Cited by 529Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsMetabolomics and Mass Spectrometry Studies

The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.

The One Hour Yeast Proteome

Open access

Hebert, Alexander S., Richards, Alicia L., Bailey, Derek J., Ulbrich, Arne, Coughlin, Emma E., Westphall, Michael S., Coon, Joshua J.·2014·Molecular & Cellular Proteomics

DOI 10.1074/mcp.m113.034769PMC PMC3879625

Open this paper in CoChat — free account

Abstract

We describe the comprehensive analysis of the yeast proteome in just over one hour of optimized analysis. We achieve this expedited proteome characterization with improved sample preparation, chromatographic separations, and by using a new Orbitrap hybrid mass spectrometer equipped with a mass filter, a collision cell, a high-field Orbitrap analyzer, and, finally, a dual cell linear ion trap analyzer (Q-OT-qIT, Orbitrap Fusion). This system offers high MS(2) acquisition speed of 20 Hz and detects up to 19 peptide sequences within a single second of operation. Over a 1.3 h chromatographic method, the Q-OT-qIT hybrid collected an average of 13,447 MS(1) and 80,460 MS(2) scans (per run) to produce 43,400 (x) peptide spectral matches and 34,255 (x) peptides with unique amino acid sequences (1% false discovery rate (FDR)). On average, each one hour analysis achieved detection of 3,977 proteins (1% FDR). We conclude that further improvements in mass spectrometer scan rate could render comprehensive analysis of the human proteome within a few hours.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1074/mcp.m113.034769

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995) · Science · doi:10.1126/science.270.5235.467 · Cited by 9,609 · cited 1×
“Owing to cost, speed, and accessibility, transcriptomic analysis is often used as a proteomic proxy (3, 4).”
Method
Universal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352
An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484
Exploring the metabolic and genetic control of gene expression on a genomic scale.
Derisi, J., Iyer, V., & Brown, P. (1997) · Science · doi:10.1126/science.278.5338.680 · Cited by 4,832
Large-scale analysis of the yeast proteome by multidimensional protein identification technology
Washburn, M. P., Wolters, D. A., & Yates, J. R. (2001) · Nature Biotechnology · doi:10.1038/85686 · Cited by 4,768
Correlation between Protein and mRNA Abundance in Yeast
Gygi, S. P., Rochon, Y., Franza, B. R., & Aebersold, R. (1999) · Molecular and Cellular Biology · doi:10.1128/mcb.19.3.1720 · Cited by 3,819
Global analysis of protein expression in yeast
Ghaemmaghami, S., Huh, W.-K., Bower, K., Howson, R. W., Belle, A., Dephoure, N., O'Shea, E. K., & Weissman, J. S. (2003) · Nature · doi:10.1038/nature02046 · Cited by 3,750
Direct analysis of protein complexes using mass spectrometry
Link, A. J., Eng, J. K., Schieltz, D. M., Carmack, E., Mize, G. J., Morris, D. R., Garvik, B. M., & Yates, J. R. (1999) · Nature Biotechnology · doi:10.1038/10890 · Cited by 2,218
Saccharomyces Genome Database: the genomics resource of budding yeast
Cherry, J., Hong, E., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E. T., Christie, K., Costanzo, M., Dwight, S., Engel, S., et al. (2011) · Nucleic Acids Res. · doi:10.1093/nar/gkr1029 · Cited by 1,886 · cited 1×
“Historically yeast, with its 6600 open reading frames, has been the preferred proteomic technology test-bed (8).”
Background
Evaluation of Multidimensional Chromatography Coupled with Tandem Mass Spectrometry (LC/LC−MS/MS) for Large-Scale Protein Analysis: The Yeast Proteome
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L., & Gygi, S. P. (2002) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,652

