018) The proteome of prostate cancer bone metastasis reveals heterogeneity with prognostic implications. Clinical Cancer Research 1229.2018.

Virreira Winter, S., Meier, F., Wichmann, C., Cox, J., Mann, M. and Meissner, F. (2018) EASI-tag enables accurate multiplexed and interference-free MS2-based proteome quantification. Nature Methods 15, 527.

Meier, F., Brunner, A.D., Koch, S., Koch, H., Lubeck, M., Krause, M., Goedecke, N., Decker, J., Kosinski, T., Park, M.A., Bache, N., Hoerning, O., Cox, J., Räther, O. and Mann, M. (2018) Online parallel accumulation − serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol Cell Proteomics. doi:10.1074/mcp.TIR118.000900.

Meier, F., Geyer, P.E., Virreira Winter, S., Cox, J. and Mann, M. (2018) BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nature Methods 15, 440.

Krahmer, N. et al. (2018) Organellar Proteomics and Phospho-Proteomics Reveal Subcellular Reorganization in Diet-Induced Hepatic Steatosis. Dev Cell 47, 205-221.

2017:

Hamzeiy, H. and Cox, J. (2017) What computational non-targeted mass spectrometry-based metabolomics can gain from shotgun proteomics. Current Opinion in Biotechnology 43, 141-6. doi:10.1016/j.copbio.2016.11.014.

Itzhak, D.N., Davies, C., Tyanova, S., Mishra, A., Williamson, J., Antrobus, R., Cox, J., Weekes, M.P. and Borner, G.H.H. (2017) A Mass Spectrometry-Based Approach for Mapping Protein Subcellular Localization Reveals the Spatial Proteome of Mouse Primary Neurons. Cell reports 20, 2706. doi:10.1016/j.celrep.2017.08.063.

Hughes, M.E. et al. (2017) Guidelines for Genome-Scale Analysis of Biological Rhythms. Journal of biological rhythms 32, 380.

Hartl, M., Füßl, M., Boersema, P.J., Jost, J.O., Kramer, K., Bakirbas, A., Sindlinger, J., Plöchinger, M., Leister, D., Uhrig, G., Moorhead, G.B.G., Cox, J., Salvucci, M.E., Schwarzer, D., Mann, M. and Finkemeier, I. (2017) Lysine acetylome profiling uncovers novel histone deacetylase substrate proteins in Arabidopsis. Molecular systems biology 13, 949. doi:10.15252/msb.20177819.

Hosp, F., Gutiérrez-Ángel, S., Schaefer, M.H., Cox, J., Meissner, F., Hipp, M.S., Hartl, F.U., Klein, R., Dudanova, I. and Mann, M. (2017) Spatiotemporal Proteomic Profiling of Huntington’s Disease Inclusions Reveals Widespread Loss of Protein Function. Cell reports 21, 2291. doi:10.1016/j.celrep.2017.08.063.

2016:

Tyanova, S., Temu, T. and Cox, J. (2016) The MaxQuant platform for mass spectrometry-based shotgun proteomics. Nature Protocols 11(12), 2301-19, doi:10.1038/nprot.2016.136.

Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M., Geiger, T., Mann, M. and Cox, J. (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods 13 (9), 731-40, doi:10.1038/nmeth.3901.

Temu, T., Mann, M., Raeschle, M. and Cox, J. (2016) Homology-driven assembly of Non-redundant protein Sequence Sets (NOmESS) for mass spectrometry. Bioinformatics, btv756. doi:10.1093/bioinformatics/btv756.

Tyanova S., Albrechsten R., Kronqvist P., Cox J., Mann M. and Geiger T. (2016) Proteomic maps of breast cancer subtypes. Nat Commun 7: 10259. doi:10.1038/ncomms10259.

Itzhak, D.N., Tyanova, S., Cox, J. and Borner, G.H.H. (2016) Global, quantitative and dynamic mapping of protein subcellular localization. eLife 5, e16950. doi:10.7554/eLife.

Chen, Z.A., Fischer, L., Cox, J. and Rappsilber, J. (2016) Quantitative cross-linking/mass spectrometry using isotope-labeled cross-linkers and MaxQuant. Mol Cell Proteomics 15, 2769-78. doi:10.1074/mcp.M115.056481.

