Skyline: an open source document editor for creating and analyzing targeted proteomics experiments

ProteoWizard: open source software for rapid proteomics tools development

Kessner, D., Chambers, M., Burke, R., Agus, D., & Mallick, P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn323 · Cited by 2,024 · see citing works

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma

Addona, T. A., Abbatiello, S. E., Schilling, B., Skates, S. J., Mani, D. R., Bunk, D. M., Spiegelman, C. H., Zimmerman, L. J., Ham, A.-J. L., Keshishian, H., et al. (2009) · Nature Biotechnology · doi:10.1038/nbt.1546 · Cited by 1,037 · see citing works

The PeptideAtlas project

Desiere, F. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj040 · Cited by 910 · see citing works

Purely Functional Data Structures

Okasaki, C. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511530104 · Cited by 635 · see citing works

An Improved Model for Prediction of Retention Times of Tryptic Peptides in Ion Pair Reversed-phase HPLC

Krokhin, O. V., Craig, R., Spicer, V., Ens, W., Standing, K. G., Beavis, R. C., & Wilkins, J. A. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400031-mcp200 · Cited by 284 · see citing works

Analysis of Peptide MS/MS Spectra from Large-Scale Proteomics Experiments Using Spectrum Libraries

Frewen, B. E., Merrihew, G. E., Wu, C. C., Noble, W. S., & MacCoss, M. J. (2006) · Analytical Chemistry · doi:10.1021/ac060279n · Cited by 249 · see citing works

Expediting the Development of Targeted SRM Assays: Using Data from Shotgun Proteomics to Automate Method Development

Prakash, A., Tomazela, D. M., Frewen, B., MacLean, B., Merrihew, G., Peterman, S., & MacCoss, M. J. (2009) · Journal of Proteome Research · doi:10.1021/pr801028b · Cited by 147 · see citing works

MRMer, an Interactive Open Source and Cross-platform System for Data Extraction and Visualization of Multiple Reaction Monitoring Experiments

Martin, D. B., Holzman, T., May, D., Peterson, A. C., Eastham, A., Eng, J. K., & McIntosh, M. W. (2008) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m700504-mcp200 · Cited by 86 · see citing works

Label-Free Comparative Analysis of Proteomics Mixtures Using Chromatographic Alignment of High-Resolution μLC−MS Data

Finney, G. L., Blackler, A. R., Hoopmann, M. R., Canterbury, J. D., Wu, C. C., & MacCoss, M. J. (2008) · Analytical Chemistry · doi:10.1021/ac701649e · Cited by 61 · see citing works

Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis

Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877 · see citing works

Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens

Gubin, M. M., Zhang, X., Schuster, H., Caron, E., Ward, J. P., Noguchi, T., Ivanova, Y., Hundal, J., Arthur, C. D., Krebber, W.-J., et al. (2014) · Nature · doi:10.1038/nature13988 · Cited by 1,974 · see citing works

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Shen, B., Yi, X., Sun, Y., Bi, X., Du, J., Zhang, C., Quan, S., Zhang, F., Sun, R., Qian, L., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.032 · Cited by 1,514 · see citing works

Proteogenomic characterization of human colon and rectal cancer

CPTAC, T. N., Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., Chambers, M. C., Zimmerman, L. J., Shaddox, K. F., et al. (2014) · Nature · doi:10.1038/nature13438 · Cited by 1,462 · see citing works

Protein Analysis by Shotgun/Bottom-up Proteomics

Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491 · see citing works

Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues

Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinović, S. M., Cheng, L.-Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044305 · Cited by 1,238 · see citing works

Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions

Picotti, P., & Aebersold, R. (2012) · Nature Methods · doi:10.1038/nmeth.2015 · Cited by 1,302 · see citing works

Targeting KRAS mutant cancers with a covalent G12C-specific inhibitor

Janes, M. R., Zhang, J., Li, L.-S., Hansen, R., Peters, U., Guo, X., Chen, Y., Babbar, A., Firdaus, S. J., Darjania, L., et al. (2018) · Cell · doi:10.1016/j.cell.2018.01.006 · Cited by 1,172 · see citing works

Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

Sharon, G., Cruz, N. J., Kang, D.-W., Gandal, M. J., Wang, B., Kim, Y.-M., Zink, E. M., Casey, C. P., Taylor, B. C., Lane, C. J., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.004 · Cited by 1,182 · see citing works