Flagged first, then by citations

Show all 48 references

Sorted by citations

Items 201–300 of 529

Argonaut: A Web Platform for Collaborative Multi-omic Data Visualization and Exploration
Brademan, D. R., Miller, I. J., Kwiecien, N. W., Pagliarini, D. J., Westphall, M. S., Coon, J. J., & Shishkova, E. (2020) · Patterns · doi:10.1016/j.patter.2020.100122 · Cited by 32
Self-citation
Mitochondrial Contact Site and Cristae Organization System and F 1 F O -ATP Synthase Crosstalk Is a Fundamental Property of Mitochondrial Cristae
Cadena, L. R., Gahura, O., Panicucci, B., Zı́ková, A., & Hashimi, H. (2021) · mSphere · doi:10.1128/msphere.00327-21 · Cited by 32
“Triplicate eluates of coimmunoprecipitated proteins were processed for mass spectroscopy analysis as described previously (58, 59).”
Method
Proteomic Stable Isotope Probing Reveals Biosynthesis Dynamics of Slow Growing Methane Based Microbial Communities
Marlow, J. J., Skennerton, C. T., Li, Z., Chourey, K., Hettich, R. L., Pan, C., & Orphan, V. J. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00563 · Cited by 32
“However, the [1%, 1TP, 1UP] approach has been promoted on statistical grounds (Gupta and Pevzner, 2009) and used in several environmental and culture-based proteomics studies (Ram et al., 2005; Dong et al., 2010; Webb et al., 2013; Hebert et al., 2014).”
Comparative proteomic analysis between nitrogen supplemented and starved conditions in Magnaporthe oryzae
Oh, Y., Robertson, S. L., Parker, J. K., Muddiman, D. C., & Dean, R. A. (2017) · Proteome Science · doi:10.1186/s12953-017-0128-y · Cited by 31
“Similar results have been recently reported in yeast where it is now possible to identity nearly 4000 proteins representing ~60% of the theoretical proteome without sample fractionation [27].”
Result
ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching
Cifani, P., Dhabaria, A., Chen, Z., Yoshimi, A., Kawaler, E. A., Abdel-Wahab, O. I., Poirier, J. T., & Kentsis, A. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00295 · Cited by 34
Mass spectrometry-based proteomic exploration of the small urinary extracellular vesicles in ANCA-associated vasculitis in comparison with total urine
Přikryl, P., Satrapová, V., Frýdlová, J., Hrušková, Z., Zima, T., Tesař, V., & Vokurka, M. (2020) · Journal of Proteomics · doi:10.1016/j.jprot.2020.104067 · Cited by 31
Data-Dependent Acquisition Ladder for Capillary Electrophoresis Mass Spectrometry-Based Ultrasensitive (Neuro)Proteomics
Choi, S. B., Muñoz-LLancao, P., Manzini, M. C., & Nemes, P. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c03327 · Cited by 32
mzDB: A File Format Using Multiple Indexing Strategies for the Efficient Analysis of Large LC-MS/MS and SWATH-MS Data Sets *
Bouyssié, D., Dubois, M., Nasso, S., de Peredo, A. G., Burlet‐Schiltz, O., Aebersold, R. H., & Monsarrat, B. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.039115 · Cited by 34
Finding the Sweet Spot in ERLIC Mobile Phase for Simultaneous Enrichment of N-Glyco and Phosphopeptides
Cui, Y., Yang, K., Tabang, D. N., Huang, J., Tang, W., & Li, L. (2019) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-019-02230-6 · Cited by 32
BRAT1 links Integrator and defective RNA processing with neurodegeneration
Cihlářová, Z., Kubovčiak, J., Sobol, M. A., Krejčíková, K., Šáchová, J., Kolář, M., Staněk, D., Bařinka, C., Yoon, G., Caldecott, K. W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32763-6 · Cited by 30
Machine Learning in Mass Spectrometric Analysis of DIA Data
Xu, L. L., Young, A., Zhou, A., & Röst, H. L. (2020) · PROTEOMICS · doi:10.1002/pmic.201900352 · Cited by 33
Neucode Labels for Multiplexed, Absolute Protein Quantification
Potts, G. K., Voigt, E. A., Bailey, D. J., Rose, C. M., Westphall, M. S., Hebert, A. S., Yin, J., & Coon, J. J. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.5b04773 · Cited by 31
Self-citation
3D-SISPROT: A simple and integrated spintip-based protein digestion and three-dimensional peptide fractionation technology for deep proteome profiling
Chen, W., Adhikari, S., Chen, L., Lin, L., Li, H., Luo, S., Yang, P., & Tian, R. (2017) · Journal of Chromatography A · doi:10.1016/j.chroma.2017.01.033 · Cited by 30
Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey
Tariq, M. U., Haseeb, M., Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3047588 · Cited by 34
“Mass spectrometers can generate millions of spectra [35], [36] in a few hours [35] and the number of distinct peaks that needs to be analyzed is enormous (e.”
Background
Performance Investigation of Proteomic Identification by HCD/CID Fragmentations in Combination with High/Low-Resolution Detectors on a Tribrid, High-Field Orbitrap Instrument
Tu, C., Li, J., Shen, S., Sheng, Q., Shyr, Y., & Qu, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160160 · Cited by 28