Bassani-Sternberg, M., Bräunlein, E., Klar, R., Engleitner, T., Sinitcyn, P., Audehm, S., Straub, M., Weber, J., Slotta-Huspenina, J., Specht, K., Martignoni, M.E., Werner, A., Hein, R., Busch, D.H., Peschel, C., Rad, R., Cox, J., Mann, M. and Krackhardt, A.M. (2016) Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat Commun 7: 13404. doi:10.1038/ncomms13404.

Sacco, F., Humphrey, S.J., Cox, J, Mischnik, M., Schulte, A., Klabunde, T., Schäfer, M. and Mann, M. (2016) Glucose-regulated and drug-perturbed phosphoproteome reveals molecular mechanisms controlling insulin secretion. Nat Commun 7: 13250. doi:10.1038/ncomms13250.

Grassl, N., Kulak, N.A., Pichler, G., Geyer, P.E., Jung, J., Schubert, S., Sinitcyn, P., Cox, J. and Mann, M. (2016) Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome. Genome medicine 8(1), 1. doi:10.1186/s13073-016-0293-0.

Iglesias-Gato, D., Wikström, P., Tyanova, S., Lavallee, C., Thysell, E., Carlsson, J., Hägglöf, C., Cox, J., Andrén, O., Stattin, P., Egevad, L., Widmark, A., Bjartell, A., Collins, C.C., Bergh, A., Geiger, T., Mann, M. and Flores-Morales, A. (2016) The proteome of primary prostate cancer. European Urology 69(5), 942-52. doi:10.1016/j.eururo.2015.10.053.

2015:

Tyanova, S., Temu, T., Carlson, A., Sinitcyn, P., Mann, M. and Cox, J. (2015) Visualization of LC-MS/MS proteomics data in MaxQuant. Proteomics 15 (8), 1453-6. doi:10.1002/pmic.201400449.

Hein, M.Y., Hubner, N.C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I.A., Weisswange, I., Mansfeld, J., Buchholz, F., Hyman, A. and Mann, M. (2015) A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances. Cell 163(3), 712-23.

Räschle, M., Smeenk, G., Hansen, R.K., Temu, T., Oka, Y., Hein, M.Y., Nagaraj, N., Long, D.T., Walter, J.C., Hofmann, K., Storchova, Z., Cox, J., Bekker-Jensen, S., Mailand, N. and Mann, M. (2015) Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links. Science 348 .1253671.

Schölz, C., Weinert, B.T., Wagner, S.A., Beli, P., Miyake, Y., Qi, J., Jensen, L.J., Streicher, W., McCarthy, A.R., Westwood, N.J., Lain, S., Cox, J., Matthias, P., Mann, M., Bradner, J.E. and Choudhary, C. (2015) Acetylation site specificities of lysine deacetylase inhibitors in human cells. Nature Biotechnol 33(4), 415-23.

Beck, S., Michalski, A., Raether, O., Lubeck, M., Kaspar, S., Goedecke, N., Baessmann, C., Hornburg, D., Meier, F., Paron, I., Kulak, N.A., Cox, J. and Mann, M. (2015) The impact II, a very high resolution quadrupole time-of-flight instrument for deep shotgun proteomics. Mol Cell Proteomics, mcp.M114.047407.

Deeb, S., Tyanova, S., Hummel, M., Schmidt-Supprian, M., Cox, J. and Mann, M. (2015) Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles. Mol Cell Proteomics 14, 2947-60.

Deshmukh, A.S., Murgia, M., Nagaraj, N., Treebak, J.T., Cox, J. and Mann, M. (2015) Deep proteomics of mouse skeletal muscle enables quantitation of protein isoforms, metabolic pathways and transcription factors. Mol Cell Proteomics, mcp.M114.044333.

Deshmukh, A.S., Cox, J., Jensen, L.J., Meissner, F. and Mann, M. (2015) Secretome analysis of lipid induced insulin resistance in skeletal muscle cells by a combined experimental and bioinformatics workflow. J Proteome Res. 14 (11), 4885-95.