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight

Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., McKenna, M. J., Meydan, C., Mishra, T., Nasrini, J., et al. (2019) · Science · doi:10.1126/science.aau8650 · Cited by 1,128 · see citing works

MS-GF+ makes progress towards a universal database search tool for proteomics

Kim, S., & Pevzner, P. A. (2014) · Nature Communications · doi:10.1038/ncomms6277 · Cited by 1,098 · see citing works

Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001 · see citing works

The target landscape of clinical kinase drugs

Klaeger, S., Heinzlmeir, S., Wilhelm, M., Polzer, H., Vick, B., Koenig, P.-A., Reinecke, M., Ruprecht, B., Petzoldt, S., Meng, C., et al. (2017) · Science · doi:10.1126/science.aan4368 · Cited by 854 · see citing works

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities

Vasaikar, S., Huang, C., Wang, X., Petyuk, V. A., Savage, S. R., Wen, B., Dou, Y., Zhang, Y., Shi, Z., Arshad, O. A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.030 · Cited by 799 · see citing works

Tau PTM Profiles Identify Patient Heterogeneity and Stages of Alzheimer’s Disease

Wesseling, H., Mair, W., Kumar, M., Schlaffner, C. N., Tang, S., Beerepoot, P., Fatou, B., Guise, A. J., Cheng, L., Takeda, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.029 · Cited by 688 · see citing works

Potential roles of gut microbiome and metabolites in modulating ALS in mice

Blacher, E., Bashiardes, S., Shapiro, H., Rothschild, D., Mor, U., Dori-Bachash, M., Kleimeyer, C., Moresi, C., Harnik, Y., Zur, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1443-5 · Cited by 700 · see citing works

Irisin Mediates Effects on Bone and Fat via αV Integrin Receptors

Kim, H., Wrann, C. D., Jedrychowski, M., Vidoni, S., Kitase, Y., Nagano, K., Zhou, C., Chou, J., Parkman, V.-J. A., Novick, S. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.025 · Cited by 638 · see citing works

Diet-Microbiota Interactions Mediate Global Epigenetic Programming in Multiple Host Tissues

Krautkramer, K. A., Kreznar, J. H., Romano, K. A., Vivas, E. I., Barrett-Wilt, G. A., Rabaglia, M. E., Keller, M. P., Attie, A. D., Rey, F. E., & Denu, J. M. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.10.025 · Cited by 607 · see citing works

Unexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion

Walls, A. C., Xiong, X., Park, Y.-J., Tortorici, M. A., Snijder, J., Quispe, J., Cameroni, E., Gopal, R., Dai, M., Lanzavecchia, A., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.028 · Cited by 582 · see citing works

Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach

Carr, S. A., Abbatiello, S. E., Ackermann, B. L., Borchers, C., Domon, B., Deutsch, E. W., Grant, R. P., Hoofnagle, A. N., Hüttenhain, R., Koomen, J. M., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.036095 · Cited by 561 · see citing works

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 · see citing works

A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease

Seyfried, N. T., Dammer, E. B., Swarup, V., Nandakumar, D., Duong, D. M., Yin, L., Deng, Q., Nguyen, T., Hales, C. M., Wingo, T., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.11.006 · Cited by 477 · see citing works

Gene‐specific correlation of RNA and protein levels in human cells and tissues

Edfors, F., Danielsson, F., Hallström, B. M., Käll, L., Lundberg, E., Pontén, F., Forsström, B., & Uhlén, M. (2016) · Molecular Systems Biology · doi:10.15252/msb.20167144 · Cited by 493 · see citing works

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome

Mars, R. A. T., Yang, Y., Ward, T., Houtti, M., Priya, S., Lekatz, H. R., Tang, X., Sun, Z., Kalari, K. R., Korem, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.007 · Cited by 456 · see citing works

A repository of assays to quantify 10,000 human proteins by SWATH-MS

Rosenberger, G., Koh, C. C., Guo, T., Röst, H. L., Kouvonen, P., Collins, B. C., Heusel, M., Liu, Y., Caron, E., Vichalkovski, A., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.31 · Cited by 413 · see citing works

