An automated pipeline for high-throughput label-free quantitative proteomics

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification

Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works

Mass spectrometry-based proteomics

Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955 · see citing works

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 · see citing works

Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment

Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101065j · Cited by 5,349 · see citing works

XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification

Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. A., & Siuzdak, G. E. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,296 · see citing works

Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Keller, A., Nesvizhskii, A., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,970 · see citing works

A statistical model for identifying proteins by tandem mass spectrometry.

Nesvizhskii, A., Keller, A., Kolker, E., & Aebersold, R. (2003) · Analytical Chemistry · doi:10.1021/ac0341261 · Cited by 4,902 · see citing works

TANDEM: matching proteins with tandem mass spectra

Craig, R., & Beavis, R. C. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth092 · Cited by 2,553 · see citing works

A Model for Random Sampling and Estimation of Relative Protein Abundance in Shotgun Proteomics

Liu, H., Sadygov, R. G., & Yates, J. R. (2004) · Analytical Chemistry · doi:10.1021/ac0498563 · Cited by 2,486 · see citing works

Semi-supervised learning for peptide identification from shotgun proteomics datasets

Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424 · see citing works

Quantitative mass spectrometry in proteomics: a critical review

Bantscheff, M., Schirle, M., Sweetman, G. M. A., Rick, J. M., & Küster, B. (2007) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-007-1486-6 · Cited by 1,659 · see citing works

Absolute Quantification of Proteins by LCMSE

Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C., & Geromanos, S. J. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500230-mcp200 · Cited by 1,529 · see citing works

Open Mass Spectrometry Search Algorithm

Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L., Xu, M. Q., Maynard, D., Yang, X., Shi, W., & Bryant, S. H. (2004) · Journal of Proteome Research · doi:10.1021/pr0499491 · Cited by 1,420 · see citing works

Selected reaction monitoring for quantitative proteomics: a tutorial

Lange, V., Picotti, P., Domon, B., & Aebersold, R. H. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.61 · Cited by 1,396 · see citing works

Exploring Expression Data: Identification and Analysis of Coexpressed GenesPreprint

Heyer, L. J., Kruglyak, S., & Yooseph, S. (1999) · Genome Research · doi:10.1101/gr.9.11.1106 · Cited by 1,034 · see citing works

mzML—a Community Standard for Mass Spectrometry Data

Martens, L., Chambers, M., Sturm, M., Kessner, D., Levander, F., Shofstahl, J., Tang, W. H., Römpp, A., Neumann, S., Pizarro, A., et al. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r110.000133 · Cited by 754 · see citing works

A guided tour of the Trans‐Proteomic Pipeline

Deutsch, E. W., Mendoza, L., Shteynberg, D. D., Farrah, T., Lam, H., Tasman, N., Sun, Z., Nilsson, E. J., Pratt, B. S., Prazen, B. J., et al. (2010) · PROTEOMICS · doi:10.1002/pmic.200900375 · Cited by 746 · see citing works

OpenMS – An open-source software framework for mass spectrometry

Sturm, M., Bertsch, A., Gröpl, C., Hildebrandt, A., Hussong, R., Lange, E., Pfeifer, N., Schulz-Trieglaff, O. B., Zerck, A., Reinert, K., et al. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-163 · Cited by 646 · see citing works

Options and considerations when selecting a quantitative proteomics strategy

Domon, B., & Aebersold, R. H. (2010) · Nature Biotechnology · doi:10.1038/nbt.1661 · Cited by 601 · see citing works

InsPecT: Identification of Posttranslationally Modified Peptides from Tandem Mass Spectra

Tanner, S. W., Shu, H., Frank, A. M., Wang, L.-C., Zandi, E., Mumby, M. C., Pevzner, P. A., & Bafna, V. (2005) · Analytical Chemistry · doi:10.1021/ac050102d · Cited by 568 · see citing works

MyriMatch: Highly Accurate Tandem Mass Spectral Peptide Identification by Multivariate Hypergeometric Analysis

Tabb, D. L., Fernando, C., & Chambers, M. (2007) · Journal of Proteome Research · doi:10.1021/pr0604054 · Cited by 548 · see citing works

iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates

Shteynberg, D. D., Deutsch, E. W., Lam, H., Eng, J. K., Sun, Z., Tasman, N., Mendoza, L., Moritz, R. L., Aebersold, R. H., & Nesvizhskii, A. I. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.007690 · Cited by 548 · see citing works

Development and validation of a spectral library searching method for peptide identification from MS/MS

Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K., King, N., Stein, S. E., & Aebersold, R. (2007) · PROTEOMICS · doi:10.1002/pmic.200600625 · Cited by 517 · see citing works

Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions

Senko, M. W., Beu, S. C., & McLaffertycor, F. W. (1995) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(95)00017-8 · Cited by 451 · see citing works

Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans

Malmström, J., Beck, M., Schmidt, A., Lange, V., Deutsch, E. W., & Aebersold, R. (2009) · Nature · doi:10.1038/nature08184 · Cited by 421 · see citing works