Mischnik, M., Sacco, F., Cox, J., Schneider, H.C., Schäfer, M., Hendlich, M., Crowther, D., Mann, M. and Klabunde, T. (2015) IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data. Bioinformatics, btv699.

2014:

Cox, J., Hein, M.Y., Luber, C.A., Paron, I., Nagaraj, N. and Mann, M. (2014) Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 13(9), 2513-26.

Sharma, K., D’Souza, R.C.J., Tyanova, S., Wisniewski, J.R., Schaab, C., Cox, J. and Mann, M. (2014) Ultra-deep human phosphoproteome reveals distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Reports 8(5), 1583-94.

Tyanova, S., Mann, M. and Cox, J. MaxQuant for in-depth analysis of large SILAC datasets. in Methods in Molecular Biology: Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC): Methods and Protocols, Springer, 351-64.

Robles, M.S., Cox, J. and Mann, M. (2014) In-vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genetics 10(1):e1004047.

Deeb, S.J., Cox, J., Schmidt-Supprian, M. and Mann, M. (2014). N-linked glycosylation for in-depth cell surface proteomics of diffuse large B-cell lymphoma subtypes. Mol Cell Proteomics 13(1), 240-251.

Azimifar, S.B., Nagaraj, N., Cox, J. and Mann, M. (2014). Cell-type-resolved quantitative proteomics of murine liver. Cell Metabolism 20(6), 1076-87.

Wisniewski, J.R., Hein, M.Y., Cox, J. and Mann, M. (2014). A ‘proteomic ruler’ for protein copy number and concentration estimation without spike-in standards. Mol Cell Proteomics 13(12), 1076-87.

Griss, J., Jones, A.R., Sachsenberg, T., Walzer, M., Gatto, L., Hartler, J., Thallinger, G.G., Salek, R.M., Steinbeck, C., Neuhauser, N., Cox, J., Neumann, S., Fan, J., Reisinger, F., Xu, Q.W., Bandeira, N., Xenarios, I., Kohlbacher, O., Vizcaíno, J.A. and Hermjakob, H. (2014) The mzTab data exchange format: communicating MS-based proteomics and metabolomics experimental results to a wider audience. Mol Cell Proteomics.

2013:

Marx, H., Lemeer, S., Schliep, J.E., Matheron, L., Mohammed, S., Cox, J., Mann, M., Heck, A. and Kuster, B. (2013) A large synthetic phosphopeptide library for mass spectrometry based proteomics. Nature Biotechnol 31, 557-564.

Oppermann F.S., Klammer, M., Bobe, C. , Cox, J., Schaab, C., Tebbe, A. and Daub, H. (2013) Comparison of SILAC and mTRAQ quantification for phosphoproteomics on a quadrupole orbitrap mass spectrometer. J Proteome Res 12, 4089-4100.

Tyanova, S., Cox, J., Olsen, J., Mann, M. and Frishman, D. (2013) Phosphorylation variation during the cell cycle scales with structural propensities of proteins. PLoS Comput Biol 9:e1002842.

Mann, M., Kulak, N., Nagaraj, N. and Cox, J. (2013) The Coming Age of Complete, Accurate and Ubiquitous Proteomes. Molecular Cell 49, 583-590.

Geiger, T., Velic, A., Macek, B., Lundberg, E., Kampf, C., Nagaraj, N., Uhlen, M., Cox, J. and Mann, M. (2013) Initial quantitative proteomic map of twenty-eight mouse tissues using the SILAC mouse. Mol Cell Proteomics 12, 1709-1722.

Zanivan, S., Meves, A., Behrendt, K., Schoof, E.M., Neilson, L.J., Cox, J., Tang, H.R., Kalna, G., van Ree, J.H., van Deursen, J.M., Trempus, C.S., Machesky, L.M., Linding. R., Wickström, S.A., Fässler, R. and Mann, M. (2013) In Vivo SILAC-Based Proteomics Reveals Phosphoproteome Changes during Mouse Skin Carcinogenesis. Cell Rep. 3, 552-566.