Global Phosphoproteomic Analysis of Human Skeletal Muscle Reveals a Network of Exercise-Regulated Kinases and AMPK Substrates

Hoffman, N. J., Parker, B. L., Chaudhuri, R., Fisher-Wellman, K. H., Kleinert, M., Humphrey, S. J., Yang, P., Holliday, M., Trefely, S., Fazakerley, D. J., et al. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.09.001 · Cited by 426 · see citing works

A wellness study of 108 individuals using personal, dense, dynamic data clouds

Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3870 · Cited by 427 · see citing works

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Keenan, A. B., Jenkins, S. L., Jagodnik, K. M., Koplev, S., He, E., Torre, D., Wang, Z., Dohlman, A. B., Silverstein, M. C., Lachmann, A., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.001 · Cited by 423 · see citing works

Parallel Reaction Monitoring: A Targeted Experiment Performed Using High Resolution and High Mass Accuracy Mass Spectrometry

Rauniyar, N. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161226120 · Cited by 406 · see citing works

A multicenter study benchmarks software tools for label-free proteome quantification

Navarro, P., Kuharev, J., Gillet, L. C., Bernhardt, O. M., MacLean, B., Röst, H. L., Tate, S. A., Tsou, C.-C., Reiter, L., Distler, U., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3685 · Cited by 346 · see citing works

Trans‐Proteomic Pipeline, a standardized data processing pipeline for large‐scale reproducible proteomics informatics

Deutsch, E. W., Mendoza, L., Shteynberg, D., Slagel, J., Sun, Z., & Moritz, R. L. (2015) · PROTEOMICS – Clinical Applications · doi:10.1002/prca.201400164 · Cited by 342 · see citing works

Gut microbiome-related effects of berberine and probiotics on type 2 diabetes (the PREMOTE study)

Zhang, Y., Gu, Y., Ren, H., Wang, S., Zhong, H., Zhao, X., Ma, J., Gu, X., Xue, Y., Huang, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18414-8 · Cited by 380 · see citing works

Quantitative variability of 342 plasma proteins in a human twin population

Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L., Vitek, O., Mouritsen, J., Lachance, G., Spector, T. D., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145728 · Cited by 328 · see citing works

Transcriptional signature in microglia associated with Aβ plaque phagocytosis

Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357 · see citing works

Endogenous Parkin Preserves Dopaminergic Substantia Nigral Neurons following Mitochondrial DNA Mutagenic Stress

Pickrell, A. M., Huang, C.-H., Kennedy, S. R., Ordureau, A., Sideris, D. P., Hoekstra, J. G., Harper, J. W., & Youle, R. J. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.034 · Cited by 327 · see citing works

Mass Spectrometry Applied to Bottom-Up Proteomics: Entering the High-Throughput Era for Hypothesis Testing

Gillet, L. C., Leitner, A., & Aebersold, R. (2016) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-071015-041535 · Cited by 323 · see citing works

Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

Collins, B. C., Gillet, L. C., Rosenberger, G., Röst, H. L., Vichalkovski, A., Gstaiger, M., & Aebersold, R. (2013) · Nature Methods · doi:10.1038/nmeth.2703 · Cited by 314 · see citing works

Repression of Stress-Induced LINE-1 Expression Protects Cancer Cell Subpopulations from Lethal Drug Exposure

Guler, G. D., Tindell, C. A., Pitti, R., Wilson, C., Nichols, K., KaiWai Cheung, T., Kim, H.-J., Wongchenko, M., Yan, Y., Haley, B., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.07.002 · Cited by 277 · see citing works

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280 · see citing works

Metabolomic identification of subtypes of nonalcoholic steatohepatitis

Alonso, C., Fernández-Ramos, D., Varela-Rey, M., Martínez-Arranz, I., Navasa, N., Van Liempd, S. M., Lavín Trueba, J. L., Mayo, R., Ilisso, C. P., de Juan, V. G., et al. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.01.015 · Cited by 263 · see citing works

CPTAC Assay Portal: a repository of targeted proteomic assays

the Clinical Proteomic Tumor Analysis Consortium (CPTAC), Whiteaker, J. R., Halusa, G. N., Hoofnagle, A. N., Sharma, V., MacLean, B., Yan, P., Wrobel, J. A., Kennedy, J., Mani, D. R., et al. (2014) · Nature Methods · doi:10.1038/nmeth.3002 · Cited by 223 · see citing works