Targeted proteomic strategy for clinical biomarker discovery

Schiess, R., Wollscheid, B., & Aebersold, R. H. (2008) · Molecular Oncology · doi:10.1016/j.molonc.2008.12.001 · Cited by 369 · see citing works

PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species

Gillespie, J. J., Wattam, A. R., Cammer, S. A., Gabbard, J. L., Shukla, M. P., Dalay, O., Driscoll, T., Hix, D., Mane, S. P., Mao, C., et al. (2011) · Infection and Immunity · doi:10.1128/iai.00207-11 · Cited by 348 · see citing works

SuperHirn – a novel tool for high resolution LC‐MS‐based peptide/protein profiling

Mueller, L. N., Rinner, O., Schmidt, A., Letarte, S., Bodenmiller, B., Brusniak, M., Olga Vitek, A., Aebersold, R. H., & Müller, M. (2007) · PROTEOMICS · doi:10.1002/pmic.200700057 · Cited by 308 · see citing works

Posterior Error Probabilities and False Discovery Rates: Two Sides of the Same Coin

Käll, L., Storey, J. D., MacCoss, M. J., & Noble, W. S. (2007) · Journal of Proteome Research · doi:10.1021/pr700739d · Cited by 293 · see citing works

TOPP—the OpenMS proteomics pipeline

Kohlbacher, O., Reinert, K., Gröpl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O. B., & Sturm, M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl299 · Cited by 281 · see citing works

Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry

Ludwig, C., Claassen, M., Schmidt, A., & Aebersold, R. H. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.013987 · Cited by 137 · see citing works

A hybrid of exponential and gaussian functions as a simple model of asymmetric chromatographic peaks

Lan, K., & Jorgenson, J. W. (2001) · Journal of Chromatography A · doi:10.1016/s0021-9673(01)00594-5 · Cited by 123 · see citing works

Absolute quantification of microbial proteomes at different states by directed mass spectrometry

Schmidt, A., Beck, M., Malmström, J. A., Lam, H., Claassen, M., Campbell, D. S., & Aebersold, R. H. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.37 · Cited by 101 · see citing works

A geometric approach for the alignment of liquid chromatography—mass spectrometry data

Lange, E., Gröpl, C., Schulz-Trieglaff, O. B., Leinenbach, A., Huber, C. G., & Reinert, K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm209 · Cited by 101 · see citing works

Protein Quantification in Label-Free LC-MS Experiments

Clough, T., Key, M., Ott, I., Ragg, S., Schadow, G., & Vitek, O. (2009) · Journal of Proteome Research · doi:10.1021/pr900610q · Cited by 96 · see citing works

TOPPView: An Open-Source Viewer for Mass Spectrometry Data

Sturm, M., & Kohlbacher, O. (2009) · Journal of Proteome Research · doi:10.1021/pr900171m · Cited by 93 · see citing works

HIGH-ACCURACY PEAK PICKING OF PROTEOMICS DATA USING WAVELET TECHNIQUES

Lange, E., Gröpl, C., Reinert, K., Kohlbacher, O., & Hildebrandt, A. (2005) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812701626_0023 · Cited by 91 · see citing works

Comparison of two-dimensional electrophoresis patterns of heat shock protein Hsp27 species in normal and cardiomyopathic hearts

Scheler, C., Li, X., Salnikow, J., Dunn, M. J., & Jungblut, P. R. (1999) · Electrophoresis · doi:10.1002/(sici)1522-2683(19991201)20:18 · Cited by 72 · see citing works

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

Brusniak, M., Bodenmiller, B., Campbell, D. S., Cooke, K., Eddes, J. S., Garbutt, A., Lau, H., Letarte, S., Mueller, L. N., Sharma, V., et al. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-542 · Cited by 72 · see citing works

Probabilistic Consensus Scoring Improves Tandem Mass Spectrometry Peptide Identification

Nahnsen, S., Bertsch, A., Rahnenführer, J., Nordheim, A., & Kohlbacher, O. (2011) · Journal of Proteome Research · doi:10.1021/pr2002879 · Cited by 46 · see citing works

TOPPAS: A Graphical Workflow Editor for the Analysis of High-Throughput Proteomics Data

Junker, J., Bielow, C., Bertsch, A., Sturm, M., Reinert, K., & Kohlbacher, O. (2012) · Journal of Proteome Research · doi:10.1021/pr300187f · Cited by 45 · see citing works

Highly accelerated feature detection in proteomics data sets using modern graphics processing units

Hussong, R., Gregorius, B., Tholey, A., & Hildebrandt, A. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp294 · Cited by 42 · see citing works

Streptococcus pyogenes in Human Plasma

Malmström, J. A., Karlsson, C. A. Q., Nordenfelt, P., Ossola, R., Weisser, H., Quandt, A., Hansson, K., Aebersold, R. H., Malmström, L., & Björck, L. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.267674 · Cited by 36 · see citing works

Time-series alignment by non-negative multiple generalized canonical correlation analysis