Neuhauser, N., Nagaraj, N., McHardy, P., Zanivan, S., Scheltema, R., Cox, J. and Mann, M. (2013) High performance computational analysis of large-scale datasets to assess incremental contribution to coverage of human genome, J Proteome Res 12, 2858-2864.

Perez-Riverol Y, Hermjakob H, Kohlbacher O, Martens L, Creasy D, Cox J, Leprevost F, Shan BP, Pérez-Nueno VI, Blazejczyk M, Punta M, Vierlinger K, Valiente P, Leon K, Chinea G, Guirola O, Bringas R, Cabrera G, Guillen G, Padron G, Gonzalez LJ and Besada V. (2013) Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 Workshop report. J Proteomics 87, 134-138.

2012:

Cox, J. and Mann, M. (2012) 1D and 2D annotation enrichment: A statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics, 13 Suppl 16:S12.

Stingele, S., Stoehr, G., Peplowska, K., Cox, J., Mann, M. And Storchova, Z. (2012) Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Molecular Systems Biology 8, 608.

Hein, M.Y., Sharma, K., Cox, J. and Mann, M. (2012) Proteomic analysis of cellular systems. in Handbook of Systems Biology: Concepts and Insights, Academic Press.

Deeb, S.J., D’Souza, R.C.J., Cox, J., Schmidt-Supprian, M. and Mann, M. (2012). Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles. Mol Cell Proteomics 11(5), 77-89.

Neuhauser, N., Michalski, A., Cox, J. and Mann, M. (2012). Expert System for Computer Assisted Annotation of MS/MS Spectra. Mol Cell Proteomics 11, 1500-9.

Geiger, T., Madden, S.F., Gallagher, W.M., Cox, J. and Mann, M. (2012). Proteomic portrait of human breast cancer progression identifies novel prognostic markers. Cancer Res 72, 2428-39.

Graumann, J., Scheltema, R.A., Zhang, Y., Cox, J. and Mann, M. (2012). A framework for intelligent data acquisition and real-time database searching for shotgun proteomics. Mol Cell Proteomics 11(3):M111.013185.

Schaab, C., Geiger, T., Stoehr, G., Cox, J. and Mann, M. (2012). Analysis of high accuracy, quantitative proteomics data in the MaxQB database. Mol Cell Proteomics 11(3):M111.014068.

Geiger, T., Wehner, A., Schaab, C., Cox, J. and Mann, M. (2012). Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol Cell Proteomics 11(3):M111.014050.

Sharma, K., Vabulas, R.M., Macek, B., Pinkert, S., Cox, J., Mann, M. and Hartl, F.U. (2012). Quantitative proteomics reveals that Hsp90 inhibition preferentially targets kinases and the DNA damage response. Mol Cell Proteomics 11(3):M111.014654.

Nagaraj, N., Kulak, N.A., Cox, J., Neuhauser, N., Mayr, K., Hoerning, O., Vorm, O. and Mann, M. (2012). System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol Cell Proteomics 11(3):M111.013722.

Michalski, A., Damoc, E., Lange, O., Denisov, E., Nolting, D., Muller, M., Viner, R., Schwartz, J., Remes, P., Belford, M., Dunyach, J.J., Cox, J., Horning, S., Mann, M. and Makarov, A. (2012). Ultra high resolution linear ion trap mass spectrometer (Orbitrap Elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes. Mol Cell Proteomics 11(3):O111.013698.

Bennetzen, M.V., Cox, J., Mann, M. and Andersen, J.S. (2012) PhosphoSiteAnalyzer: A bioinformatic platform for deciphering phospho proteomes using kinase predictions retrieved from NetworKIN. J Proteome Res 11, 3480-6.

Michalski, A., Neuhauser, N., Cox, J. and Mann, M. (2012) A systematic investigation into the nature of tryptic HCD spectra. J Proteome Res 11, 5479-91.

2011:

Cox, J., Neuhauser, N., Michalski, A., Scheltema, R.A., Olsen, J.V. and Mann, M. (2011) Andromeda – a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10, 1794-1805.

Cox, J. and Mann, M. (2011). Quantitative, high resolution proteomics for data-driven systems biology. Annual Review of Biochemistry 80, 273-299.