Cand1 Promotes Assembly of New SCF Complexes through Dynamic Exchange of F Box Proteins

Pierce, N. W., Lee, J. E., Liu, X., Sweredoski, M. J., Graham, R. L. J., Larimore, E. A., Rome, M., Zheng, N., Clurman, B. E., Hess, S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.02.024 · Cited by 262 · see citing works

Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma

Abbatiello, S. E., Schilling, B., Mani, D. R., Zimmerman, L. J., Hall, S. C., MacLean, B., Albertolle, M., Allen, S., Burgess, M., Cusack, M. P., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.047050 · Cited by 215 · see citing works

Exosomes from patients with major depression cause depressive-like behaviors in mice with involvement of miR-139-5p-regulated neurogenesis.

Wei, Z.-X., Xie, G.-J., Mao, X., Zou, X.-P., Liao, Y.-J., Liu, Q.-S., Wang, H., & Cheng, Y. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0622-2 · Cited by 227 · see citing works

Alzheimer’s-associated PLCγ2 is a signaling node required for both TREM2 function and the inflammatory response in human microglia

Andreone, B. J., Przybyla, L., Llapashtica, C., Rana, A., Davis, S. S., van Lengerich, B., Lin, K., Shi, J., Mei, Y., Astarita, G., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0650-6 · Cited by 233 · see citing works

A molecular switch from STAT2-IRF9 to ISGF3 underlies interferon-induced gene transcription

Platanitis, E., Demiroz, D., Schneller, A., Fischer, K., Capelle, C., Hartl, M., Gossenreiter, T., Müller, M., Novatchkova, M., & Decker, T. (2019) · Nature Communications · doi:10.1038/s41467-019-10970-y · Cited by 233 · see citing works

Assembling the Community-Scale Discoverable Human Proteome

Wang, M., Wang, J., Carver, J., Pullman, B. S., Cha, S. W., & Bandeira, N. (2018) · Cell Systems · doi:10.1016/j.cels.2018.08.004 · Cited by 201 · see citing works

Cancer proteogenomics: current impact and future prospects

Mani, D. R., Krug, K., Zhang, B., Satpathy, S., Clauser, K. R., Ding, L., Ellis, M., Gillette, M. A., & Carr, S. A. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00446-5 · Cited by 229 · see citing works

Functioning of a metabolic flux sensor in <i>Escherichia coli</i>

Kochanowski, K., Volkmer, B., Gerosa, L., Haverkorn van Rijsewijk, B. R., Schmidt, A., & Heinemann, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202582110 · Cited by 196 · see citing works

Quantitative measurements of <i><scp>N</scp></i>‐linked glycoproteins in human plasma by <scp>SWATH</scp>‐<scp>MS</scp>

Liu, Y., Hüttenhain, R., Surinova, S., Gillet, L. C., Mouritsen, J., Brunner, R., Navarro, P., & Aebersold, R. (2013) · PROTEOMICS · doi:10.1002/pmic.201200417 · Cited by 204 · see citing works

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

Rosenberger, G., Bludau, I., Schmitt, U., Heusel, M., Hunter, C. L., Liu, Y., MacCoss, M. J., MacLean, B. X., Nesvizhskii, A. I., Pedrioli, P. G. A., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4398 · Cited by 203 · see citing works

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data

Ting, Y. S., Egertson, J. D., Payne, S. H., Kim, S., MacLean, B., Käll, L., Aebersold, R., Smith, R. D., Noble, W. S., & MacCoss, M. J. (2015) · Molecular &amp; Cellular Proteomics · doi:10.1074/mcp.o114.047035 · Cited by 175 · see citing works

Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins

Kennedy, J. J., Abbatiello, S. E., Kim, K., Yan, P., Whiteaker, J. R., Lin, C., Kim, J. S., Zhang, Y., Wang, X., Ivey, R. G., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2763 · Cited by 184 · see citing works

Quantitative Measurement of Intact Alpha-Synuclein Proteoforms from Post-Mortem Control and Parkinson's Disease Brain Tissue by Intact Protein Mass Spectrometry