Quantitative Global Proteomics of Yeast PBP1 Deletion Mutants and Their Stress Responses Identifies Glucose Metabolism, Mitochondrial, and Stress Granule Changes
Seidel, G., Meierhofer, D., Şen, N.-E., Guenther, A., Krobitsch, S., & Auburger, G. W. J. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00647 · Cited by 29
New‐generation mass spectrometry expands the toolbox of cell and developmental biology
Lombard‐Banek, C., Portero, E. P., Onjiko, R. M., & Nemes, P. (2017) · genesis · doi:10.1002/dvg.23012 · Cited by 28
“By analyzing 1–50 mg of proteins, recent-generation MS enables the near complete coverage of the encoded proteomes (Hebert et al., 2014; Nagaraj et al., 2012; Picotti et al., 2013), providing powerful new types of data complementing Next Generation Sequencing.”
Method
Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics
Gao, M., Yang, W., Li, C., Chang, Y., Liu, Y., Wang, S., He, Q., Zhong, C., Shuai, J., Yu, R., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02726-6 · Cited by 28
Improving data quality and preserving HCD-generated reporter ions with EThcD for isobaric tag-based quantitative proteomics and proteome-wide PTM studies
Yu, Q., Shi, X., Yu, F., Kent, K. C., & Li, L. (2017) · Analytica Chimica Acta · doi:10.1016/j.aca.2017.03.003 · Cited by 28
The Proteome of Cataract Markers: Focus on Crystallins
Zhang, K., Zhu, X., & Lü, Y. (2018) · Advances in clinical chemistry · doi:10.1016/bs.acc.2018.05.005 · Cited by 28
Improved Nanoflow RPLC-CZE-MS/MS System with High Peak Capacity and Sensitivity for Nanogram Bottom-up Proteomics
Yang, Z., Shen, X., Chen, D., & Sun, L. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00545 · Cited by 27
The Cannabis Proteome Draft Map Project
Jenkins, C., & Orsburn, B. C. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21030965 · Cited by 26
Yield of 6,000 proteins by 1D nLC–MS/MS without pre-fractionation
Anagnostopoulos, A. K., Stravopodis, D. J., & Tsangaris, G. T. (2016) · Journal of Chromatography B · doi:10.1016/j.jchromb.2016.08.031 · Cited by 27
Beyond the survival and death of the deltamethrin-threatened pollen beetle Meligethes aeneus : An in-depth proteomic study employing a transcriptome database
Erban, T., Harant, K., Chalupnikova, J., Kocourek, F., & Stará, J. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.09.016 · Cited by 27
“1% trifluoroacetic acid, with analysis performed as previously described [17].”
Method
Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries
Hutchins, P. D., Russell, J. D., & Coon, J. J. (2019) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-018-02125-y · Cited by 26
Self-citation
Super-Resolution Localisation of Nuclear PI(4)P and Identification of Its Interacting Proteome
Fáberová, V., Kalasová, I., Krausová, A., & Hozák, P. (2020) · Cells · doi:10.3390/cells9051191 · Cited by 27
Biological mass spectrometry enables spatiotemporal ‘omics: From tissues to cells to organelles
Pade, L. R., Stepler, K. E., Portero, E. P., DeLaney, K., & Nemes, P. (2023) · Mass Spectrometry Reviews · doi:10.1002/mas.21824 · Cited by 28
Toward the complete proteome of Synechocystis sp. PCC 6803
Gao, L., Wang, J., Ge, H., Fang, L., Zhang, Y., Huang, X., & Wang, Y. (2015) · Photosynthesis Research · doi:10.1007/s11120-015-0140-y · Cited by 27
MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing
Awan, M. G., & Saeed, F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw023 · Cited by 27
“With the introduction of modern mass spectrometers such as Thermo Orbitrap, thousands of spectra can be generated in just a single run of experiment [14].”
Expanding Proteoform Identifications in Top-Down Proteomic Analyses by Constructing Proteoform Families
Schaffer, L. V., Shortreed, M. R., Cesnik, A. J., Frey, B. L., Solntsev, S. K., Scalf, M., & Smith, L. M. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04221 · Cited by 27
Narrow-window DIA: Ultra-fast quantitative analysis of comprehensive proteomes with high sequencing depthPreprint
Guzmán, U. H., Martínez‐Val, A., Ye, Z., Damoc, E., Arrey, T. N., Pashkova, A., Denisov, E. V., Petzoldt, J., Peterson, A. C., Harking, F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.02.543374 · Cited by 27
Development and performance evaluation of an ultralow flow nanoliquid chromatography‐tandem mass spectrometry set‐up
Köcher, T., Pichler, P., Pra, M. D., Rieux, L., Swart, R., & Mechtler, K. (2014) · PROTEOMICS · doi:10.1002/pmic.201300418 · Cited by 26
Abundance-based Classifier for the Prediction of Mass Spectrometric Peptide Detectability Upon Enrichment (PPA)
Muntel, J., Boswell, S. A., Tang, S., Ahmed, S., Wapinski, I., Foley, G., Steen, H., & Springer, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044321 · Cited by 28