Cox, J., Michalski, A. and Mann, M. (2011). Software lock mass by two-dimensional minimization of peptide mass errors. J Am Soc Mass Spectrom 22, 1373-80.

Cox, J., Heeren, R.M.A., James, P., et al. (2011) Facing challenges in Proteomics today and in the coming decade: Report of Roundtable Discussions at the 4th EuPA Scientific Meeting, Portugal, Estoril 2010. J Proteomics 75, 4-17.

Wagner S.A., Beli, P., Weinert, B.T., Nielsen, M.L., Cox, J. and Mann, M. (2011). A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol Cell Proteomics 10.

Thakur, S.S., Geiger, T., Chatterjee, B., Bandilla, P., Fröhlich, F., Cox, J. and Mann, M. (2011). Deep proteome coverage in single-run liquid chromatography tandem mass spectrometry. Mol Cell Proteomics 10.

Nagaraj, N., Wisniewski, J.R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Paabo. S. and Mann, M. (2011) Deep proteome and transcriptome mapping of a human cancer cell line. Molecular Systems Biology 7, 548.

Michalski, A., Damoc, E., Hauschild, J.P., Lange, O., Wieghaus, A., Makarov, A., Nagaraj, N., Cox, J., Mann, M. and Horning, S. (2011). Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol Cell Proteomics 10.

Michalski, A., Cox, J. and Mann, M. (2011). More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data dependent LC MS/MS. J Proteome Res 10, 1785-93.

Geiger, T., Wisniewski, J.R., Cox, J., Zanivan, S., Kruger, M., Ishihama, Y. and Mann, M. (2011). Use of Stable isotope labeling by amino acids in cell culture (SILAC) as an internal standard in quantitative proteomics. Nature Protocols 6, 147-157.

2010:

Geiger, T., Cox, J. and Mann, M. (2010). Proteomics on an orbitrap benchtop mass spectrometer using all ion fragmentation. Mol Cell Proteomics 9, 2252-61.

Luber, C.A., Cox, J., Lauterbach, H., Fancke, B., Selbach, M., Tschopp, J., Akira, S., Wiegand, M., Hochrein, H., O’Keeffe, M. and Mann, M. (2010). Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity 32, 279-289.

Geiger, T., Cox, J. and Mann, M. (2010). Proteomic changes resulting from gene copy number variations in cancer cells. PLoS Genetics 6.

Lundberg, E., Fagerberg, L., Klevebring, D., Matic, I., Geiger, T., Cox, J., Algenas. C., Lundeberg, J., Mann, M. and Uhlen, M. (2010) Defining the transcriptome and proteome in three functionally different human cell lines. Molecular Systems Biology 6, 450.

Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R. and Mann, M. (2010). Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7, 383-5.

Olsen, J.V., Vermeulen, M., Santamaria, A., Kumar, C., Miller, M.L., Jensen, L.J., Gnad, F., Cox, J., Jensen, T.S., Nigg, E.A. Brunak, S. and Mann, M. (2010). Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci Signal 3(104):ra3.

Schreiber T.B., Mäusbacher, N., Keri, G., Cox, J. and Daub, H. (2010). An integrated phosphoproteomics workflow reveals extensive network regulation in early lysophosphatidic acid signaling. Mol Cell Proteomics 9, 1047-62.

Nagaraj, N., D’Souza, R.C.J., Cox, J., Olsen, J.V. and Mann, M. (2010). Feasibility of large scale phosphoproteomics with HCD fragmentation. J Proteome Res 9, 6786-94.

Hubner, N.C., Bird, A.W., Cox, J., Splettstoesser, B., Bandilla, P., Poser, I., Hyman, A. and Mann, M. (2010). Quantitative proteomics combined with BAC TransgeneOmics reveals in-vivo protein interactions. Journal of Cell Biology 189, 739-754.

Mortensen, P., Gouw, J.W., Olsen, J.V., Ong, S.E., Rigbolt, K.T., Bunkenborg, J., Cox, J., Foster, L., Heck, A.J., Blagoev, B., Andersen, J.S. and Mann, M. (2010). MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 9, 393-401.

