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

ProteoWizard: open source software for rapid proteomics tools development

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

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The PeptideAtlas project

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Purely Functional Data Structures

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

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

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

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

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

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

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Mass-spectrometric exploration of proteome structure and function

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Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens

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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

Protein Analysis by Shotgun/Bottom-up Proteomics

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Proteogenomic characterization of human colon and rectal cancer

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring

Shi, Y., Gao, W., Lytle, N. K., Huang, P., Yuan, X., Dann, A. M., Ridinger-Saison, M., DelGiorno, K. E., Antal, C. E., Liang, G., et al. (2019) · Nature · doi:10.1038/s41586-019-1130-6 · Cited by 431

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

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

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

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

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

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

USP8 regulates mitophagy by removing K6‐linked ubiquitin conjugates from parkin

Durcan, T. M., Tang, M. Y., Pérusse, J. R., Dashti, E. A., Aguileta, M. A., McLelland, G., Gros, P., Shaler, T. A., Faubert, D., Coulombe, B., et al. (2014) · The EMBO Journal · doi:10.15252/embj.201489729 · Cited by 375

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Functioning of a metabolic flux sensor in Escherichia coli

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

Quantitative measurements of N‐linked glycoproteins in human plasma by SWATH‐MS

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

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

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 & Cellular Proteomics · doi:10.1074/mcp.o114.047035 · Cited by 175

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Rapid empirical discovery of optimal peptides for targeted proteomics

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