“For validation, in silico digestion was performed on the identified proteins of the data set under study as described for the training data (identification data from this study in Supplemental Tables S2, S9–S11, additionally used data: Wisniewski et al. (27) and Hebert et al. (2)).”
Method
Post-Translation Regulation of Influenza Virus Replication
Dawson, A. R., Wilson, G. M., Coon, J. J., & Mehle, A. (2020) · Annual Review of Virology · doi:10.1146/annurev-virology-010320-070410 · Cited by 25
Self-citation
Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric
Orsburn, B. C. (2021) · Proteomes · doi:10.3390/proteomes9030034 · Cited by 26
“For a more thorough review of this topic, please see “The One Hour Yeast Proteome”, which thoroughly covers this topic up to the date of its publication [29].”
Background
ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics
Rehfeldt, T. G., Gabriels, R., Bouwmeester, R., Gessulat, S., Neely, B. A., Palmblad, M., Pérez‐Riverol, Y., Schmidt, T., Vizcaíno, J. A., & Deutsch, E. W. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00629 · Cited by 26
RNA interactome capture in yeast
Beckmann, B. M. (2016) · Methods · doi:10.1016/j.ymeth.2016.12.008 · Cited by 25
Single‐Cell Mass Spectrometry for Discovery Proteomics: Quantifying Translational Cell Heterogeneity in the 16‐Cell Frog (Xenopus) Embryo
Lombard‐Banek, C., Moody, S. A., & Nemes, P. (2016) · Angewandte Chemie · doi:10.1002/ange.201510411 · Cited by 27
SuperQuant: A Data Processing Approach to Increase Quantitative Proteome Coverage
Gorshkov, V., Verano‐Braga, T., & Kjeldsen, F. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b01166 · Cited by 24
Bolt: a New Age Peptide Search Engine for Comprehensive MS/MS Sequencing Through Vast Protein Databases in Minutes
Prakash, A., Ahmad, S., Majumder, S., Jenkins, C., & Orsburn, B. C. (2019) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-019-02306-3 · Cited by 23
Shot-gun proteomics: why thousands of unidentified signals matter
den Ridder, M., Daran-Lapujade, P. A. S., & Pabst, M. (2019) · FEMS Yeast Research · doi:10.1093/femsyr/foz088 · Cited by 24
Multidimensional electrostatic repulsion–hydrophilic interaction chromatography (ERLIC) for quantitative analysis of the proteome and phosphoproteome in clinical and biomedical research
Loroch, S., Schommartz, T., Brune, W., Zahedi, R. P., & Sickmann, A. (2015) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2015.01.006 · Cited by 25
Proteomic Analysis of Postsynaptic Protein Complexes Underlying Neuronal Plasticity
Baucum, A. J. (2017) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.7b00008 · Cited by 25
Quantitative proteomics in cardiovascular research: Global and targeted strategies
Shen, X., Young, R. F., Canty, J. M., & Qu, J. (2014) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201400014 · Cited by 25
Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions
Na, S., & Paek, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.002 · Cited by 24
“Recent high-performance liquid chromatography (HPLC) and high MS acquisition speed enabled the detection of tens of thousands of peptides in an hour [8], the detection of over 10,000 proteins from human cell lines [9], and the exploration of the human interactome consisting of ~ 56,000 interactions…”
Background
Methionine to isothreonine conversion as a source of false discovery identifications of genetically encoded variants in proteogenomics
Chernobrovkin, A. L., Kopylov, A. T., Zgoda, V. G., Moysa, A. A., Pyatnitskiy, M. A., Kuznetsova, K. G., Ilina, I. Y., Karpova, M. A., Karpov, D. S., Veselovsky, A. V., et al. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.03.003 · Cited by 24
“[4] Hebert AS, Richards AL, Bailey DJ, Ulbrich A, Coughlin EE, Westphall MS, et al.”
Background
High-Sensitivity Mass Spectrometry for Probing Gene Translation in Single Embryonic Cells in the Early Frog (Xenopus) Embryo
Lombard‐Banek, C., Moody, S. A., & Nemes, P. (2016) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2016.00100 · Cited by 24
Proteomic analysis of serum biomarkers for prediabetes using the Long‐Evans Agouti rat, a spontaneous animal model of type 2 diabetes mellitus
Takahashi, E., Unoki‐Kubota, H., Shimizu, Y., Okamura, T., Iwata, W., Kajio, H., Yamamoto‐Honda, R., Shiga, T., Yamashita, S., Tobe, K., et al. (2017) · Journal of Diabetes Investigation · doi:10.1111/jdi.12638 · Cited by 23
Advances in endometrial cancer protein biomarkers for use in the clinic
Martínez-García, E., López-Gil, C., Campoy, I., Vallvé-Juanico, J., la Rubia, E. C.-D., Cabrera, S., y Cajal, S. R., Matías‐Guiu, X., van Oostrum, J., Reventós, J., et al. (2017) · Expert Review of Proteomics · doi:10.1080/14789450.2018.1410061 · Cited by 25
“Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is currently the preferred analytical platform for the measurement of proteins in a complex biological matrix [24,25].”