Fischer, B. M., Röth, V., & Buhmann, J. M. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-s10-s4 · Cited by 28 · see citing works

Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ

Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180 · see citing works

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

Röst, H. L., Sachsenberg, T. T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H., Gutenbrunner, P., Kenar, E., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3959 · Cited by 680 · see citing works

Mining seed proteome: from protein dynamics to modification profiles

Frolov, A., Мамонтова, Т. В., Ihling, C. H., Lukasheva, E., Bankin, M., Chantseva, V. V., Vikhnina, M., Soboleva, A., Shumilina, J., Mavropolo-Stolyarenko, G., et al. (2018) · Biological Communications · doi:10.21638/spbu03.2018.106 · Cited by 381 · 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

Proteome Discoverer—A Community Enhanced Data Processing Suite for Protein Informatics

Orsburn, B. C. (2021) · Proteomes · doi:10.3390/proteomes9010015 · 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

Genome-wide Mapping of Transcriptional Start Sites Defines an Extensive Leaderless Transcriptome in Mycobacterium tuberculosis

Cortes, T., Schubert, O. T., Rose, G., Arnvig, K. B., Comas, I., Aebersold, R., & Young, D. B. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.10.031 · Cited by 310 · see citing works

Systems proteomics of liver mitochondria function

Williams, E. G., Wu, Y., Jha, P., Dubuis, S., Blattmann, P., Argmann, C. A., Houten, S. M., Amariuta, T., Wolski, W., Zamboni, N., et al. (2016) · Science · doi:10.1126/science.aad0189 · Cited by 289 · see citing works

Quantitative proteomics: challenges and opportunities in basic and applied research

Schubert, O. T., Röst, H. L., Collins, B. C., Rosenberger, G., & Aebersold, R. H. (2017) · Nature Protocols · doi:10.1038/nprot.2017.040 · Cited by 276 · see citing works

Integrated transcriptomic and proteomic analyses of P. falciparum gametocytes: molecular insight into sex-specific processes and translational repression

Lasonder, E., Rijpma, S. R., van Schaijk, B. C. L., Hoeijmakers, W. A. M., Kensche, P. R., Gresnigt, M. S., Italiaander, A., Vos, M. W., Woestenenk, R., Bousema, T., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw536 · Cited by 230 · see citing works

Glycoproteomic Analysis of Prostate Cancer Tissues by SWATH Mass Spectrometry Discovers N-acylethanolamine Acid Amidase and Protein Tyrosine Kinase 7 as Signatures for Tumor Aggressiveness

Liu, Y., Chen, J., Sethi, A., Li, Q. K., Chen, L., Collins, B. C., Gillet, L. C. J., Wollscheid, B., Zhang, H., & Aebersold, R. H. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.038273 · Cited by 187 · see citing works

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Tang, J., Fu, J., Wang, Y., Li, B., Li, Y., Yang, Q., Cui, X., Hong, J., Li, X., Chen, Y., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby127 · Cited by 168 · see citing works

pyOpenMS: A Python‐based interface to the OpenMS mass‐spectrometry algorithm library

Röst, H. L., Schmitt, U., Aebersold, R. H., & Malmström, L. (2013) · PROTEOMICS · doi:10.1002/pmic.201300246 · Cited by 167 · see citing works

Current Challenges in Plant Eco-Metabolomics

Peters, K., Worrich, A., Weinhold, A., Alka, O., Balcke, G. U., Birkemeyer, C., Bruelheide, H., Calf, O. W., Dietz, S., Dührkop, K., et al. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19051385 · Cited by 139 · see citing works

OpenMS – A platform for reproducible analysis of mass spectrometry data

Pfeuffer, J., Sachsenberg, T. T., Alka, O., Walzer, M., Fillbrunn, A., Nilse, L., Schilling, O., Reinert, K., & Kohlbacher, O. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.05.016 · Cited by 106 · see citing works

Prediction of colorectal cancer diagnosis based on circulating plasma proteins

Šurinová, S., Choi, M., Tao, S., Schüffler, P. J., Chang, C.-Y., Clough, T., Vyslouzil, K., Khoylou, M., Srovnal, J., Liu, Y., et al. (2015) · EMBO Molecular Medicine · doi:10.15252/emmm.201404873 · Cited by 96 · see citing works

Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset

Ramus, C., Hovasse, A., Marcellin, M., Hesse, A.-M., Mouton‐Barbosa, E., Bouyssié, D., Vaca, S., Carapito, C., Chaoui, K., Bruley, C., et al. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.11.011 · Cited by 87 · see citing works

Algorithms and design strategies towards automated glycoproteomics analysis

Hu, H., Khatri, K., & Zaia, J. (2016) · Mass Spectrometry Reviews · doi:10.1002/mas.21487 · Cited by 85 · see citing works

Analysis of the Cerebrospinal Fluid Proteome in Alzheimer's Disease

Khoonsari, P. E., Månberg, A., Lönnberg, M., Mikus, M. S., Kilander, L., Lannfelt, L., Bergquist, J., Ingelsson, M., Nilsson, P., Kultima, K., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150672 · Cited by 84 · see citing works

IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts

Shen, X., Shen, S., Li, J., Hu, Q., Nie, L., Tu, C., Wang, X., Poulsen, D. J., Orsburn, B. C., Wang, J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800541115 · Cited by 76 · see citing works

Quantitative proteomics in the field of microbiology

Otto, A., Becher, D., & Schmidt, F. (2013) · PROTEOMICS · doi:10.1002/pmic.201300403 · Cited by 71 · see citing works

Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis

Oliveira, A. P., Ludwig, C., Zampieri, M., Weisser, H., Aebersold, R. H., & Sauer, U. (2015) · Science Signaling · doi:10.1126/scisignal.2005768 · Cited by 68 · see citing works

DeMix-Q: Quantification-Centered Data Processing Workflow

Zhang, B., Käll, L., & Zubarev, R. A. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o115.055475 · Cited by 65 · see citing works

Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes

Ammar, C., Schessner, J. P., Willems, S., Michaelis, A. C., & Mann, M. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100581 · Cited by 64 · see citing works

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

Herman, S., Khoonsari, P. E., Tolf, A., Steinmetz, J. L., Zetterberg, H., Åkerfeldt, T., Jakobsson, P., Larsson, A., Spjuth, O., Burman, J., et al. (2018) · Theranostics · doi:10.7150/thno.26249 · Cited by 64 · see citing works

Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing–Remitting Multiple Sclerosis

Herman, S., Åkerfeldt, T., Spjuth, O., Burman, J., & Kultima, K. (2019) · Cells · doi:10.3390/cells8020084 · Cited by 63 · see citing works

Quantitative Profiling of Post-translational Modifications by Immunoaffinity Enrichment and LC-MS/MS in Cancer Serum without Immunodepletion

Gu, H., Ren, J. M., Jia, X., Levy, T. M., Rikova, K., Yang, V., Lee, K. A., Stokes, M. P., & Silva, J. C. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o115.052266 · Cited by 63 · see citing works

Application of volumetric absorptive microsampling for robust, high-throughput mass spectrometric quantification of circulating protein biomarkers

van den Broek, I., Fu, Q., Kushon, S., Kowalski, M. P., Millis, K., Percy, A., Holewinski, R. J., Venkatraman, V., & Van Eyk, J. E. (2017) · Clinical Mass Spectrometry · doi:10.1016/j.clinms.2017.08.004 · Cited by 62 · see citing works

Proteomic analysis of the thermophilic methylotroph B acillus methanolicus MGA 3

Müller, J. E. N., Litsanov, B., Bortfeld‐Miller, M., Trachsel, C., Grossmann, J., Brautaset, T., & Vorholt, J. A. (2014) · PROTEOMICS · doi:10.1002/pmic.201300515 · Cited by 62 · see citing works

Advances in Mass Spectrometric Tools for Probing Neuropeptides

Buchberger, A. R., Yu, Q., & Li, L. (2015) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-071114-040210 · Cited by 61 · see citing works

DeMix Workflow for Efficient Identification of Cofragmented Peptides in High Resolution Data-dependent Tandem Mass Spectrometry

Zhang, B., Pirmoradian, M., Chernobrovkin, A. L., & Zubarev, R. A. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.038877 · Cited by 56 · see citing works

IceR improves proteome coverage and data completeness in global and single-cell proteomics

Kalxdorf, M., Müller, T., Stegle, O., & Krijgsveld, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25077-6 · Cited by 55 · see citing works

Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington’s disease subjects

Herman, S., Niemelä, V., Khoonsari, P. E., Sundblom, J., Burman, J., Landtblom, A., Spjuth, O., Nyholm, D., & Kultima, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-40186-5 · Cited by 54 · see citing works

Container-based bioinformatics with Pachyderm

Novella, J. A., Khoonsari, P. E., Herman, S., Whitenack, D., Capuccini, M., Burman, J., Kultima, K., & Spjuth, O. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty699 · Cited by 54 · see citing works

MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics

Bichmann, L., Nelde, A., Ghosh, M., Heumos, L., Mohr, C., Peltzer, A., Kuchenbecker, L., Sachsenberg, T. T., Walz, J. S., Stevanović, S., et al. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00313 · Cited by 50 · see citing works

Mass++: A Visualization and Analysis Tool for Mass Spectrometry

Tanaka, S., Fujita, Y., Parry, H. E., Yoshizawa, A. C., Morimoto, K., Murase, M., Yamada, Y., Yao, J., Utsunomiya, S.-I., Kajihara, S., et al. (2014) · Journal of Proteome Research · doi:10.1021/pr500155z · Cited by 48 · see citing works

Cellular costs underpin micronutrient limitation in phytoplankton

McCain, J. S. P., Tagliabue, A., Susko, E., Achterberg, E. P., Allen, A. E., & Bertrand, E. M. (2021) · Science Advances · doi:10.1126/sciadv.abg6501 · Cited by 47 · see citing works

Targeted Feature Detection for Data-Dependent Shotgun Proteomics

Weisser, H., & Choudhary, J. S. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00248 · Cited by 47 · see citing works

A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction

Fu, H., Guo, J., Yu, Y., Li, H.-D., Cui, H., Liu, P., Wang, B., Wang, S., & Lü, P. (2016) · Journal of Chromatography A · doi:10.1016/j.chroma.2016.05.018 · Cited by 46 · see citing works

Delayed effects of transcriptional responses in Mycobacterium tuberculosis exposed to nitric oxide suggest other mechanisms involved in survival

Cortes, T., Schubert, O. T., Banaei‐Esfahani, A., Collins, B. C., Aebersold, R. H., & Young, D. (2017) · Scientific Reports · doi:10.1038/s41598-017-08306-1 · Cited by 45 · see citing works

Probing the colorectal cancer proteome for biomarkers: Current status and perspectives

Lee, P. Y., Chin, S., Low, T. Y., & Jamal, R. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.06.014 · Cited by 45 · see citing works

Functional neuropeptidomics in invertebrates

Haes, W. D., Sinay, E. V., Detienne, G., Temmerman, L., Schoofs, L., & Boonen, K. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.12.011 · Cited by 43 · see citing works

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

Lenz, S., Giese, S. H., Fischer, L., & Rappsilber, J. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00600 · Cited by 40 · see citing works

Ubiquitously Expressed Proteins and Restricted Phenotypes: Exploring Cell-Specific Sensitivities to Impaired tRNA Charging

Kuo, M. E., & Antonellis, A. (2019) · Trends in Genetics · doi:10.1016/j.tig.2019.11.007 · Cited by 39 · see citing works

Comprehensive Cross-Linking Mass Spectrometry Reveals Parallel Orientation and Flexible Conformations of Plant HOP2–MND1

Rampler, E., Stranzl, T., Orbán-Németh, Z., Hollenstein, D. M., Hudecz, O., Schlögelhofer, P., & Mechtler, K. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00903 · Cited by 37 · see citing works

Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions

Herman, S., Khoonsari, P. E., Aftab, O., Krishnan, S., Strömbom, E., Larsson, R. L., Hammerling, U., Spjuth, O., Kultima, K., & Gustafsson, M. G. (2017) · Metabolomics · doi:10.1007/s11306-017-1213-z · Cited by 35 · see citing works

Proteomic Analysis Demonstrates a Molecular Dialog Between Trichoderma guizhouense NJAU 4742 and Cucumber (Cucumis sativus L.) Roots: Role in Promoting Plant Growth

Liu, Q., Tang, S., Meng, X., Zhu, H., Zhu, Y., Liu, D., & Shen, Q. (2021) · Molecular Plant-Microbe Interactions · doi:10.1094/mpmi-08-20-0240-r · Cited by 34 · see citing works

Workflows for automated downstream data analysis and visualization in large‐scale computational mass spectrometry

Aiche, S., Sachsenberg, T. T., Kenar, E., Walzer, M., Wiswedel, B., Kristl, T., Boyles, M. S. P., Duschl, A., Huber, C. G., Berthold, M. R., et al. (2015) · PROTEOMICS · doi:10.1002/pmic.201400391 · Cited by 33 · see citing works

Comprehensive Metaproteomic Analyses of Urine in the Presence and Absence of Neutrophil-Associated Inflammation in the Urinary Tract

Yu, Y., Sikorski, P. M., Smith, M., Bowman-Gholston, C., Cacciabeve, N., Nelson, K. E., & Pieper, R. (2016) · Theranostics · doi:10.7150/thno.16086 · Cited by 33 · see citing works

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Kontou, E. E., Walter, A., Alka, O., Pfeuffer, J., Sachsenberg, T. T., Mohite, O. S., Nuhamunada, M., Kohlbacher, O., & Weber, T. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00724-w · Cited by 32 · see citing works

Increased Levels of Extracellular Microvesicle Markers and Decreased Levels of Endocytic/Exocytic Proteins in the Alzheimer’s Disease Brain

Musunuri, S., Khoonsari, P. E., Mikus, M. S., Wetterhall, M., Månberg, A., Lannfelt, L., Erlandsson, A., Bergquist, J., Ingelsson, M., Shevchenko, G., et al. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160271 · Cited by 32 · see citing works

Proteomic investigation of the secretome of Cellvibrio japonicus during growth on chitin

Tuveng, T. R., Arntzen, M. Ø., Bengtsson, O., Gardner, J. G., Vaaje‐Kolstad, G., & Eijsink, V. G. H. (2016) · PROTEOMICS · doi:10.1002/pmic.201500419 · Cited by 30 · see citing works

Aggregatibacter actinomycetemcomitans H-NS promotes biofilm formation and alters protein dynamics of other species within a polymicrobial oral biofilm

Bao, K., Bostancı, N., Thurnheer, T., Grossmann, J., Wolski, W., Thay, B., Belibasakis, G. N., & Oscarsson, J. (2018) · npj Biofilms and Microbiomes · doi:10.1038/s41522-018-0055-4 · Cited by 30 · see citing works

LFQProfiler and RNPxl: Open-Source Tools for Label-Free Quantification and Protein–RNA Cross-Linking Integrated into Proteome Discoverer

Veit, J., Sachsenberg, T. T., Chernev, A., Aicheler, F., Urlaub, H., & Kohlbacher, O. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00407 · Cited by 28 · see citing works

Systematic analysis of the cerebrospinal fluid proteome of fibromyalgia patients

Khoonsari, P. E., Musunri, S., Herman, S., Svensson, C. I., Tanum, L., Gordh, T., & Kultima, K. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.04.014 · Cited by 28 · see citing works

Systematic evaluation of data‐independent acquisition for sensitive and reproducible proteomics—a prototype design for a single injection assay

Heaven, M. R., Funk, A. J., Cobbs, A. L., Haffey, W. D., Norris, J. L., McCullumsmith, R. E., & Greis, K. D. (2015) · Journal of Mass Spectrometry · doi:10.1002/jms.3716 · Cited by 26 · see citing works

The Cannabis Proteome Draft Map Project

Jenkins, C., & Orsburn, B. C. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21030965 · Cited by 26 · see citing works

Investigation of the anti-cancer potential of epoxyazadiradione in neuroblastoma: experimental assays and molecular analysis

Chandel, S., Bhattacharya, A., Gautam, A., Zeng, W., Alka, O., Sachsenberg, T. T., Gupta, G. D., Narang, R. K., Ravichandiran, V., & Singh, R. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2262593 · Cited by 22 · see citing works

Parallel deep transcriptome and proteome analysis of zebrafish larvae

Palmblad, M., Henkel, C. V., Dirks, R. P., Meijer, A. H., Deelder, A. M., & Spaink, H. P. (2013) · BMC Research Notes · doi:10.1186/1756-0500-6-428 · Cited by 20 · see citing works

Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine

Barbieri, R., Guryev, V., Brandsma, C., Suits, F., Bischoff, R., & Horvatovich, P. L. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-42316-6_3 · Cited by 20 · see citing works

Development of a highly automated and multiplexed targeted proteome pipeline and assay for 112 rat brain synaptic proteins

Colangelo, C. M., Ivosev, G., Chung, L. M., Abbott, T. B., Shifman, M. A., Sakaue, F., Cox, D., Kitchen, R. R., Burton, L., Tate, S. A., et al. (2014) · PROTEOMICS · doi:10.1002/pmic.201400353 · Cited by 19 · see citing works

Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins

Koopmans, F., Cornelisse, L. N., Heskes, T., & Dijkstra, T. M. H. (2014) · Journal of Proteome Research · doi:10.1021/pr500171u · Cited by 19 · see citing works

Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

McCain, J. S. P., Allen, A. E., & Bertrand, E. M. (2021) · The ISME Journal · doi:10.1038/s41396-021-01084-9 · Cited by 19 · see citing works

Platforms and Pipelines for Proteomics Data Analysis and Management

Codrea, M. C., & Nahnsen, S. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-41448-5_9 · Cited by 19 · see citing works

DDASSQ: An open‐source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform

Svecla, M., Garrone, G., Farè, F., Aletti, G., Norata, G. D., & Beretta, G. (2021) · PROTEOMICS · doi:10.1002/pmic.202000319 · Cited by 18 · see citing works

Quantitative label-free redox proteomics of reversible cysteine oxidation in red blood cell membranes

Zaccarin, M., Falda, M., Roveri, A., Bosello-Travain, V., Bordin, L., Maiorino, M., Ursini, F., & Toppo, S. (2014) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2014.03.013 · Cited by 18 · see citing works

Food fingerprinting: Mass spectrometric determination of the cocoa shell content (Theobroma cacao L.) in cocoa products by HPLC-QTOF-MS

Cain, N., Alka, O., Segelke, T., von Wuthenau, K., Kohlbacher, O., & Fischer, M. J. (2019) · Food Chemistry · doi:10.1016/j.foodchem.2019.125013 · Cited by 18 · see citing works

Benchmarking accuracy and precision of intensity‐based absolute quantification of protein abundances in <i>Saccharomyces cerevisiae</i>

Sánchez, B. J., Lahtvee, P., Campbell, K., Kasvandik, S., Yu, R., Domenzain, I., Zelezniak, A., & Nielsen, J. (2021) · PROTEOMICS · doi:10.1002/pmic.202000093 · Cited by 18 · see citing works

Label-free relative quantification applied to LC-MALDI acquisition for rapid analysis of chondrocyte secretion modulation

Riffault, M., Moulin, D., Grossin, L., Mainard, D., Magdalou, J., & Vincourt, J. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2014.10.026 · Cited by 17 · see citing works

Saliva-Induced Clotting Captures Streptococci: Novel Roles for Coagulation and Fibrinolysis in Host Defense and Immune Evasion

Waldetoft, K. W., Mohanty, T., Karlsson, C. A. Q., Mörgelin, M., Frick, I.-M., Malmström, J. A., & Björck, L. (2016) · Infection and Immunity · doi:10.1128/iai.00307-16 · Cited by 17 · see citing works

Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid

Peters, K., Herman, S., Khoonsari, P. E., Burman, J., Neumann, S., & Kultima, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-97491-1 · Cited by 16 · see citing works

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

Fu, J., Luo, Y., Mou, M., Zhang, H., Tang, J., Wang, Y., & Zhu, F. (2019) · Current Drug Targets · doi:10.2174/1389450120666190821160207 · Cited by 15 · see citing works

From the desktop to the grid: scalable bioinformatics via workflow conversion

de la Garza, L., Veit, J., Szolek, A., Röttig, M., Aiche, S., Gesing, S., Reinert, K., & Kohlbacher, O. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0978-9 · Cited by 15 · see citing works

Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups

Pais, R. J. (2022) · BioTech · doi:10.3390/biotech11030035 · Cited by 15 · see citing works

Flexible Data Analysis Pipeline for High-Confidence Proteogenomics

Weisser, H., Wright, J. C., Mudge, J. M., Gutenbrunner, P., & Choudhary, J. S. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00765 · Cited by 15 · see citing works

Computational Methods in Mass Spectrometry-Based Proteomics

Li, S., & Tang, H. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-981-10-1503-8_4 · Cited by 14 · see citing works

Curious Containers: A framework for computational reproducibility in life sciences with support for Deep Learning applications

Jansen, C., Annuscheit, J., Schilling, B., Strohmenger, K., Witt, M., Bartusch, F., Herta, C., Hufnagl, P., & Krefting, D. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.05.007 · Cited by 14 · see citing works

Toward improved peptide feature detection in quantitative proteomics using stable isotope labeling

Nilse, L., Sigloch, F. C., Biniossek, M. L., & Schilling, O. (2015) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201400173 · Cited by 13 · see citing works

DIA-based proteome reveals the involvement of cuticular proteins and lipids in the wing structure construction in the silkworm

Fang, C., Ye, Z., Gai, T., Lu, K., Dai, F., Lu, C., & Tong, X. (2021) · Journal of Proteomics · doi:10.1016/j.jprot.2021.104155 · Cited by 13 · see citing works

Exploring skyline for both MS E ‐based label‐free proteomics and HRMS quantitation of small molecules

Liu, S., Chen, X., Yan, Z., Qin, S., Xu, J., Lin, J., Yang, C., & Shui, W. (2013) · PROTEOMICS · doi:10.1002/pmic.201300352 · Cited by 13 · see citing works

Modifications to the composition of the hyphal outer layer of Aspergillus fumigatus modulates HUVEC proteins related to inflammatory and stress responses

Neves, G. W. P., Curty, N., Kubitschek-Barreira, P. H., Fontaine, T., Souza, G. H. M. F., Cunha, M. L., Goldman, G. H., Beauvais, A., Latgé, J., & Lopes-Bezerra, L. M. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.06.015 · Cited by 13 · see citing works

A Dual Workflow to Improve the Proteomic Coverage in Plasma Using Data-Independent Acquisition-MS

Zhang, S., Raedschelders, K., Venkatraman, V., Huang, L., Holewinski, R. J., Fu, Q., & Eyk, J. E. V. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00607 · Cited by 13 · see citing works

Proteomic comparison of near-isogenic barley (Hordeum vulgare L.) germplasm differing in the allelic state of a major senescence QTL identifies numerous proteins involved in plant pathogen defense

Mason, K. E., Hilmer, J. K., Maaty, W. S. A., Reeves, B. D., Grieco, P. A., Bothner, B. B., & Fischer, A. M. (2016) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2016.09.008 · Cited by 12 · see citing works

Phytoplankton antioxidant systems and their contributions to cellular elemental stoichiometry

McCain, J. S. P., & Bertrand, E. M. (2021) · Limnology and Oceanography Letters · doi:10.1002/lol2.10233 · Cited by 12 · see citing works

A Universal Spectrum Annotator for Complex Peptidoforms in Mass Spectrometry-Based Proteomics

Schulte, D., Leuvenink, R. W., Jager, S., Heck, A. J. R., & Snijder, J. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c02832 · Cited by 12 · see citing works

Metabolomics Data Processing Using OpenMS

Rurik, M., Alka, O., Aicheler, F., & Kohlbacher, O. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0239-3_4 · Cited by 12 · see citing works

Discovery and Longitudinal Evaluation of Candidate Protein Biomarkers for Disease Recurrence in Prostate Cancer

Tonry, C. L., Doherty, D., O'Shea, C., Morrissey, B., Staunton, L., Flatley, B., Shannon, A. M., Armstrong, J., & Pennington, S. R. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00041 · Cited by 12 · see citing works

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Sadia, M., Boudguiyer, Y., Helmus, R., Seijo, M., Praetorius, A., & Samanipour, S. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05425-3 · Cited by 11 · see citing works

One Sample, One Shot - Evaluation of sample preparation protocols for the mass spectrometric proteome analysis of human bile fluid without extensive fractionation

Megger, D. A., Padden, J., Rosowski, K., Uszkoreit, J., Bracht, T., Eisenacher, M., Gerges, C., Neuhaus, H., Schumacher, B. M., Schlaak, J., et al. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.11.021 · Cited by 11 · see citing works

Pipelines and Systems for Threshold-Avoiding Quantification of LC–MS/MS Data

Brotons, A. S., Eriksson, J. O., Kwiatkowski, M., Wolters, J. C., Kema, I. P., Barcaru, A., Kuipers, F., Bakker, S. J. L., Bischoff, R., Suits, F., et al. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c01892 · Cited by 11 · see citing works

An Accelerated Background Subtraction Algorithm for Processing High-Resolution MS Data and its Application to Metabolite Identification

Shekar, V., Shah, A., Shadid, M., Wu, J., Bolleddula, J., & Chowdhury, S. K. (2016) · Bioanalysis · doi:10.4155/bio-2016-0101 · Cited by 11 · see citing works

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

Matlock, A. D., Vaibhav, V., Holewinski, R. J., Venkatraman, V., Dardov, V. J., Manalo, D.-M., Shelley, B., Ornelas, L., Bañuelos, M. G., Mandefro, B., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01687-7 · Cited by 11 · see citing works

Gene and protein expression in human megakaryocytes derived from induced pluripotent stem cells

Kammers, K., Taub, M. A., Mathias, R. A., Yanek, L. R., Kanchan, K., Venkatraman, V., Sundararaman, N., Martin, J., Liu, S., Hoyle, D., et al. (2021) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.15334 · Cited by 10 · see citing works

Using synthetic peptides to benchmark peptide identification software and search parameters for MS/MS data analysis

Quandt, A., Pernas, L. E., Balaskó, Á., Weisser, H., Brusniak, M., Kunszt, P., Aebersold, R. H., & Malmström, L. (2014) · EuPA Open Proteomics · doi:10.1016/j.euprot.2014.10.001 · Cited by 10 · see citing works

Redox status in a model of cancer stem cells

Zaccarin, M., Bosello-Travain, V., Paolo, M. L. D., Falda, M., Maiorino, M., Miotto, G., Piccolo, S., Roveri, A., Ursini, F., Venerando, R., et al. (2016) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2016.09.002 · Cited by 10 · see citing works

SMITER—A Python Library for the Simulation of LC-MS/MS Experiments

Kösters, M., Leufken, J., & Leidel, S. A. (2021) · Genes · doi:10.3390/genes12030396 · Cited by 10 · see citing works

Quantitative Proteomics Reveals Transforming Growth Factor β Receptor Targeted by Resveratrol and Hesperetin Coformulation in Endothelial Cells

Mestareehi, A., Li, H., Zhang, X., Venkata, S. P. M., Jaiswal, R., Yu, F. X., Yi, Z., & Wang, J. (2023) · ACS Omega · doi:10.1021/acsomega.3c00678 · Cited by 10 · see citing works

Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data

Jenkins, C., & Orsburn, B. C. (2023) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.3c00238 · Cited by 10 · see citing works

A mass spectrometry-based proteomic analysis of Homer2-interacting proteins in the mouse brain

Goulding, S. P., Szumlinski, K. K., Contet, C., MacCoss, M. J., & Wu, C. C. (2017) · Journal of Proteomics · doi:10.1016/j.jprot.2017.07.008 · Cited by 10 · see citing works

Computational methods and opportunities for phosphorylation network medicine.

Chen, Y. A., & Eschrich, S. A. (2014) · PubMed · Cited by 10 · see citing works

DDASSQ: An open‐source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform

Svecla, M., Garrone, G., Farè, F., Aletti, G., Norata, G. D., & Beretta, G. (2021) · PROTEOMICS · doi:10.1002/pmic.202000319 · Cited by 18 · see citing works

Limitation of algorithms for quantitative label-free LC-MS-based proteomics

Noughani, A. E. (2012)

LFQ uant: A label‐free fast quantitative analysis tool for high‐resolution LC ‐ MS / MS proteomics data

Zhang, W., Zhang, J., Xu, C., Li, N., Liu, H., Ma, J., Zhu, Y., & Xie, H. (2012) · PROTEOMICS · doi:10.1002/pmic.201200017 · Cited by 45 · see citing works

MassChroQ: A versatile tool for mass spectrometry quantification

Valot, B., Langella, O., Nano, E., & Zivy, M. (2011) · PROTEOMICS · doi:10.1002/pmic.201100120 · Cited by 332 · see citing works

Quality Control for high-throughput Quantitative Proteomics - Harnessing the potential of label-free LC-MS

Sandin, M. (2014) · Lund University Publications (Lund University) · Cited by 1 · see citing works