Kellie, J. F., Higgs, R. E., Ryder, J. W., Major, A., Beach, T. G., Adler, C. H., Merchant, K., & Knierman, M. D. (2014) · Scientific Reports · doi:10.1038/srep05797 · Cited by 182 · see citing works

Chemical and pathogen-induced inflammation disrupt the murine intestinal microbiome

Borton, M. A., Sabag-Daigle, A., Wu, J., Solden, L. M., O’Banion, B. S., Daly, R. A., Wolfe, R. A., Gonzalez, J. F., Wysocki, V. H., Ahmer, B. M. M., et al. (2017) · Microbiome · doi:10.1186/s40168-017-0264-8 · Cited by 149 · see citing works

Amplification of human interneuron progenitors promotes brain tumors and neurological defects

Eichmüller, O. L., Corsini, N. S., Vértesy, Á., Morassut, I., Scholl, T., Gruber, V.-E., Peer, A. M., Chu, J., Novatchkova, M., Hainfellner, J. A., et al. (2022) · Science · doi:10.1126/science.abf5546 · Cited by 163 · see citing works

Hybrid mass spectrometry approaches in glycoprotein analysis and their usage in scoring biosimilarity

Yang, Y., Liu, F., Franc, V., Halim, L. A., Schellekens, H., & Heck, A. J. R. (2016) · Nature Communications · doi:10.1038/ncomms13397 · Cited by 152 · see citing works

The histone methyltransferase MMSET/WHSC1 activates TWIST1 to promote an epithelial-mesenchymal transition and invasive properties of prostate cancer

Ezponda, T., Popovic, R., Shah, M. Y., Martinez-Garcia, E., Zheng, Y., Min, D.-J., Will, C., Neri, A., Kelleher, N. L., Yu, J., et al. (2013) · Oncogene · doi:10.1038/onc.2012.297 · Cited by 158 · see citing works

Neuronal ribosomes exhibit dynamic and context-dependent exchange of ribosomal proteins

Fusco, C. M., Desch, K., Dörrbaum, A. R., Wang, M., Staab, A., Chan, I. C. W., Vail, E., Villeri, V., Langer, J. D., & Schuman, E. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26365-x · Cited by 157 · see citing works

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Meyer, J. G., & Schilling, B. (2017) · Expert Review of Proteomics · doi:10.1080/14789450.2017.1322904 · Cited by 151 · see citing works

Single-Cell Proteomics Reveal that Quantitative Changes in Co-expressed Lineage-Specific Transcription Factors Determine Cell Fate

Palii, C. G., Cheng, Q., Gillespie, M. A., Shannon, P., Mazurczyk, M., Napolitani, G., Price, N. D., Ranish, J. A., Morrissey, E., Higgs, D. R., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.02.006 · Cited by 148 · see citing works

Glycodelete engineering of mammalian cells simplifies N-glycosylation of recombinant proteins

Meuris, L., Santens, F., Elson, G., Festjens, N., Boone, M., Dos Santos, A., Devos, S., Rousseau, F., Plets, E., Houthuys, E., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2885 · Cited by 141 · see citing works

Rescuing Over-activated Microglia Restores Cognitive Performance in Juvenile Animals of the Dp(16) Mouse Model of down Syndrome.

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 · see citing works

Quantitative proteomic profiling of cerebrospinal fluid to identify candidate biomarkers for Alzheimer’s disease

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 · see citing works

Metabolic programs of T cell tissue residency empower tumour immunity

Reina-Campos, M., Heeg, M., Kennewick, K., Mathews, I. T., Galletti, G., Luna, V., Nguyen, Q., Huang, H., Milner, J. J., Hu, K. H., et al. (2023) · Nature · doi:10.1038/s41586-023-06483-w · Cited by 132 · see citing works

Liver-fibrosis-activated transcriptional networks govern hepatocyte reprogramming and intra-hepatic communication

Loft, A., Alfaro, A. J., Schmidt, S. F., Pedersen, F. B., Terkelsen, M. K., Puglia, M., Chow, K. K., Feuchtinger, A., Troullinaki, M., Maida, A., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.06.005 · Cited by 129 · see citing works

Targeted brain proteomics uncover multiple pathways to Alzheimer's dementia

Yu, L., Petyuk, V. A., Gaiteri, C., Mostafavi, S., Young‐Pearse, T., Shah, R. C., Buchman, A. S., Schneider, J. A., Piehowski, P. D., Sontag, R. L., et al. (2018) · Annals of Neurology · doi:10.1002/ana.25266 · Cited by 124 · see citing works

Proteomic approaches to uncovering virus–host protein interactions during the progression of viral infection

Lum, K. K., & Cristea, I. M. (2016) · Expert Review of Proteomics · doi:10.1586/14789450.2016.1147353 · Cited by 113 · see citing works

Rapid empirical discovery of optimal peptides for targeted proteomics

Stergachis, A. B., MacLean, B., Lee, K., Stamatoyannopoulos, J. A., & MacCoss, M. J. (2011) · Nature Methods · doi:10.1038/nmeth.1770 · Cited by 110 · see citing works

Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis

Gillespie, M. A., Palii, C. G., Sanchez-Taltavull, D., Shannon, P., Longabaugh, W. J. R., Downes, D. J., Sivaraman, K., Espinoza, H. M., Hughes, J. R., Price, N. D., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.03.031 · Cited by 110 · see citing works

Multiplexed relative and absolute quantitative immunopeptidomics reveals MHC I repertoire alterations induced by CDK4/6 inhibition

Stopfer, L. E., Mesfin, J. M., Joughin, B. A., Lauffenburger, D. A., & White, F. M. (2020) · Nature Communications · doi:10.1038/s41467-020-16588-9 · Cited by 109 · see citing works

Identification of trypsin-degrading commensals in the large intestine

Li, Y., Watanabe, E., Kawashima, Y., Plichta, D. R., Wang, Z., Ujike, M., Ang, Q. Y., Wu, R., Furuichi, M., Takeshita, K., et al. (2022) · Nature · doi:10.1038/s41586-022-05181-3 · Cited by 106 · see citing works

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer

Donnella, H. J., Webber, J. T., Levin, R. S., Camarda, R., Momcilovic, O., Bayani, N., Shah, K. N., Korkola, J. E., Shokat, K. M., Goga, A., et al. (2018) · Nature Chemical Biology · doi:10.1038/s41589-018-0081-9 · Cited by 90 · see citing works

Targeted mass spectrometry to quantify brain-derived cerebrospinal fluid biomarkers in Alzheimer’s disease

Zhou, M., Haque, R. U., Dammer, E. B., Duong, D. M., Ping, L., Johnson, E. C. B., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2020) · Clinical Proteomics · doi:10.1186/s12014-020-09285-8 · Cited by 92 · see citing works

Molecular basis for substrate recruitment to the PRMT5 methylosome

Mulvaney, K. M., Blomquist, C., Acharya, N., Li, R., Ranaghan, M. J., O’Keefe, M., Rodriguez, D. J., Young, M. J., Kesar, D., Pal, D., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.07.019 · Cited by 93 · see citing works

Abnormal Eating Patterns Cause Circadian Disruption and Promote Alcohol-Associated Colon Carcinogenesis

Bishehsari, F., Engen, P. A., Voigt, R. M., Swanson, G., Shaikh, M., Wilber, S., Naqib, A., Green, S. J., Shetuni, B., Forsyth, C. B., et al. (2020) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2019.10.011 · Cited by 76 · see citing works

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

Bludau, I., Frank, M., Dörig, C., Cai, Y., Heusel, M., Rosenberger, G., Picotti, P., Collins, B. C., Röst, H., & Aebersold, R. (2021) · Nature Communications · doi:10.1038/s41467-021-24030-x · Cited by 88 · see citing works

CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes

Gupta, M., Azumaya, C. M., Moritz, M., Pourmal, S., Diallo, A., Merz, G. E., Jang, G., Bouhaddou, M., Fossati, A., Brilot, A. F., et al. (2021) · doi:10.1101/2021.05.10.443524 · Cited by 89 · see citing works

Quantification of lysine acetylation and succinylation stoichiometry in proteins using mass spectrometric data-independent acquisitions (SWATH)

Meyer, J. G., D’Souza, A. K., Sorensen, D. J., Rardin, M. J., Wolfe, A. J., Gibson, B. W., & Schilling, B. (2016) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-016-1476-z · Cited by 79 · see citing works

BIN1 protein isoforms are differentially expressed in astrocytes, neurons, and microglia: neuronal and astrocyte BIN1 are implicated in tau pathology

Taga, M., Petyuk, V. A., White, C., Marsh, G., Ma, Y., Klein, H.-U., Connor, S. M., Kroshilina, A., Yung, C. J., Khairallah, A., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00387-3 · Cited by 81 · see citing works

PIKES Analysis Reveals Response to Degraders and Key Regulatory Mechanisms of the CRL4 Network

Reichermeier, K. M., Straube, R., Reitsma, J. M., Sweredoski, M. J., Rose, C. M., Moradian, A., den Besten, W., Hinkle, T., Verschueren, E., Petzold, G., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.12.013 · Cited by 78 · see citing works

A conserved fertilization complex bridges sperm and egg in vertebrates

Deneke, V. E., Blaha, A., Lu, Y., Suwita, J. P., Draper, J. M., Phan, C. S., Panser, K., Schleiffer, A., Jacob, L., Humer, T., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.035 · Cited by 78 · see citing works

Trade-Off between Growth and Carbohydrate Accumulation in Nutrient-Limited Arthrospira sp. PCC 8005 Studied by Integrating Transcriptomic and Proteomic Approaches

Depraetere, O., Deschoenmaeker, F., Badri, H., Monsieurs, P., Foubert, I., Leys, N., Wattiez, R., & Muylaert, K. (2015) · PLOS ONE · doi:10.1371/journal.pone.0132461 · Cited by 68 · see citing works

Novel <scp>CSF</scp> biomarkers in genetic frontotemporal dementia identified by proteomics

van der Ende, E. L., Meeter, L. H., Stingl, C., van Rooij, J. G. J., Stoop, M. P., Nijholt, D. A. T., Sanchez‐Valle, R., Graff, C., Öijerstedt, L., Grossman, M., et al. (2019) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.745 · Cited by 65 · see citing works

DOT1L modulates the senescence-associated secretory phenotype through epigenetic regulation of IL1A

Leon, K. E., Buj, R., Lesko, E., Dahl, E. S., Chen, C.-W., Tangudu, N. K., Imamura-Kawasawa, Y., Kossenkov, A. V., Hobbs, R. P., & Aird, K. M. (2021) · Journal of Cell Biology · doi:10.1083/jcb.202008101 · Cited by 70 · see citing works

A systems mechanism for KRAS mutant allele–specific responses to targeted therapy

McFall, T., Diedrich, J. K., Mengistu, M., Littlechild, S. L., Paskvan, K. V., Sisk-Hackworth, L., Moresco, J. J., Shaw, A. S., & Stites, E. C. (2019) · Science Signaling · doi:10.1126/scisignal.aaw8288 · Cited by 65 · see citing works

Recapitulation of Endogenous 4R Tau Expression and Formation of Insoluble Tau in Directly Reprogrammed Human Neurons

Capano, L. S., Sato, C., Ficulle, E., Yu, A., Horie, K., Kwon, J.-S., Burbach, K. F., Barthélemy, N. R., Fox, S. G., Karch, C. M., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.04.018 · Cited by 65 · see citing works

HDAC11 deficiency disrupts oncogene-induced hematopoiesis in myeloproliferative neoplasms

Yue, L., Sharma, V., Horvat, N. P., Akuffo, A. A., Beatty, M. S., Murdun, C., Colin, C., Billington, J. M. R., Goodheart, W. E., Sahakian, E., et al. (2020) · Blood · doi:10.1182/blood.2019895326 · Cited by 67 · see citing works

Molecular investigation of the radiation resistance of edible cyanobacterium <i>Arthrospira</i> sp. <scp>PCC</scp> 8005

Badri, H., Monsieurs, P., Coninx, I., Wattiez, R., & Leys, N. (2015) · MicrobiologyOpen · doi:10.1002/mbo3.229 · Cited by 56 · see citing works

Engineered hypermutation adapts cyanobacterial photosynthesis to combined high light and high temperature stress

Sun, H., Luan, G., Ma, Y., Lou, W., Chen, R., Feng, D., Zhang, S., Sun, J., & Lu, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36964-5 · Cited by 56 · see citing works

Capillary Electrophoresis–Nanoelectrospray Ionization–Selected Reaction Monitoring Mass Spectrometry via a True Sheathless Metal-Coated Emitter Interface for Robust and High-Sensitivity Sample Quantification

Guo, X., Fillmore, T. L., Gao, Y., & Tang, K. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.5b04912 · Cited by 54 · see citing works

Autophagy accounts for approximately one-third of mitochondrial protein turnover and is protein selective

Vincow, E. S., Thomas, R. E., Merrihew, G. E., Shulman, N. J., Bammler, T. K., MacDonald, J. W., MacCoss, M. J., & Pallanck, L. J. (2019) · Autophagy · doi:10.1080/15548627.2019.1586258 · Cited by 54 · see citing works

Concentration Determination of &gt;200 Proteins in Dried Blood Spots for Biomarker Discovery and Validation

Eshghi, A., Pistawka, A. J., Liu, J., Chen, M., Sinclair, N. J. T., Hardie, D. B., Elliott, M., Chen, L., Newman, R., Mohammed, Y., et al. (2020) · Molecular &amp; Cellular Proteomics · doi:10.1074/mcp.tir119.001820 · Cited by 51 · see citing works

Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain

Wingo, T. S., Duong, D. M., Zhou, M., Dammer, E. B., Wu, H., Cutler, D. J., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00324 · Cited by 51 · see citing works

ApoC-III is a novel inducer of calcification in human aortic valves

Schlotter, F., de Freitas, R. C. C., Rogers, M. A., Blaser, M. C., Wu, P.-J., Higashi, H., Halu, A., Iqbal, F., Andraski, A. B., Rodia, C. N., et al. (2021) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.015700 · Cited by 49 · see citing works

Nuclear Phosphoproteomic Screen Uncovers ACLY as Mediator of IL-2-induced Proliferation of CD4+ T lymphocytes

Osinalde, N., Mitxelena, J., Sánchez-Quiles, V., Akimov, V., Aloria, K., Arizmendi, J. M., Zubiaga, A. M., Blagoev, B., & Kratchmarova, I. (2016) · Molecular &amp; Cellular Proteomics · doi:10.1074/mcp.m115.057158 · Cited by 48 · see citing works

Hsp70 Binds to the Androgen Receptor N-terminal Domain and Modulates the Receptor Function in Prostate Cancer Cells

Dong, J., Wu, Z., Wang, D., Pascal, L. E., Nelson, J. B., Wipf, P., & Wang, Z. (2019) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-18-0432 · Cited by 49 · see citing works

Oncogene‐induced cellular senescence elicits an anti‐ <scp>W</scp> arburg effect

Li, M., Durbin, K. R., Sweet, S. M. M., Tipton, J. D., Zheng, Y., & Kelleher, N. L. (2013) · PROTEOMICS · doi:10.1002/pmic.201200298 · Cited by 43 · see citing works

cindr, the Drosophila Homolog of the CD2AP Alzheimer’s Disease Risk Gene, Is Required for Synaptic Transmission and Proteostasis

Ojelade, S. A., Lee, T. V., Giagtzoglou, N., Yu, L., Ugur, B., Li, Y., Duraine, L., Zuo, Z., Petyuk, V., De Jager, P. L., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.041 · Cited by 42 · see citing works

A novel Tolerant Skyline Operator for decision support

Chai, J., Liu, J. N. K., Gao, D., et al. (2012) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2012.6468562 · Cited by 1 · see citing works

A novel tolerant skyline operation for decision making

Chai, J., Liu, J. N. K., & Li, A. (2013) · Journal of Decision System · doi:10.1080/12460125.2013.797325 · Cited by 4 · see citing works

An Efficient Method for Parallel Processing of Skyline Queries

Huang, Z., XIANG, Y., Yong-sheng, X., et al. (2010) · Acta Automatica Sinica · doi:10.3724/sp.j.1004.2010.00968 · Cited by 1 · see citing works

A STUDY ON CONTROL METHOD OF URBAN SKYLINE IN JAPANESE CITIES

UMETSU, R., & Asano, J. (2012) · AIJ Journal of Technology and Design · doi:10.3130/aijt.18.319 · Cited by 1 · see citing works

Evaluating Top-k Skyline Queries Efficiently

Goncalves, M., & Vidal, M.-E. (2011) · Advances in data mining and database management book series · doi:10.4018/978-1-60960-475-2.ch004