Method
A view of the genetic and proteomic profile of extracellular matrix molecules in aging and stroke
Chmelová, M., Androvic, P., Kirdajová, D., Turečková, J., Kriška, J., Valihrach, L., Anděrová, M., & Vargová, L. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1296455 · Cited by 24
Characterization and usage of the EASY-spray technology as part of an online 2D SCX-RP ultra-high pressure system
Marino, F., Cristobal, A., Binai, N. A., Bache, N., Heck, A. J. R., & Mohammed, S. (2014) · The Analyst · doi:10.1039/c4an01568a · Cited by 23
RePLiCal: A QconCAT Protein for Retention Time Standardization in Proteomics Studies
Holman, S. W., McLean, L., & Eyers, C. E. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00988 · Cited by 25
BAC-DROP: Rapid Digestion of Proteome Fractionated via Dissolvable Polyacrylamide Gel Electrophoresis and Its Application to Bottom-Up Proteomics Workflow
Takemori, A., Ishizaki, J., Nakashima, K., Shibata, T., Kato, H., Kodera, Y., Suzuki, T., Hasegawa, H., & Takemori, N. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00749 · Cited by 23
Obtaining Complete Human Proteomes
Martínez‐Val, A., Guzmán, U. H., & Olsen, J. V. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-112921-024948 · Cited by 23
Optimization of parameters affecting signal intensity in an LTQ-orbitrap in negative ion mode: A design of experiments approach
Lemonakis, N., Skaltsounis, A., Tsarbopoulos, A., & Gikas, E. E. (2015) · Talanta · doi:10.1016/j.talanta.2015.10.009 · Cited by 23
Exploring dinoflagellate biology with high-throughput proteomics
Morse, D., Tse, S. P.-K., & Lo, S. C. (2018) · Harmful Algae · doi:10.1016/j.hal.2018.03.010 · Cited by 25
Proteome response of two natural strains ofSaccharomyces cerevisiaewith divergent lignocellulosic inhibitor stress tolerance
de Witt, R. N., Kroukamp, H., & Volschenk, H. (2018) · FEMS Yeast Research · doi:10.1093/femsyr/foy116 · Cited by 23
Comprehensive proteomic analysis of exoproteins expressed by ERIC I, II, III and IV Paenibacillus larvae genotypes reveals a wide range of virulence factors
Erban, T., Zítek, J., Bodrinova, M., Talacko, P., Bartoš, M., & Hrabák, J. (2019) · Virulence · doi:10.1080/21505594.2019.1603133 · Cited by 22
“The instrument was run in top speed mode with 2 s cycles [88,89].”
Method
DIA-based systems biology approach unveils E3 ubiquitin ligase-dependent responses to a metabolic shift
Karayel, Ö., Michaelis, A. C., Mann, M., Schulman, B. A., & Langlois, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020197117 · Cited by 22
Multi-omics analysis identifies essential regulators of mitochondrial stress response in two wild-type C. elegans strains
Gao, A. W., Alam, G. E., Lalou, A., Li, T. Y., Molenaars, M., Zhu, Y., Overmyer, K. A., Shishkova, E., Hof, K., Sleiman, M. S. B., et al. (2022) · iScience · doi:10.1016/j.isci.2022.103734 · Cited by 22
Self-citation
Phosphoproteome Discovery in Human Biological Fluids
Giorgianni, F., & Beranová-Giorgianni, Š. (2016) · Proteomes · doi:10.3390/proteomes4040037 · Cited by 22
A tail of two voltages: Proteomic comparison of the three electric organs of the electric eel
Traeger, L. L., Sabat, G., Barrett‐Wilt, G. A., Wells, G. B., & Sussman, M. R. (2017) · Science Advances · doi:10.1126/sciadv.1700523 · Cited by 22
ICan: An Optimized Ion-Current-Based Quantification Procedure with Enhanced Quantitative Accuracy and Sensitivity in Biomarker Discovery
Tu, C., Sheng, Q., Li, J., Shen, X., Zhang, M., Shyr, Y., & Qu, J. (2014) · Journal of Proteome Research · doi:10.1021/pr5008224 · Cited by 21
Survey of Shotgun Proteomics
Nogueira, F. C. S., & Domont, G. B. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0685-7_1 · Cited by 23
Characterization of 150 Wheat Cultivars by LC-MS-Based Label-Free Quantitative Proteomics Unravels Possibilities to Design Wheat Better for Baking Quality and Human Health
Afzal, M., Sielaff, M., Curella, V., Neerukonda, M., Hassouni, K. E., Schuppan, D., Tenzer, S., & Longin, C. F. H. (2021) · Plants · doi:10.3390/plants10030424 · Cited by 22
“During the last decade, the technology of mass spectrometry has been revolutionized [30], permitting a detailed analysis of the whole proteomes of eukaryotes [31], including humans [32] and plants [33].”
Background
Shotgun proteomics of bacterial pathogens: advances, challenges and clinical implications
Šemanjski, M., & Maček, B. (2015) · Expert Review of Proteomics · doi:10.1586/14789450.2016.1132168 · Cited by 22
“[10,11] even in a very short time frame [12].”
Background
Top-down protein identification using isotopic envelope fingerprinting
Xiao, K., Yu, F., & Tian, Z. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.10.010 · Cited by 21
Time, space, and disorder in the expanding proteome universe
