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Significance in the mass spectrometry analysis was determined by permutation-based FDR statistics with the s0 factor iteratively modified to exclude all identifications enriched in the control experiment, yielding an s0 of 1 at 1% FDR.
“Significance was determined by permutation-based FDR statistics122 where the s0 factor was iteratively modified to exclude all identifications enriched in the control experiment, yielding an s0 of 1 at 1% FDR.”
From Structure of the endosomal Commander complex linked to Ritscher-Schinzel syndrome (2023) · cites this paper as [122]
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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
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Mapping 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
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
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
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., & Weissman, J. S. (2009) · Science · doi:10.1126/science.1168978 · Cited by 3,853
COPASI—a COmplex PAthway SImulator
Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., & Kummer, U. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl485 · Cited by 2,618
Beausoleil, S. A., Villén, J., Gerber, S. A., Rush, J., & Gygi, S. P. (2006) · Nature Biotechnology · doi:10.1038/nbt1240 · Cited by 1,577
The BioPlex network: a systematic exploration of the human interactome
Huttlin, E. L., Ting, L., Bruckner, R. J., Gebreab, F., Gygi, M. P., Szpyt, J., Tam, S., Zarraga, G., Colby, G., Baltier, K., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.043 · Cited by 1,425
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
Self-citationComprehensive 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-citationThe 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
A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards
Wiśniewski, J. R., Hein, M. Y., Cox, J., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.037309 · Cited by 690
Self-citationHebert, A. S., Richards, A. L., Bailey, D. J., Ulbrich, A., Coughlin, E. E., Westphall, M. S., & Coon, J. J. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.034769 · Cited by 528
The Coming Age of Complete, Accurate, and Ubiquitous Proteomes
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Flagged first, then by citations
Research works printed but not linked: 56
- Altelaar, A.F., Munoz, J. & Heck, A.J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35–48 (2013). · DOI
- Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273–299 (2011). · DOI · Paper page
- Eng, J.K., McCormack, A.L. & Yates, J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994). This publication describes the earliest approach to correlating tandem mass spectra of peptides to theoretical fragment-ion series calculated from in silico digests of known protein sequences with the aim of identifying peptides and proteins. · DOI · Paper page
- Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999). · DOI
- Geer, L.Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958–964 (2004). · DOI
- Craig, R. & Beavis, R.C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–1467 (2004). · DOI
- Bern, M., Cai, Y. & Goldberg, D. Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry. Anal. Chem. 79, 1393–1400 (2007). · DOI
- Craig, R., Cortens, J.P. & Beavis, R.C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004). · DOI
- Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007). · DOI
- Deutsch, E.W. et al. Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics Clin. Appl. 9, 745–754 (2015). · DOI · Paper page
- Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008). Perseus has been developed in conjunction with MaxQuant, which comprises a complete quantitative workflow for the analysis of shotgun proteomics data, including support for a large variety of experimental techniques. · DOI · Paper page
- Vizcaino, J.A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013). · DOI · Paper page
- Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223–226 (2014). · DOI
- Munoz, J. et al. The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells. Mol. Syst. Biol. 7, 550 (2011). · DOI
- Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, yermed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014). Here the MaxLFQ algorithm for relative label-free protein quantification is described. It enabled many researchers to conduct large proteomics studies with complex experimental designs without the need for labeling their samples. · DOI · Paper page
- Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010). · DOI · Paper page
- Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001). A pioneering method is described for the robust detection of significantly changing biomolecules in large omics data sets. It uses repeated permutations of the data to determine FDRs. · DOI · Paper page
- Alter, O., Brown, P.O. & Botstein, D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97, 10101–10106 (2000). · DOI
- Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015). · DOI
- Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011). · DOI
- Olsen, J.V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010). · DOI
- Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–1594 (2014). · DOI
- Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015). · DOI · Paper page
- Hornbeck, P.V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015). · DOI · Paper page
- Tyanova, S., Cox, J., Olsen, J., Mann, M. & Frishman, D. Phosphorylation variation during the cell cycle scales with structural propensities of proteins. PLoS Comput. Biol. 9, e1002842 (2013). · DOI
- Hein, M.Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712–723 (2015). · DOI · Paper page
- Hubner, N.C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010). · DOI · Paper page
- Selbach, M. & Mann, M. Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK). Nat. Methods 3, 981–983 (2006). · DOI
- Keilhauer, E.C., Hein, M.Y. & Mann, M. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS). Mol. Cell. Proteomics 14, 120–135 (2015). · DOI
- Räschle, M. et al. DNA repair. Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links. Science 348, 1253671 (2015). · DOI
- Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998). · DOI
- Gauthier, N.P. et al. Cyclebase.org—a comprehensive multi-organism online database of cell-cycle experiments. Nucleic Acids Res. 36, D854–D859 (2008). · DOI
- Eser, P. et al. Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression. Mol. Syst. Biol. 10, 717 (2014). · DOI
- Partch, C.L., Green, C.B. & Takahashi, J.S. Molecular architecture of the mammalian circadian clock. Trends Cell Biol. 24, 90–99 (2014). · DOI · Paper page
- Robles, M.S., Cox, J. & Mann, M. In vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genet. 10, e1004047 (2014). · DOI · Paper page
- Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013). · DOI · Paper page
- Aviner, R., Shenoy, A., Elroy-Stein, O. & Geiger, T. Uncovering hidden layers of cell cycle regulation through integrative multi-omic analysis. PLoS Genet. 11, e1005554 (2015). · DOI
- Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016). · DOI
- Cox, J. & Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13, S12 (2012). · DOI
- Deeb, S.J. et al. Machine learning-based classification of diffuse large B-cell lymphoma patients by their protein expression profiles. Mol. Cell. Proteomics 14, 2497–2460 (2015). · DOI
- Iglesias-Gato, D. et al. The proteome of primary prostate cancer. Eur. Urol. 69, 942–952 (2016). · DOI
- Tyanova, S. et al. Proteomic maps of breast cancer subtypes. Nat Commun. 7, 10259 (2016). · DOI
- Vapnik, V.N. The Nature of Statistical Learning Theory (Springer, 1995). · DOI · Paper page
- Ideker, T. & Krogan, N.J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012). · DOI
- Creixell, P. et al. Pathway and network analysis of cancer genomes. Nat. Methods 12, 615–621 (2015). · DOI
- Angermann, B.R. et al. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat. Methods 9, 283–289 (2012). · DOI
- Cowan, A.E., Moraru, II., Schaff, J.C., Slepchenko, B.M. & Loew, L.M. Spatial modeling of cell signaling networks. Methods Cell Biol. 110, 195–221 (2012). · DOI
- Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014). · DOI · Paper page
- Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016). · DOI · Paper page
- Tyanova, S. et al. Visualization of LC-MS/MS proteomics data in MaxQuant. Proteomics 15, 1453–1456 (2015). · DOI
- Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996). · DOI
- Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001). · DOI · Paper page
- Liew, A.W., Law, N.F. & Yan, H. Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Brief. Bioinform. 12, 498–513 (2011). · DOI
- UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015). · DOI · Paper page
- Hosp, F. et al. A double-barrel liquid chromatography-tandem mass spectrometry (LC-MS/MS) system to quantify 96 interactomes per day. Mol. Cell. Proteomics 14, 2030–2041 (2015). · DOI
- Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012). · DOI
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Self-citationHsieh, Y.-C., Guo, C., Yalamanchili, H. K., Abreha, M., Al-Ouran, R., Li, Y., Dammer, E. B., Lah, J. J., Levey, A. I., Bennett, D. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.104 · Cited by 196
Clearance of senescent cells during cardiac ischemia–reperfusion injury improves recovery
Dookun, E., Walaszczyk, A., Redgrave, R., Palmowski, P., Tual‐Chalot, S., Suwana, A., Chapman, J., Jirkovsky, E., Donastorg Sosa, L., Gill, E., et al. (2020) · Aging Cell · doi:10.1111/acel.13249 · Cited by 173
Kinase-impaired BTK mutations are susceptible to clinical-stage BTK and IKZF1/3 degrader NX-2127
Montoya, S., Bourcier, J., Noviski, M., Lu, H., Thompson, M. C., Chirino, A., Jahn, J., Sondhi, A. K., Gajewski, S., Tan, Y. S. (., et al. (2024) · Science · doi:10.1126/science.adi5798 · Cited by 175
Defining the Adult Neural Stem Cell Niche Proteome Identifies Key Regulators of Adult Neurogenesis
Kjell, J., Fischer-Sternjak, J., Thompson, A. J., Friess, C., Sticco, M. J., Salinas, F., Cox, J., Martinelli, D. C., Ninkovic, J., Franze, K., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.01.002 · Cited by 163
Self-citationThe 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
Zhu, Y., Dou, M., Piehowski, P. D., Liang, Y., Wang, F., Chu, R. K., Chrisler, W. B., Smith, J. N., Schwarz, K. C., Shen, Y., et al. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir118.000686 · Cited by 147
Ooi, G. J., Meikle, P. J., Huynh, K., Earnest, A., Roberts, S. K., Kemp, W., Parker, B. L., Brown, W., Burton, P., & Watt, M. J. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.04.013 · Cited by 137
Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs
Weingarten-Gabbay, S., Klaeger, S., Sarkizova, S., Pearlman, L. R., Chen, D.-Y., Gallagher, K. M. E., Bauer, M. R., Taylor, H. B., Dunn, W. A., Tarr, C., et al. (2021) · Cell · doi:10.1016/j.cell.2021.05.046 · Cited by 147
Pinto, B., Morelli, G., Rastogi, M., Savardi, A., Fumagalli, A., Petretto, A., Bartolucci, M., Varea, E., Catelani, T., Contestabile, A., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.010 · Cited by 135
Sathe, G., Na, C. H., Renuse, S., Madugundu, A. K., Albert, M., Moghekar, A., & Pandey, A. (2019) · PROTEOMICS – Clinical Applications · doi:10.1002/prca.201800105 · Cited by 126
Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology
Haas, R., Zelezniak, A., Iacovacci, J., Kamrad, S., Townsend, S., & Ralser, M. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.08.009 · Cited by 131
Spatial proteomics in three-dimensional intact specimens
Bhatia, H. S., Brunner, A.-D., Öztürk, F., Kapoor, S., Rong, Z., Mai, H., Thielert, M., Ali, M., Al-Maskari, R., Paetzold, J. C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.021 · Cited by 129
Self-citationRiguet, N., Mahul-Mellier, A.-L., Maharjan, N., Burtscher, J., Croisier, M., Knott, G., Hastings, J., Patin, A., Reiterer, V., Farhan, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26684-z · Cited by 118
Amelioration of pathologic α-synuclein-induced Parkinson’s disease by irisin
Kam, T.-I., Park, H., Chou, S.-C., Van Vranken, J. G., Mittenbühler, M. J., Kim, H., A, M., Choi, Y. R., Biswas, D., Wang, J., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204835119 · Cited by 125
Dietrich, L., Rathmer, B., Ewan, K., Bange, T., Heinrichs, S., Dale, T. C., Schade, D., & Grossmann, T. N. (2017) · Cell Chemical Biology · doi:10.1016/j.chembiol.2017.06.013 · Cited by 126
Kruse, T., Gnosa, S. P., Nasa, I., Garvanska, D. H., Hein, J. B., Nguyen, H., Samsøe‐Petersen, J., Lopez‐Mendez, B., Hertz, E. P. T., Schwarz, J., et al. (2020) · The EMBO Journal · doi:10.15252/embj.2019103695 · Cited by 97
Decoding the Transcriptional Response to Ischemic Stroke in Young and Aged Mouse Brain
Androvic, P., Kirdajova, D., Tureckova, J., Zucha, D., Rohlova, E., Abaffy, P., Kriska, J., Valny, M., Anderova, M., Kubista, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107777 · Cited by 118
Inverso, D., Shi, J., Lee, K. H., Jakab, M., Ben-Moshe, S., Kulkarni, S. R., Schneider, M., Wang, G., Komeili, M., Vélez, P. A., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.05.001 · Cited by 114
ELOF1 is a transcription-coupled DNA repair factor that directs RNA polymerase II ubiquitylation
van der Weegen, Y., de Lint, K., van den Heuvel, D., Nakazawa, Y., Mevissen, T. E. T., van Schie, J. J. M., San Martin Alonso, M., Boer, D. E. C., González-Prieto, R., Narayanan, I. V., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00688-9 · Cited by 114
Fazal, R., Boeynaems, S., Swijsen, A., De Decker, M., Fumagalli, L., Moisse, M., Vanneste, J., Guo, W., Boon, R., Vercruysse, T., et al. (2021) · The EMBO Journal · doi:10.15252/embj.2020106177 · Cited by 108
Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis
Blaser, M. C., Kraler, S., Lüscher, T. F., & Aikawa, E. (2021) · Circulation Research · doi:10.1161/circresaha.120.317979 · Cited by 109
Andersen, J. V., Skotte, N. H., Christensen, S. K., Polli, F. S., Shabani, M., Markussen, K. H., Haukedal, H., Westi, E. W., Diaz-delCastillo, M., Sun, R. C., et al. (2021) · Cell Death & Disease · doi:10.1038/s41419-021-04237-y · Cited by 104
Self-citationBeheshti, A., Chakravarty, K., Fogle, H., Fazelinia, H., Silveira, W. A. D., Boyko, V., Polo, S.-H. L., Saravia-Butler, A. M., Hardiman, G., Taylor, D., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55869-2 · Cited by 90
Network Medicine in Pathobiology
Lee, L. Y.-H., & Loscalzo, J. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.03.009 · Cited by 95
Finger sweat analysis enables short interval metabolic biomonitoring in humans
Brunmair, J., Gotsmy, M., Niederstaetter, L., Neuditschko, B., Bileck, A., Slany, A., Feuerstein, M. L., Langbauer, C., Janker, L., Zanghellini, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26245-4 · Cited by 96
Kim, D. K., Park, J., Han, D., Yang, J., Kim, A., Woo, J., Kim, Y., & Mook-Jung, I. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0234-4 · Cited by 95
Omomyc Reveals New Mechanisms To Inhibit the MYC Oncogene
Demma, M. J., Mapelli, C., Sun, A., Bodea, S., Ruprecht, B., Javaid, S., Wiswell, D., Muise, E., Chen, S., Zelina, J., et al. (2019) · Molecular and Cellular Biology · doi:10.1128/mcb.00248-19 · Cited by 92
Structure of the endosomal Commander complex linked to Ritscher-Schinzel syndrome
Healy, M. D., McNally, K. E., Butkovič, R., Chilton, M., Kato, K., Sacharz, J., McConville, C., Moody, E. R. R., Shaw, S., Planelles-Herrero, V. J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.04.003 · Cited by 91
ATP13A3 is a major component of the enigmatic mammalian polyamine transport system
Hamouda, N. N., Van den Haute, C., Vanhoutte, R., Sannerud, R., Azfar, M., Mayer, R., Cortés Calabuig, Á., Swinnen, J. V., Agostinis, P., Baekelandt, V., et al. (2021) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.013908 · Cited by 85
SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells
Kearney, C. J., Vervoort, S. J., Ramsbottom, K. M., Todorovski, I., Lelliott, E. J., Zethoven, M., Pijpers, L., Martin, B. P., Semple, T., Martelotto, L., et al. (2021) · Science Advances · doi:10.1126/sciadv.abe3610 · Cited by 87
Goljanek‐Whysall, K., Soriano‐Arroquia, A., McCormick, R., Chinda, C., & McDonagh, B. (2020) · Aging Cell · doi:10.1111/acel.13140 · Cited by 81
“The Perseus computational platform for comprehensive analysis of (prote)omics data.”
MethodProteome constraints reveal targets for improving microbial fitness in nutrient‐rich environments
Chen, Y., van Pelt‐KleinJan, E., van Olst, B., Douwenga, S., Boeren, S., Bachmann, H., Molenaar, D., Nielsen, J., & Teusink, B. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010093 · Cited by 73
Starost, L., Lindner, M., Herold, M., Xu, Y. K. T., Drexler, H. C. A., Heß, K., Ehrlich, M., Ottoboni, L., Ruffini, F., Stehling, M., et al. (2020) · Acta Neuropathologica · doi:10.1007/s00401-020-02217-8 · Cited by 79
A size-exclusion-based approach for purifying extracellular vesicles from human plasma
Vanderboom, P. M., Dasari, S., Ruegsegger, G. N., Pataky, M. W., Lucien, F., Heppelmann, C. J., Lanza, I. R., & Nair, K. S. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100055 · Cited by 75
Lubeckyj, R. A., Basharat, A. R., Shen, X., Liu, X., & Sun, L. (2019) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-019-02167-w · Cited by 72
A CSB-PAF1C axis restores processive transcription elongation after DNA damage repair
van den Heuvel, D., Spruijt, C. G., González-Prieto, R., Kragten, A., Paulsen, M. T., Zhou, D., Wu, H., Apelt, K., van der Weegen, Y., Yang, K., et al. (2020) · doi:10.1101/2020.01.04.894808 · Cited by 69
Molecular connectivity of mitochondrial gene expression and OXPHOS biogenesis
Singh, A. P., Salvatori, R., Aftab, W., Kohler, A., Carlström, A., Forne, I., Imhof, A., & Ott, M. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.07.024 · Cited by 65
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
SMOC1 is a glucose-responsive hepatokine and therapeutic target for glycemic control
Montgomery, M. K., Bayliss, J., Devereux, C., Bezawork-Geleta, A., Roberts, D., Huang, C., Schittenhelm, R. B., Ryan, A., Townley, S. L., Selth, L. A., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz8048 · Cited by 63
Effects of APOE Genotype on Brain Proteomic Network and Cell Type Changes in Alzheimer's Disease
Dai, J., Johnson, E. C. B., Dammer, E. B., Duong, D. M., Gearing, M., Lah, J. J., Levey, A. I., Wingo, T. S., & Seyfried, N. T. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00454 · Cited by 62
Małecki, J. M., Odonohue, M.-F., Kim, Y., Jakobsson, M. E., Gessa, L., Pinto, R., Wu, J., Davydova, E., Moen, A., Olsen, J. V., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab088 · Cited by 59
Mammalian target of rapamycin complex 2 regulates muscle glucose uptake during exercise in mice
Kleinert, M., Parker, B. L., Fritzen, A. M., Knudsen, J. R., Jensen, T. E., Kjøbsted, R., Sylow, L., Ruegg, M., James, D. E., & Richter, E. A. (2017) · The Journal of Physiology · doi:10.1113/jp274203 · Cited by 59
Dorsch, M., Kowalczyk, M., Planque, M., Heilmann, G., Urban, S., Dujardin, P., Forster, J., Ueffing, K., Nothdurft, S., Oeck, S., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110056 · Cited by 55
Wiederstein, J. L., Nolte, H., Günther, S., Piller, T., Baraldo, M., Kostin, S., Bloch, W., Schindler, N., Sandri, M., Blaauw, B., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.136 · Cited by 52
A dynamic charge-charge interaction modulates PP2A:B56 substrate recruitment
Wang, X., Garvanska, D. H., Nasa, I., Ueki, Y., Zhang, G., Kettenbach, A. N., Peti, W., Nilsson, J., & Page, R. (2020) · eLife · doi:10.7554/elife.55966 · Cited by 53
SALL4 controls cell fate in response to DNA base composition
Pantier, R., Chhatbar, K., Quante, T., Skourti-Stathaki, K., Cholewa-Waclaw, J., Alston, G., Alexander-Howden, B., Lee, H. Y., Cook, A. G., Spruijt, C. G., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.11.046 · Cited by 45
Proteomic architecture of valvular extracellular matrix
Bouchareb, R., Guauque-Olarte, S., Snider, J., Zaminski, D., Anyanwu, A., Stelzer, P., & Lebeche, D. (2021) · JACC: Basic to Translational Science · doi:10.1016/j.jacbts.2020.11.008 · Cited by 52
Insights into replicative senescence of human testicular peritubular cells
Schmid, N., Flenkenthaler, F., Stöckl, J. B., Dietrich, K.-G., Köhn, F. M., Schwarzer, J. U., Kunz, L., Luckner, M., Wanner, G., Arnold, G. J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-51380-w · Cited by 49
HDAC inhibition ameliorates cone survival in retinitis pigmentosa mice
Samardzija, M., Corna, A., Gomez-Sintes, R., Jarboui, M. A., Armento, A., Roger, J. E., Petridou, E., Haq, W., Paquet-Durand, F., Zrenner, E., et al. (2021) · Cell Death & Differentiation · doi:10.1038/s41418-020-00653-3 · Cited by 48
Lysozyme-Induced Transcriptional Regulation of TNF-α Pathway Genes in Cells of the Monocyte Lineage
Bergamo, A., Gerdol, M., Pallavicini, A., Greco, S., Schepens, I., Hamelin, R., Armand, F., Dyson, P. J., & Sava, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20215502 · Cited by 42
Dittmar, G., Hernandez, D. P., Kowenz-Leutz, E., Kirchner, M., Kahlert, G., Wesolowski, R., Baum, K., Knoblich, M., Hofstätter, M., Muller, A., et al. (2019) · iScience · doi:10.1016/j.isci.2019.02.026 · Cited by 41
Raghavan, K. S., Francescone, R., Franco-Barraza, J., Gardiner, J. C., Vendramini-Costa, D. B., Luong, T., Pourmandi, N., Andren, A., Kurimchak, A., Ogier, C., et al. (2022) · Cancer Research Communications · doi:10.1158/2767-9764.crc-21-0147 · Cited by 40
Eitan, C., Siany, A., Barkan, E., Olender, T., van Eijk, K. R., Moisse, M., Farhan, S. M. K., Danino, Y. M., Yanowski, E., Marmor-Kollet, H., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01040-6 · Cited by 38
Molecular wiring of a mitochondrial translational feedback loop
Salvatori, R., Kehrein, K., Singh, A. P., Aftab, W., Möller-Hergt, B. V., Forne, I., Imhof, A., & Ott, M. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.11.019 · Cited by 34
Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics
The, M., & Käll, L. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001018 · Cited by 37
Feiten, A. F., Dahm, K., Schlepckow, K., van Lengerich, B., Suh, J. H., Reifschneider, A., Wefers, B., Bartos, L. M., Wind-Mark, K., de Weerd, L., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68706-8 · Cited by 39
Application of an iPSC‐Derived Organoid Model for Localized Scleroderma Therapy
Ma, J., Li, W., Cao, R., Gao, D., Zhang, Q., Li, X., Li, B., Lv, L., Li, M., Jiang, J., et al. (2022) · Advanced Science · doi:10.1002/advs.202106075 · Cited by 38
Proteolysis in Irish farmhouse Camembert cheese during ripening
Mane, A., & McSweeney, P. L. H. (2020) · Journal of Food Biochemistry · doi:10.1111/jfbc.13101 · Cited by 35
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
Sala, C., Odermatt, N. T., Soler-Arnedo, P., Gülen, M. F., von Schultz, S., Benjak, A., & Cole, S. T. (2018) · PLOS Pathogens · doi:10.1371/journal.ppat.1007491 · Cited by 35
A cyclin-dependent kinase-mediated phosphorylation switch of disordered protein condensation
Valverde, J. M., Dubra, G., Phillips, M., Haider, A., Elena-Real, C., Fournet, A., Alghoul, E., Chahar, D., Andrés-Sanchez, N., Paloni, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42049-0 · Cited by 33
Transforming Chemical Proteomics Enrichment into a High-Throughput Method Using an SP2E Workflow
Becker, T., Wiest, A., Telek, A., Bejko, D., Hoffmann-Röder, A., & Kielkowski, P. (2022) · JACS Au · doi:10.1021/jacsau.2c00284 · Cited by 33
Van houcke, J., Mariën, V., Zandecki, C., Ayana, R., Pepermans, E., Boonen, K., Seuntjens, E., Baggerman, G., & Arckens, L. (2023) · npj Regenerative Medicine · doi:10.1038/s41536-023-00304-4 · Cited by 29
Mechanism of orphan subunit recognition during assembly quality control
Yagita, Y., Zavodszky, E., Peak-Chew, S.-Y., & Hegde, R. S. (2023) · Cell · doi:10.1016/j.cell.2023.06.016 · Cited by 30
Transcriptional and translational landscape of Candida auris in response to caspofungin
Zamith-Miranda, D., Amatuzzi, R. F., Munhoz da Rocha, I. F., Martins, S. T., Lucena, A. C. R., Vieira, A. Z., Trentin, G., Almeida, F., Rodrigues, M. L., Nakayasu, E. S., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.007 · Cited by 29
Lymphoid gene expression supports neuroprotective microglia function
Ayata, P., Crowley, J. M., Challman, M. F., Sahasrabuddhe, V., Gratuze, M., Werneburg, S., Ribeiro, D., Hays, E. C., Durán-Laforet, V., Faust, T. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09662-z · Cited by 32
Experimental evidence for temporal uncoupling of brain Aβ deposition and neurodegenerative sequelae
Rother, C., Uhlmann, R. E., Müller, S. A., Schelle, J., Skodras, A., Obermüller, U., Häsler, L. M., Lambert, M., Baumann, F., Xu, Y., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34538-5 · Cited by 28
Cancer proteome and metabolite changes linked to SHMT2
Tong, J., Krieger, J. R., Taylor, P., Bagshaw, R., Kang, J., Jeedigunta, S., Wybenga-Groot, L. E., Zhang, W., Badr, H., Mirhadi, S., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237981 · Cited by 24
Metadata
- Authors
- Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J.
- Venue
- Nature Methods
- Published
- 2016-06-27
- Volume
- 13
- Issue
- 9
- Pages
- 731-740
- ISSN
- 1548-7091
- 1548-7105
- Cited by
- 8,803
- References
- 20
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
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