Joshua J. Coon

33.1k total citations · 5 hit papers
359 papers, 22.0k citations indexed

About

Joshua J. Coon is a scholar working on Molecular Biology, Spectroscopy and Biomedical Engineering. According to data from OpenAlex, Joshua J. Coon has authored 359 papers receiving a total of 22.0k indexed citations (citations by other indexed papers that have themselves been cited), including 234 papers in Molecular Biology, 171 papers in Spectroscopy and 49 papers in Biomedical Engineering. Recurrent topics in Joshua J. Coon's work include Advanced Proteomics Techniques and Applications (151 papers), Mass Spectrometry Techniques and Applications (145 papers) and Metabolomics and Mass Spectrometry Studies (69 papers). Joshua J. Coon is often cited by papers focused on Advanced Proteomics Techniques and Applications (151 papers), Mass Spectrometry Techniques and Applications (145 papers) and Metabolomics and Mass Spectrometry Studies (69 papers). Joshua J. Coon collaborates with scholars based in United States, Germany and Switzerland. Joshua J. Coon's co-authors include Michael S. Westphall, John E. P. Syka, Donald F. Hunt, Jeffrey Shabanowitz, Alexander S. Hebert, Derek J. Bailey, Melanie Schroeder, Danielle L. Swaney, Craig D. Wenger and Graeme C. McAlister and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Joshua J. Coon

347 papers receiving 21.7k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry

2004 1.9k citations John E. P. Syka, Joshua J. Coon et al. Proceedings of the National Academy of Sciences profile →
Formic-acid-induced depolymerization of oxidized lignin to aromatics

2014 983 citations Joshua J. Coon et al. profile →
Parallel Reaction Monitoring for High Resolution and High Mass Accuracy Quantitative, Targeted Proteomics

2012 930 citations Amelia C. Peterson, Jason D. Russell et al. Molecular & Cellular Proteomics profile →
Calorie Restriction and SIRT3 Trigger Global Reprogramming of the Mitochondrial Protein Acetylome

2012 527 citations Alexander S. Hebert, Derek J. Bailey et al. profile →
The One Hour Yeast Proteome

2013 424 citations Alexander S. Hebert, Derek J. Bailey et al. Molecular & Cellular Proteomics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Joshua J. Coon United States	73	13.6k	10.3k	2.4k	1.2k	1.2k	359	22.0k
Michael Karas Germany	80	12.8k 0.9×	15.9k 1.5×	2.3k 0.9×	1.4k 1.2×	1.2k 1.0×	320	28.3k
Joseph A. Loo United States	82	11.8k 0.9×	10.7k 1.0×	2.7k 1.1×	2.2k 1.8×	1.4k 1.2×	346	23.8k
Yasushi Ishihama Japan	61	13.7k 1.0×	6.2k 0.6×	2.5k 1.0×	911 0.7×	1.9k 1.6×	253	21.5k
Bernhard Küster Germany	74	15.0k 1.1×	6.8k 0.7×	734 0.3×	1.5k 1.2×	1.4k 1.2×	280	20.2k
Lloyd M. Smith United States	64	10.8k 0.8×	3.7k 0.4×	3.3k 1.4×	1.3k 1.0×	827 0.7×	349	21.8k
Ole N. Jensen Denmark	86	19.3k 1.4×	9.9k 1.0×	968 0.4×	1.0k 0.9×	2.5k 2.0×	356	27.4k
Denis F. Hochstrasser Switzerland	65	14.3k 1.1×	6.3k 0.6×	1.2k 0.5×	1.2k 1.0×	1.4k 1.1×	231	21.9k
David R. Goodlett United States	76	15.3k 1.1×	6.3k 0.6×	1.3k 0.5×	1.3k 1.1×	1.2k 1.0×	311	26.7k
Michael J. MacCoss United States	77	17.4k 1.3×	9.3k 0.9×	766 0.3×	1.6k 1.3×	1.9k 1.6×	288	25.0k
Christoph H. Borchers Canada	64	11.0k 0.8×	5.3k 0.5×	740 0.3×	1.1k 0.9×	1.1k 0.9×	372	16.6k

Countries citing papers authored by Joshua J. Coon

Since Specialization

Citations

This map shows the geographic impact of Joshua J. Coon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joshua J. Coon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua J. Coon more than expected).

Fields of papers citing papers by Joshua J. Coon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joshua J. Coon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joshua J. Coon. The network helps show where Joshua J. Coon may publish in the future.

Co-authorship network of co-authors of Joshua J. Coon

This figure shows the co-authorship network connecting the top 25 collaborators of Joshua J. Coon. A scholar is included among the top collaborators of Joshua J. Coon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joshua J. Coon. Joshua J. Coon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kraus, Felix, Yuchen He, Sharan Swarup, et al.. (2025). Global cellular proteo-lipidomic profiling of diverse lysosomal storage disease mutants using nMOST. Science Advances. 11(4). eadu5787–eadu5787. 1 indexed citations

Xie, Dan, José Serate, Katherine A. Overmyer, et al.. (2025). pH adjustment increases biofuel production from inhibitory switchgrass hydrolysates. Bioresource Technology. 432. 132651–132651. 1 indexed citations

Forbes, David V., et al.. (2025). Laser-Induced Rehydration of Cryo-Landed Proteins Restores Native Structure. Molecular & Cellular Proteomics. 24(6). 100987–100987.

Thomas, Aaron C. Q., Gary M. Wilson, Chris McGlory, et al.. (2025). Identification of a resistance-exercise-specific signalling pathway that drives skeletal muscle growth. Nature Metabolism. 7(7). 1404–1423. 9 indexed citations

Peters-Clarke, Trenton M., et al.. (2024). Phosphorothioate RNA Analysis by NETD Tandem Mass Spectrometry. Molecular & Cellular Proteomics. 23(4). 100742–100742. 5 indexed citations

Brademan, Dain R., et al.. (2024). LipiDex 2 Integrates MSⁿ Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics. Analytical Chemistry. 96(17). 6715–6723. 6 indexed citations

Serrano, Lia R., Trenton M. Peters-Clarke, Tabiwang N. Arrey, et al.. (2024). The One Hour Human Proteome. Molecular & Cellular Proteomics. 23(5). 100760–100760. 23 indexed citations

Ma, Mengxiao, Ramin Dubey, Ganesh V. Pusapati, et al.. (2024). Regulated N-glycosylation controls chaperone function and receptor trafficking. Science. 386(6722). 667–672. 10 indexed citations

Overmyer, Katherine A., et al.. (2023). PKA regulatory subunit Bcy1 couples growth, lipid metabolism, and fermentation during anaerobic xylose growth in Saccharomyces cerevisiae. PLoS Genetics. 19(7). e1010593–e1010593. 4 indexed citations

10.

Curtis, Annie M., James A. Thomson, Dennis Clegg, et al.. (2023). Plasma metabolomics supports non-fasted sampling for metabolic profiling across a spectrum of glucose tolerance in the Nile rat model for type 2 diabetes. Lab Animal. 52(11). 269–277. 4 indexed citations

11.

Johansson, Mats W., Joseph Balnis, Laura K. Muehlbauer, et al.. (2023). Decreased plasma cartilage acidic protein 1 in COVID‐19. Physiological Reports. 11(17). e15814–e15814. 1 indexed citations

12.

Wen, Zhi, Xin Gao, Lin Li, et al.. (2022). Tcof1 haploinsufficiency promotes early T cell precursor-like leukemia in NrasQ61R/+ mice. Leukemia. 36(4). 1167–1170. 1 indexed citations

13.

Balnis, Joseph, Alejandro P. Adam, Amit Chopra, et al.. (2021). Unique inflammatory profile is associated with higher SARS-CoV-2 acute respiratory distress syndrome (ARDS) mortality. Publications of the Astronomical Society of Japan. 320(3). R250–R257. 24 indexed citations

14.

Leung, Kevin, Gary M. Wilson, Lisa L. Kirkemo, et al.. (2020). Broad and thematic remodeling of the surfaceome and glycoproteome on isogenic cells transformed with driving proliferative oncogenes. Proceedings of the National Academy of Sciences. 117(14). 7764–7775. 59 indexed citations

15.

Linke, Vanessa, Kelsey L. Barrett, Frederick J. Boehm, et al.. (2019). Genetic determinants of gut microbiota composition and bile acid profiles in mice. PLoS Genetics. 15(8). e1008073–e1008073. 81 indexed citations

16.

Potts, Gregory K., Rachel M. McNally, Rocky Blanco, et al.. (2017). A map of the phosphoproteomic alterations that occur after a bout of maximal‐intensity contractions. The Journal of Physiology. 595(15). 5209–5226. 64 indexed citations

17.

Tagliabracci, Vincent S., Sandra E. Wiley, Xiao Guo, et al.. (2015). A Single Kinase Generates the Majority of the Secreted Phosphoproteome. Cell. 161(7). 1619–1632. 247 indexed citations

18.

Honarpour, Narimon, Christopher M. Rose, Justin Brumbaugh, et al.. (2014). F-box Protein FBXL16 Binds PP2A-B55α and Regulates Differentiation of Embryonic Stem Cells along the FLK1+ Lineage. Molecular & Cellular Proteomics. 13(3). 780–791. 23 indexed citations

19.

Peterson, Amelia C., Jason D. Russell, Derek J. Bailey, Michael S. Westphall, & Joshua J. Coon. (2012). Parallel Reaction Monitoring for High Resolution and High Mass Accuracy Quantitative, Targeted Proteomics. Molecular & Cellular Proteomics. 11(11). 1475–1488. 930 indexed citations breakdown →

20.

Chi, An, Curtis Huttenhower, Lewis Y. Geer, et al.. (2007). Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry. Proceedings of the National Academy of Sciences. 104(7). 2193–2198. 460 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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