Minde, D., Dunker, A. K., & Lilley, K. S. (2017) · PROTEOMICS · doi:10.1002/pmic.201600399 · Cited by 21
Impact of Mass Spectrometry-Based Technologies and Strategies on Chemoproteomics as a Tool for Drug Discovery
McClure, R. A., & Williams, J. D. (2018) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.8b00181 · Cited by 21
Fast Proteome Identification and Quantification from Data-Dependent Acquisition–Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools
Meyer, J. G. (2019) · Methods and Protocols · doi:10.3390/mps2010008 · Cited by 23
Stress modulation as a means to improve yeasts for lignocellulose bioconversion
Brandt, B. A., Jansen, T., Volschenk, H., Görgens, J. F., van Zyl, W. H., & den Haan, R. (2021) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-021-11383-y · Cited by 21
NeuCode Labeling in Nematodes: Proteomic and Phosphoproteomic Impact of Ascaroside Treatment in Caenorhabditis elegans
Rhoads, T. W., Prasad, A. K., Kwiecien, N. W., Merrill, A. E., Zawack, K., Westphall, M. S., Schroeder, F. C., Kimble, J. E., & Coon, J. J. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.049684 · Cited by 20
Self-citation
Multi-omics Evidence for Inheritance of Energy Pathways in Red Blood Cells
Weisenhorn, E., Erve, T. J. V. ‘., Riley, N. M., Hess, J. R., Raife, T. J., & Coon, J. J. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.062349 · Cited by 20
“The column was installed on a Dionex Ultimate 3000 UPLC system and heated to 60 °C using an in house designed column heater for all runs (33, 34).”
MethodSelf-citation
Iron economy in Naegleria gruberi reflects its metabolic flexibility
Mach, J., Bíla, J., Ženíšková, K., Arbon, D., Malych, R., Glavanakovová, M., Nývltová, E., & Šuťák, R. (2018) · International Journal for Parasitology · doi:10.1016/j.ijpara.2018.03.005 · Cited by 20
High-resolution proteomics reveals differences in the proteome of spelt and bread wheat flour representing targets for research on wheat sensitivities
Afzal, M., Pfannstiel, J., Zimmermann, J., Bischoff, S. C., Würschum, T., & Longin, C. F. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-71712-5 · Cited by 22
Molecular Biomarkers of Neovascular Age-Related Macular Degeneration With Incomplete Response to Anti-Vascular Endothelial Growth Factor Treatment
Mantel, I., Borgo, A., Guidotti, J. M., Forestier, E., Kirsch, O., Derradji, Y., Waridel, P., Burdet, F., Mehl, F., Schweizer, C., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.594087 · Cited by 20
Evaluation of dimethyl sulfoxide (DMSO) as a mobile phase additive during top 3 label-free quantitative proteomics
Strzelecka, D., Holman, S. W., & Eyers, C. E. (2015) · International Journal of Mass Spectrometry · doi:10.1016/j.ijms.2015.07.004 · Cited by 20
“9 % respectively) were observed, which were broadly similar in magnitude to that observed in other recent studies.[10, 11] The increases were lower than those reported in the original work of Hahne et al.”
Result
High performance computing framework for tera-scale database search of mass spectrometry data
Haseeb, M., & Saeed, F. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00113-z · Cited by 18
GreenPhos, a universal method for in-depth measurement of plant phosphoproteomes with high quantitative reproducibility
Duan, X., Zhang, Y., Huang, X., Ma, X., Gao, H., Wang, Y., Xiao, Z., Huang, C., Wang, Z., Li, B., et al. (2023) · Molecular Plant · doi:10.1016/j.molp.2023.11.010 · Cited by 19
Proteomic profiling of nipple aspirate fluid (NAF): Exploring the complementarity of different peptide fractionation strategies
Brunoro, G. V. F., Carvalho, P. C., Teixeira‐Ferreira, A., Perales, J., Valente, R. H., Gallo, C. V. D. M., Pagnoncelli, D., & Neves‐Ferreira, A. G. C. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.01.011 · Cited by 19
“One of the most striking examples of this powerful approach was the detection of 3977 yeast proteins during a 70-min analysis time [7].”
Background
A rapid methods development workflow for high-throughput quantitative proteomic applications
Chen, Y., Vu, J. T., Thompson, M. G., Sharpless, W. A., Chan, L. J. G., Gin, J., Keasling, J. D., Adams, P. D., & Petzold, C. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211582 · Cited by 17
“To extend the workflow to take advantage of community resources, we also built spectral libraries from raw data acquired from public proteomic data repositories, such as data from a comprehensive proteomic analysis of Pseudomonas putida F1 from PRIDE (PXD001219), and data of the one-hour S. cerevisiae BY4741 proteome…”
BackgroundInfluential
Genome Wide Phosphoproteome Analysis of Zymomonas mobilis Under Anaerobic, Aerobic, and N2-Fixing Conditions
Tatli, M., Hebert, A. S., Coon, J. J., & Amador‐Noguez, D. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01986 · Cited by 18