Gnad, F., Ren, S., Choudhary, C., Cox, J. and Mann, M. (2010). Predicting posttranslational lysine acetylation using support vector machines. Bioinformatics 26, 1666-8.

2009:

Cox, J., Matic, I., Hilger, M., Nagaraj, N., Selbach, S., Olsen, J.V. and Mann, M. (2009). A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nature Protocols 4, 698-705.

Cox, J. and Mann, M. (2009). Computational principles of determining and improving mass precision and accuracy for proteome measurements in an orbitrap. J Am Soc Mass Spectrom 20, 1477-85.

Choudhary C., Olsen, J.V., Brandts, C., Cox, J., Reddy, P.N., Böhmer, F.D., Gehrke, V., Schmidt-Arras, D.E., Berdel, W.E., Müller-Tidow, C., Mann, M. and Serve, H. (2009). Mislocalized activation of oncogenic RTKs switches downstream signaling outcomes. Mol Cell 36, 326-39

Sharma, K., Weber, C., Bairlein, M., Greff, Z., Keri, G., Cox, J., Olsen, J.V. and Daub, H. (2009). Proteomics strategy for quantitative protein interaction profiling in cell extracts. Nat Methods 6, 741-4.

Golebiowski, F., Matic, I., Tatham, M.H., Cole, C., Yin, Y., Nakamura, A., Barton, G.J., Mann, M. and Hay, R.T. (2009). System-wide changes to SUMO modifications in response to heat shock. Sci Signal 2(72):ra24. 

Gnad, F., de Godoy, L.M., Cox, J., Neuhauser, N., Ren, S., Olsen, J.V. and Mann, M. (2009). High-accuracy identification and bioinformatic analysis of in vivo protein phosphorylation sites in yeast. Proteomics 9, 4642-52

2008:

Cox, J. and Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotech 26, 1367-72.

Cox, J., Hubner, N.C. and Mann, M. (2008). How much sequence information is contained in ion trap tandem mass spectra? J Am Soc Mass Spectrom 19, 1813-20.

de Godoy, L.M.F., Olsen, J.V., Cox, J., Nielsen, M.L., Hubner, N.C., Fröhlich, F., Walther, T.C. and Mann, M. (2008). Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251-4.

Bonaldi, T., Straub, T., Cox, J., Kumar, C., Becker, P.B. and Mann, M. (2008). Combined use of RNAi and quantitative proteomics to study gene function in Drosophila. Mol Cell 31, 762-72.

Schimmel, J., Larsen, K.M., Matic, I., van Hagen, M., Cox, J., Mann, M., Andersen, J.S. and Vertegaal, A.C. (2008). The ubiquitin-proteasome system is a key component of the SUMO-2/3 cycle. Mol Cell Proteomics 7, 2107-22.

Nielsen, M.L., Vermeulen, M., Bonaldi, T., Cox, J., Moroder, L. and Mann, M. (2008). Iodoacetamide-induced artifact mimics ubiquitination in mass spectrometry. Nat Methods 5(6), 459-60.

Waanders, L.F., Almeida, R., Prosser, S., Cox, J., Eikel, D., Allen, M.H., Schultz, G.A. and Mann, M. (2008). A novel chromatographic method allows online reanalysis of the proteome. Mol Cell Proteomics 7, 1452-9.

Graumann, J., Hubner, N.C., Kim, J.B., Ko, K., Moser, M., Kumar, C., Cox, J., Schöler, H. and Mann, M. (2008). Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol Cell Proteomics 7, 672-83.

Zanivan, S., Gnad, F., Wickstrom, S.A., Geiger, T., Macek, B., Cox, J., Fässler, R. and Mann, M. (2008) Solid tumor proteome and phosphoproteome analysis by high resolution mass spectrometry. J Proteome Res 7, 5314-26.

2007:

Cox, J. and Mann, M. (2007). Is Proteomics the New Genomics? Cell 130, 395-398.

Shi, R., Kumar, C., Zougman, A., Zhang, Y., Podtelejnikov, A., Cox, J., Wisniewski, J.R. and Mann, M. (2007). Analysis of the Mouse Liver Proteome Using Advanced Mass Spectrometry. J Proteome Res 6, 2963-72.

Smialowski, P., Martin-Galiano, A.J., Cox, J. and Frishman, D. (2007). Predicting experimental properties of proteins from sequence by machine learning techniques. Current protein & peptide science 8, 121-33.

Lu, A., Waanders, L.F., Almeida, R., Li, G., Allen, M., Cox, J., Olsen, J.V., Bonaldi, T. and Mann, M. (2007). Nanoelectrospray peptide mapping revisited: Composite survey spectra allow high dynamic range protein characterization without LCMS on an orbitrap mass spectrometer. International Journal of Mass Spectrometry 268, 158-67.

Gnad, F., Ren, S., Cox, J., Olsen, J.V., Macek, B., Oroshi, M. and Mann, M. (2007). PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol 8, R250.

2006:

Smialowski, P., Schmidt, T., Cox, J., Kirschner, A. and Frishman, D. (2006). Will my protein crystallize? A sequence-based predictor. Proteins 62, 343-55.

2005:

Cox, J., Gmünder, H., Hohn, A. and Rehrauer, H. (2005). Generation and Validation of a Reference System for Toxicogenomics DNA Microarray Experiments. In Handbook of Toxicogenomics, Jürgen Borlak, ed., pp. 185-200.

Pfleghaar, K., Heubes, S., Cox, J., Stemmann, O. and Speicher, M.R. (2005). Securin is not required for chromosomal stability in human cells. PLoS Biology 3, e416.

2003:

Chandrasekharan, S., Cox, J., Osborn, J.C. and Wiese, U.J. (2003). Meron-cluster approach to systems of strongly correlated electrons. Nucl Phys B 673, 405-36.

2001:

Berges, J. and Cox, J. (2001). Thermalization of quantum fields from time-reversal invariant evolution equations. Phys Lett B 517, 369-74.

Alford, M., Chandrasekharan, S., Cox, J. and Wiese, U.J. (2001). Solution of the complex action problem in the Potts model for dense QCD. Nucl Phys B 602, 61-86.

2000:

Cox, J. and Holland, K. (2000). Meron-cluster algorithms and chiral-symmetry breaking in a (2+1)D staggered fermion model. Nucl Phys B 583, 331-46.

Cox, J., Gattringer, C., Holland, K., Scarlet, B. and Wiese, U.J. (2000). Meron cluster solution of fermion and other sign problems. Nucl Phys Proc Suppl 83, 777-91.

Chandrasekharan, S., Cox, J., Holland, K. and Wiese, U.J. (2000). Meron-cluster simulation of a chiral phase transition with staggered fermions. Nucl Phys B 576, 481-500.

1999:

Cox, J., Jersak, J., Pfeiffer, H., Neuhaus, T., Stephenson, P.W. and Seyfried, A. (1999). Universality of the gauge-ball spectrum of the four-dimensional pure U(1) gauge theory. Nucl Phys B 545, 607-19.

Cox, J., Jersak, J. and Pfeiffer, H. (1999). Study of the order of the phase transition in pure U(1) gauge theory with Villain action. Nucl Phys Proc Suppl 73, 712-4.

1998:

Cox, J., Franzki, W., Jersak, J., Lang, C.B. and Neuhaus, T. (1998). Strongly coupled compact lattice QED with staggered fermions. Nucl Phys B 532, 315-336.

Cox, J., Franzki, W., Jersak, J., Lang, C.B., Neuhaus, T., Seyfried, A. and Stephenson, P.W. (1998). Scaling of gauge balls and static potential in the confinement phase of the pure U(1) lattice gauge theory. Nucl Phys Proc Suppl 63, 691-3.

1997:

Cox, J., Franzki, W., Jersak, J., Lang, C.B., Neuhaus, T. and Stephenson, P.W. (1997). Gauge-ball spectrum of the four-dimensional pure U(1) gauge theory. Nucl Phys B 499, 371-408.

Cox, J., Franzki, W., Jersak, J., Lang, C.B., Neuhaus, T. and Stephenson, P.W. (1997). Properties of the non-Gaussian fixed point in 4-d compact U(1) lattice gauge theory. Nucl Phys Proc Suppl 53, 696-8.