Self-citation
The evolution of the Puf superfamily of proteins across the tree of eukaryotes
Najdrová, V., Stairs, C. W., Vinopalová, M., Voleman, L., & Doležal, P. (2020) · BMC Biology · doi:10.1186/s12915-020-00814-3 · Cited by 18
“The instrument was run in top speed mode with 2-s cycles [82].”
Method
Liver transplantation after an acute episode of spontaneous bacterial peritonitis.
Thiel, D. H. V., Hassanein, T., Gurakar, A. O., Wright, H. I., Caraceni, P., Maria, N. D., & Nadir, A. (1996) · PubMed · Cited by 19
MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion
Pelletier, A. R., Chung, Y.-E., Ning, Z., Wong, N., Figeys, D., & Lavallée‐Adam, M. (2020) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.0c00064 · Cited by 17
Differential proteomic analysis of laser-microdissected penetration glands of avian schistosome cercariae with a focus on proteins involved in host invasion
Vondráček, O., Mikeš, L., Talacko, P., Leontovyč, R., Bulantová, J., & Horák, P. (2022) · International Journal for Parasitology · doi:10.1016/j.ijpara.2021.12.003 · Cited by 18
Deep Proteome Profiling with Reduced Carryover Using Superficially Porous Microfabricated nanoLC Columns
Stejskal, K., Beeck, J. O. D., Matzinger, M., Dürnberger, G., Boychenko, O., Jacobs, P., & Mechtler, K. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c01196 · Cited by 18
Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data‐dependent proteomics
Huang, P., Liu, C., Gao, W., Chu, B., Cai, Z., & Tian, R. (2020) · Journal of Mass Spectrometry · doi:10.1002/jms.4653 · Cited by 17
Nanoscale Solid-Phase Isobaric Labeling for Multiplexed Quantitative Phosphoproteomics
Ogata, K., Tsai, C., & Ishihama, Y. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00444 · Cited by 17
Quantitation of Protein Post-Translational Modifications Using Isobaric Tandem Mass Tags
Liang, H., Lahert, E., Pike, I., & Ward, M. A. (2015) · Bioanalysis · doi:10.4155/bio.14.296 · Cited by 18
Introduction to Proteomics Technologies
Lenz, C., & Dihazi, H. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3106-4_1 · Cited by 18
Multicenter experiment for quality control of peptide-centric LC–MS/MS analysis — A longitudinal performance assessment with nLC coupled to orbitrap MS analyzers
Campos, A. R., Díaz, R., Martínez‐Bartolomé, S., Sierra, J., Gallardo, Ó., Sabidó, E., López-Lucendo, M. F., Casal, J. I., Pasquarello, C., Scherl, A., et al. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.05.012 · Cited by 18
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
High, A. A., Tan, H., Pagala, V., Niu, M., Cho, J.-H., Wang, X., Bai, B., & Peng, J. (2017) · Journal of Visualized Experiments · doi:10.3791/56474 · Cited by 18
Target-small decoy search strategy for false discovery rate estimation
Kim, H., Lee, S., & Park, H. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3034-8 · Cited by 18
The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?
Jiang, Y., DeBord, D., Vitrac, H., Stewart, J., Haghani, A., Eyk, J. E. V., Fert‐Bober, J., & Meyer, J. G. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00248 · Cited by 17
Systematic comparison between SDS-PAGE/RPLC and high-/low-pH RPLC coupled tandem mass spectrometry strategies in a whole proteome analysis
Yin, X., Zhang, Y., Liu, X., Chen, C., Lu, H., Shen, H., & Yang, P. (2014) · The Analyst · doi:10.1039/c4an02119c · Cited by 18
Separation methodology to improve proteome coverage depth
Lenz, C., & Urlaub, H. (2014) · Expert Review of Proteomics · doi:10.1586/14789450.2014.919862 · Cited by 17
“While in studies with yeast this number is sufficient to cover approximately 90% of the expressed proteome [11,26], in human currently only up to 50–60% of the expressed proteome are covered, corresponding mainly to the upper half of the expressed proteins’ concentration range.”
Background
Accelerating Lipidomic Method Development through in Silico Simulation
Hutchins, P. D., Russell, J. D., & Coon, J. J. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b01234 · Cited by 16
Self-citation
An integrated strategy for the quantitative analysis of endogenous proteins: A case of gender-dependent expression of P450 enzymes in rat liver microsome
Shao, Y., Yin, X., Kang, D., Shen, B., Zhu, Z., Li, X., Li, H., Xie, L. Y., Wang, G., & Liang, Y. (2017) · Talanta · doi:10.1016/j.talanta.2017.04.050 · Cited by 17
“For instance, high resolution mass spectrometers have been proven to play a pivotal role in the field of proteomics research, and recent improvements in sensitivity and scan rates further widened their applications toward the study of biological molecules [22,23].”
The clinical potential of prm-PASEF mass spectrometry
Lesur, A., & Dittmar, G. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1908895 · Cited by 17

Authors: Hebert, A. S., Richards, A. L., Bailey, D. J., Ulbrich, A., Coughlin, E. E., Westphall, M. S., & Coon, J. J.
Affiliations: University of Wisconsin–Madison (US)
Corresponding author: Coon, Joshua J.
ORCID: Hebert, Alexander S.
Richards, Alicia L.
Coughlin, Emma E.
Westphall, Michael S.
Coon, Joshua J.
Venue: Molecular & Cellular Proteomics
Publisher: Elsevier BV
Published: 2014-01
Volume: 13
Issue: 1
Pages: 339-347
ISSN: 1535-9476 (ISSN-L)
DOI: 10.1074/mcp.m113.034769
PMC: PMC3879625
OpenAlex: W1991502938
Cited by: 528
References: 48
Citations by year: 2026: 9 · 2025: 13 · 2024: 18 · 2023: 26 · 2022: 23 · 2021: 47 · 2020: 40 · 2019: 49 · 2018: 44 · 2017: 55 · 2016: 77 · 2015: 60 · 2014: 64
FWCI: 30.34
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 2460 d)
Locations: Molecular & Cellular Proteomics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Human Genome Research Institute (T32HG002760)
National Institute of General Medical Sciences (R01GM080148)
Topics: Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
Keywords: yeast proteome, peptide identification, sample preparation optimization, chromatographic separation, human proteome
MeSH terms: Amino Acid Sequence, Humans, Molecular Weight, Peptides, Saccharomyces cerevisiae, Proteome, Tandem Mass Spectrometry

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The One Hour Yeast Proteome

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks