Fengchao Yu

University of Michigan, Ann Arbor, Michigan, USA

I am a research assistant professor from Alexey Nesvizhskii's lab at University of Michigan. My research interests include Proteomics, Bioinformatics, computational biology, machine learning, and statistics. I am the leading developer and maintainer of FragPipe, MSFragger, IonQuant, MSBooster, and diaTracer.

Research Experience

Department of Pathology, University of Michigan

Research Assistant Professor
September 2025 - present

Department of Pathology, University of Michigan

Research Investigator
May 2021 - August 2025

Department of Pathology, University of Michigan

Research Fellow
January 2019 - April 2021

Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology

Research Associate
June 2017 - December 2018

Division of Biomedical Engineering, The Hong Kong University of Science and Technology

Doctor of Philosophy
September 2012 - June 2017

Department of Optical Engineering, Zhejiang University

Master of Engineering
September 2009 - March 2012

Selected Publications

  1. Chang, H.-Y., Deng, Y., Li, R., Avtonomov, D., Wen, B., Haynes, S., da Veiga Leprevost, F., Zhang, B., Yu, F.*, & Nesvizhskii, A. I.* (2026). Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform. Nature Communications, 17, 4010. (* co-corresponding authors)
  2. Yu, F.*, Yamei Deng, & Nesvizhskii, A. I.* (2025). MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search. Nature Communications, 16, 3329. (* co-corresponding authors)
  3. Li, K., Teo, G. C., Yang, K. L., Yu, F.*, & Nesvizhskii, A. I.* (2024). diaTracer enables spectrum-centric analysis of diaPASEF proteomics data. Nature Communications, 16, 95. (* co-corresponding authors)
  4. Yu, F.*, Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X., Demichev, V., & Nesvizhskii, A. I.*, (2023). Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nature Communications, 14, 4154. (* co-corresponding authors)
  5. Yang, K. L., Yu, F.*, Teo, G. C., Demichev, V., Ralser, M., & Nesvizhskii, A. I.*, (2023). MSBooster: improving peptide identification rates using deep learning-based features. Nature Communications, 14, 4539. (* co-corresponding authors)
  6. Yu, F., Haynes, S., & Nesvizhskii, A. (2021). IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs. Molecular & Cellular Proteomics, 20, 100077.
  7. Yu, F., Teo, G. C., Kong, A., Avtonomov, D., Geiszler, D., & Nesvizhskii, A. (2020). Identification of modified peptides using localization-aware open search. Nature Communications, 11, 4065.
  8. Yu, F.*, Haynes, S.*, Teo, G. C., Avtonomov, D., Polasky, D., & Nesvizhskii, A. (2020). Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Molecular & Cellular Proteomics, 19(9), 1575-1585. (* co-first authors)
  9. Polasky, D., Yu, F., Teo, G. C., & Nesvizhskii, A. (2020). Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nature Methods, 17(11), 1125–1132.

Journal Publications

  1. Chu, S.-C. A., Hsiao, Y., Wang, C., Kyle, J. E., Jain, R., Deng, Y., Gritsenko, M. A., Henry, L. E., Lei, J. T., Dou, Y., Tercan, B., Shi, Z., Gondal, M. N., Tsai, C.-F., Elizarraras, J. M., Chu, R. K., Yu, F., Joshi, S. K., Jing, X., Polasky, D. A., Weitz, K. K., Li, G. X., Paurus, V. L., Clendinen, C. S., Schepmoes, A. A., Lalli, P. M., Eder, J. G., Flores, J. E., Stratton, K. G., Pino, J. C., Posso, C., Petyuk, V. A., Sagendorf, T. J., Xu, Y., Ibrahim, O. M., Moore, R. J., Zhao, R., Chen, J., Monroe, M. E., Thiagarajan, M., Hostetter, G., Newton, C., An, E., Robles, A. I., Zhang, X., Edwards, N. J., Lu, Y., Zhang, H., Abdelhakim, H., Piehowski, P. D., Mesri, M., Smith, R. D., Kumar-Sinha, C., Tognon, C. E., Dunlap, J., Traer, E., Ding, L., Tyner, J. W., Chinnaiyan, A. M., Omenn, G. S., Rodland, K. D., Dhanasekaran, S. M., Gosline, S. J. C., Nesvizhskii, A. I., Zhang, B., Liu, T., Cieslik, M. P., & the Clinical Proteomic Tumor Analysis Consortium (2026). Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets. Nature Cancer, in press
  2. Yue, L., Jiang, W., Li, S., Luo, M., Fan, N., Zhan, X., Sun, R., Cheng, H., Xue, Z., Liu, T., Zhou, Q., Chen, K., Lu, T., Guo, F., Li, D., Ge, W., Nie, Z., Lyu, M., A, J., Wang, Y., Chen, Y., Fu, Z., Xiang, N., Li, L., Yu, F., Teo, G. C., Nesvizhskii, A. I., Wang, M., Snyder, M. P., Collins, B. C., Xiao, Q., Aebersold, R., Xu, F., Yang, H., Zhang, S., Han, Y., Zhu, Y., Ji, Y., Li, Y., & Guo, T. (2026). Spatial distribution of the proteome in human body and cancers. Nature, accepted
  3. Ngo, C., Takechi, S., Sivakumar, A., Villanueva, M., Yu, F., Ball, A. B., Rubio, J., Biletch, E., Burton, N. R., Boatner, L. M., Kim, P., Turmon, A. C., Perumal, N., Liesa, M., Divakaruni, A. S., Nesvizhskii, A. I., & Backus, K. M. (2026). Small-molecule binding-site discovery using silyl ether-enabled chemoproteomics. Nature Chemistry, in press
  4. Yang, Z., Ma, T., Choi, M., Yu, F., & Zhu, Y. (2026). Systematic evaluation of data-independent acquisition workflows for high-throughput and low-input proteomics analysis with an Astral mass spectrometer. Journal of Proteome Research, 25(3). 1686-1699
  5. Yue, Q.-X., Dai, C., Kamatchinathan, S., Bandla, C., Webel, H., Larrea, A., Bittremieux, W., Uszkoreit, J., Müller, T. D., Xiao, J., Cox, J., Yu, F., Ewels, P., Demichev, V., Kohlbacher, O., Sachsenberg, T., Bielow, C., Bai, M., & Perez-Riverol, Y. (2026). pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics. Molecular & Cellular Proteomics, 101530
  6. Mehta, S., Wagner, R., Do, K. T., Johnson, J. E., Yu, F., Jubenville, T., Richards, K., Coleman, S., Popescu, F. E., Nesvizhskii, A. I., Largaespada, D. A., Jagtap, P. D., & Griffin, T. J. (2026). iPepGen: A modular, immunopeptidogenomic analysis pipeline for discovery, verification, and prioritization of cancer peptide neoantigen candidates. Genome Biology, 27, 111
  7. Bathini, M., Bocaniciu, D., Johnson, F. D., de Jong, R. C. P., Yu, F., Aloi, V. D., Kuiken, M. C., Mors, J. R., Giebel, L., Champagne, J., Bleijerveld, O., Agami, R., Dijkstra, K. K., Thommen, D. S., Nesvizhskii, A. I., & Lindeboom, R. G. H. (2026). MHC1-TIP enables single-tube multimodal immunopeptidome profiling and uncovers intratumoral heterogeneity in antigen presentation. Communications Biology, 9, 296
  8. Ramírez, R. C., Yu, F., Polasky, D. A., & Nesvizhskii, A. I. (2026). A new detailed mass offset search in MSFragger for improved interpretation of complex PTMs. Journal of Proteome Research, 25(2), 842-851
  9. Lautenbacher, L., Yang, K. L., Kockmann, T., Panse, C., Gabriel, W., Bold, D., Kahl, E., Chambers, M., MacLean, B. X., Li, K., Yu, F., Searle, B. C., Wilburn, D. B., Shahneh, M. R. Z., Hong, Y., Tang, H., Wang, M., Gabriels, R., Bouwmeester, R., Devreese, R., Angelis, J., Sabidó, E., Schmidt, T. K., Nesvizhskii, A. I., & Wilhelm, M. (2025). Koina: Democratizing machine learning for proteomics research. Nature Communications, 16, 9933
  10. Zanon, P. R. A., Yu, F., Musacchio, P. Z., Lewald, L., Zollo, M., Krauskopf, K., Mrdović, D., Raunft, P., Maher, T. E., Cigler, M., Chang, C. J., Lang, K., Toste, F. D., Nesvizhskii, A. I., & Hacker, S. M. (2025). Profiling the proteome-wide selectivity of diverse electrophiles. Nature Chemistry, 17, 1712-1721
  11. Vai, A., Noberini, R., Graziadei, A., Polasky, D. A., Yu, F., Nesvizhskii, A. I., Bonaldi, T. (2025). Breaking boundaries in Histone modification MS-based detection: A tailored search strategy for unrestricted identification of novel epigenetic marks. Molecular & Cellular Proteomics, 101080
  12. Frankenfield, A. M., Yang, K., Mazli, W. N. A., Shih, J., Yu, F., Lo, E., Nesvizhskii, A. I., & Hao, L. (2025). Benchmarking SILAC proteomics workflows and data analysis platforms. Molecular & Cellular Proteomics, 100980.
  13. Desai, H., Andrews, K. H., Bergersen, K. V., Ofori, S., Yu, F., Shikwana, F., Arbing, M., Boatner, L. M., Villanueva, M., Ung, N., Reed, E. F, Nesvizhskii, A. I., & Backus, K. M. (2024). Chemoproteogenomic stratification of the missense variant cysteinome. Nature Communications, 15, 9284
  14. Kwon, Y., Woo, J., Yu, F., Williams, S. M., Markillie, L. M., Moore, R. J., Nakayasu, E. S., Chen, J., Campbell-Thompson, M., Mathews, C. E., Nesvizhskii, A. I., Qian, W. J., & Zhu Y. (2024). Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. Molecular & Cellular Proteomics, 23(11), 100841
  15. Hsiao, Y., Zhang, H., Li, G. X., Deng, Y., Yu, F., Kahrood, H. V., Steele, J. R., Schittenhelm, R. B., & Nesvizhskii, A. I. (2024). Analysis and visualization of quantitative proteomics data using FragPipe-Analyst. Journal of Proteome Research, 23(10), 4303-4315
  16. Muneer, G., Gebreyesus, S. T., Chen, C.-S., Lee, T.-T., Yu, F., Lin, C.-A., Hsieh, M.-S., Nesvizhskii, A. I., Ho, C.-C., Yu, S.-L., Tu, H.-L., & Chen, Y.-J. (2024). Mapping nanoscale-to-single-cell phosphoproteomic landscape by chip-DIA. Advanced Science, 12, 2402421
  17. Polasky, D. A., Lu, L., Yu, F., Li, K., Shortreed, M. R., Smith, L. M., & Nesvizhskii, A. I. (2025). Quantitative proteome-wide O-glycoproteomics analysis with FragPipe. Analytical and Bioanalytical Chemistry, 417, 921-930
  18. Kohler, D., Staniak, M., Yu, F., Nesvizhskii, A. I., & Vitek, O. (2024). An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing. Nature Protocols, 19, 2915–2938
  19. Ferreira, H. J., Stevenson, B. J., Pak, H. S., Yu, F., Oliveira, J. A., Huber, F., Taillandier-Coindard, M., Michaux, J., Ricart-Altimiras, E., Kraemer, A. I., Kandalaft, L. E., Speiser, D. E., Nesvizhskii, A. I., Müller, M., & Bassani-Sternberg, M. (2024). Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides. Nature Communications, 15, 2357
  20. Mi, Y., Burnham, K. L., Charles, P. D., Heilig, R., Vendre, I., Whalley, J., Torrance, H. D., Antcliffe, D. B., May, S. M., Neville, M. J., Berridge, G., Hutton, P., Geoghegan, C. G., Radhakrishnan, J., Nesvizhskii, A. I., Yu, F., GAinS Investigators, Davenport, E. E., McKechnie, S., Davies, R., O’Callaghan, D. JP, Pate, P., Karpe, F., Gordon, A. C., Ackland, G. L., Hinds, C. J., Fischer, R., & Knight, J. C., (2024). High-throughput mass spectrometry maps the sepsis plasma proteome and differences. Science Translational Medicine, 16(750), eadh0185
  21. Li, G. X., Chen, L., Hsiao, Y., Mannan, R., Zhang, Y., Luo, J., Petralia, F., Cho, H., Hosseini, N., da Veiga Leprevost, F., Calinawan, A., Li, Y., Anand, S., Dagar, A., Geffen, Y., Kumar-Sinha, C., Chugh, S., Le, A., Ponce, S., Guo, S., Zhang, C., Schnaubelt, M., Naser Al Deen, N., Chen, F., Caravan, W., Houston, A., Hopkins, A., Newton, C. J., Wang, X., Polasky, D. A., Haynes, S., Yu, F., Jing, X., Chen, S., Robles, A. I., Mesri, M., Thiagarajan, M., An, E., Getz, G. A., Linehan, W. M., Hostetter, G., Jewell, S. D., Chan, D. W., Wang, P., Omenn, G. S., Mehra, R., Ricketts, C. J., Ding, L., Chinnaiyan, A. M., Cieslik, M. P., Dhanasekaran, S. M., Zhang, H., Nesvizhskii, A. I., & Clinical Proteomic Tumor Analysis Consortium. (2024). Comprehensive proteogenomic characterization of rare kidney tumors. Cell Reports Medicine, 5(5), 101547
  22. Geiszler, D. J., Polasky, D. A., Yu, F., & Nesvizhskii, A. I., (2023). Detecting diagnostic features in MS/MS spectra of post-translationally modified peptides. Nature Communications, 14, 4132.
  23. Bedran, G., Polasky, D. A., Hsiao, Y., Yu, F., da Veiga Leprevost, F., Alfaro, J. A., Cieslik, M., & Nesvizhskii, A. I., (2023). Unraveling the glycosylated immunopeptidome with HLA-Glyco. Nature Communications, 14, 3461.
  24. Polasky, D., Geiszler, D., Yu, F., Li, K., Teo, G. C., & Nesvizhskii, A. I., (2023). MSFragger-Labile: A flexible method to improve labile PTM analysis in proteomics. Molecular & Cellular Proteomics, 22(5), 100538.
  25. Park, J., Yu, F., Fulcher, J.M., Williams, S.M., Engbrecht, K., Moore, R.J., Clair, G.C., Petyuk, V., Nesvizhskii, A.I., & Zhu, Y., (2023). Evaluating linear ion trap for MS3-based multiplexed single-cell proteomics. Analytical Chemistry, 95(3), 1888-1898.
  26. Dai, S., Liu, S., Zhou, C., Yu, F., Zhu, G., Zhang, W., Deng, H., Burlingame, A., Yu, W., Wang, T., & Li, N., (2023). Capturing the hierarchically assorted modules of protein-protein interaction in the organized nucleome. Molecular Plant, 16(5), 930-961.
  27. Chang, H.-Y., Haynes, S. E., Yu, F., & Nesvizhskii, A. I., (2023). Implementing the MSFragger search engine as a node in Proteome Discoverer. Journal of Proteome Research, 22(2), 520-525.
  28. Kacen, A., Javitt, A., Kramer, M., Morgenstern, D., Tsaban, T., Shmueli, M., Teo, G. C., da Veiga Leprevost, F., Barnea, E., Yu, F., Admon, A., Eisenbach, L., Samuels, Y., Schueler-Furman, O., Levin, Y., Nesvizhskii, A. I., & Merbl, Y. (2023). Post-translational modifications reshape the antigenic landscape of MHC I immunopeptidome in tumors. Nature Biotechnology, 41(2), 239-251.
  29. Demichev, V., Szyrwiel, L., Yu, F., Teo, G. C., Rosenberger, G., Niewienda, A., Ludwig, D., Decker, J., Kaspar-Schoenefeld, S., Lilley, K., Mulleder, M., Nesvizhskii, A., & Ralser, M. (2022). dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nature Communications, 13, 3944.
  30. Desai, H., Yan, T., Yu, F., Sun, A., Villanueva, M., Nesvizhskii, A., & Backus, K. (2022). SP3-enabled rapid and high coverage chemoproteomic identification of cell-state dependent redox-sensitive cysteines. Molecular & Cellular Proteomics, 21(4), 100218.
  31. Polasky, D., Geiszler, D., Yu, F. & Nesvizhskii, A. (2022). Multiattribute glycan identification and FDR control for glycoproteomics. Molecular & Cellular Proteomics, 21(3), 100205.
  32. Moradi, A., Dai, S., Wong, E.O.Y., Zhu, G., Yu, F., Lam, H.M., Wang, Z., Burlingame, A., Lin, C., Afsharifar, A. & Yu, W. (2021). Isotopically dimethyl labeling-based quantitative proteomic analysis of phosphoproteomes of soybean cultivars. Biomolecules, 11(8), 1218.
  33. Petyuk, V.A., Yu, L., Olson, H.M., Yu, F., Clair, G., Qian, W.J., Shulman, J.M. & Bennett, D.A. (2021). Proteomic profiling of the substantia nigra to identify determinants of lewy body pathology and dopaminergic neuronal loss. Journal of Proteome Research, 20(5), 2266-2282.
  34. Geiszler, D., Kong, A., Avtonomov, D., Yu, F., da Veiga Leprevost, F., & Nesvizhskii, A. (2021). PTM-Shepherd: analysis and summarization of post-translational and chemical modifications from open search results. Molecular & Cellular Proteomics, 20, 100018.
  35. Dai, J.*, Yu, F.*, Chen Zhou, & Yu, W. (2021). Understanding the limit of open search in the identification of peptides with post-translational modifications — A simulation-based study. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2884-2890. (* co-first authors)
  36. Teo, G. C., Polasky, D., Yu, F., & Nesvizhskii, A. (2021). Fast deisotoping algorithm and its implementation in the MSFragger search engine. Journal of Proteome Research, 20(1), 498-505.
  37. Dai, J.*, Jiang, W.*, Yu, F.*, & Yu, W. (2019). Xolik: finding cross-linked peptides with maximum paired scores in linear time. Bioinformatics, 35(2), 251-257. (* co-first authors)
  38. Liu, S.*, Yu, F.*, Hu, Q., Wang, T., Yu, L., Du, S., Yu, W., & Li, N. (2018). Development of in planta chemical cross-linking-based quantitative interactomics in Arabidopsis. Journal of Proteome Research, 17(9), 3195-3213. (* co-first authors)
  39. Liu, S.*, Yu, F.*, Yang, Z., Wang, T., Xiong, H., Chang, C., Yu, W., & Li, N. (2018). Establishment of dimethyl labeling-based quantitative acetylproteomics in Arabidopsis. Molecular & Cellular Proteomics, 17(5), 1010-1027. (* co-first authors)
  40. Yu, F., Li, N., & Yu, W. (2017). Exhaustively identifying cross-linked peptides with a linear computational complexity. Journal of Proteome Research, 16(10), 3942-3952.
  41. Yu, F., Li, N., & Yu, W. (2016). PIPI: PTM-invariant peptide identification using coding method. Journal of Proteome Research, 15(12), 4423-4435.
  42. Yu, F., Li, N., & Yu, W. (2016). ECL: an exhaustive search tool for the identification of cross-linked peptides using whole database. BMC Boinformatics, 17(1), 217.
  43. Zhu, X.*, Yu, F.*, Yang, Z., Liu, S., Dai, C., Lu, X., Liu, C., Yu, W., & Li, N. (2016). In planta chemical cross‐linking and mass spectrometry analysis of protein structure and interaction in Arabidopsis. Proteomics, 16(13), 1915-1927. (* co-first authors)
  44. Yu, F., Liu, H., Hu, Z., & Shi, P. (2012). Graphics processing unit (GPU)-accelerated particle filter framework for positron emission tomography image reconstruction. JOSA A, 29(4), 637-643.

Invited Oral Presentations

  1. Yu, F. (August 22, 2025). Integrated spectrum-centric DIA analysis in the FragPipe platform. The 8th China Workshop on Computational Proteomics (CNCP 2025). Hangzhou, China.
  2. Yu, F. (August 17, 2025). Spectrum-centric and indexing approaches for DIA proteomics in FragPipe. The 15th International Symposium on Proteomics in the Life Sciences. Cambridge, Massachusetts.
  3. Yu, F. (November 01, 2024). FragPipe: Progressing toward a unified one-stop proteomics data analysis solution. 1st CASMS In-Person Conference 2024 (CASMS 2024). King of Prussia, Pennsylvania.
  4. Yu, F. (August 30, 2023). FragPipe enables the one-stop analysis for DDA and DIA bottom-up proteomics. The 7th China Workshop on Computational Proteomics (CNCP 2023). Beijing, China.
  5. Yu, F. (August 24, 2022). FragPipe enables one-stop proteomics data analysis. Cancer Genomics Cloud Webinar.



Workshops

  1. Yu, F. (April 29, 2026). Quantitative proteomics with FragPipe. May Institute. Boston, Massachusetts.
  2. Yu, F. (September 9, 2025). Mass Spec data analysis: An overview and FragPipe set up. Biological Proteomics for Beginners - Workshop @UCSD. San Diego, California.
  3. Yu, F. & Dainel Polasky (June 3, 2025). Proteomics data analysis: From identification to quantification. ASMS 2025, evening workshops. Baltimore, Maryland.
  4. Yu, F. (July 12, 2024). Mass spec data analysis: An overview. Biological Proteomics for Beginners: 2-Day Workshop @UCSF Mission Bay. San Francisco, California.
  5. Kulej, K. amp; Yu, F. (June 5, 2024). Integrative proteogenomic approach for personalized protein mapping: prospects, challenges, and scalability bottlenecks. ASMS 2024, evening workshops. Anaheim, California.



Conference Presentations

  1. Yu, F., Yang, K. L., Polasky, D. A., & Nesvizhskii, A. I. (February 21-25, 2026). Data-independent acquisition data analysis with unlimited post-translational modifications detection and transfer learning. 22nd Annual US HUPO Conference (US HUPO 2026). St. Louis, Missouri. (poster presentation).
  2. Yu, F., Kong, A., Hsiao, Y., & Nesvizhskii, A. I. (November 9-13, 2025). PepCentric: rapid proteogenomic searches and validation of novel peptides. 24th Human Proteome Organization World Congress (HUPO 2025). Toronto, Canada. (oral presentation).
  3. Yu, F., Kong, A., Hsiao, Y., & Nesvizhskii, A. I. (June 1-5, 2025). PepCentric enables high-speed repository-scale peptide searching with robust FDR control. 73rd ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2025). Baltimore, Maryland. (poster presentation).
  4. Yu, F., Kong, A., Hsiao, Y., & Nesvizhskii, A. I. (February 22-26, 2025). PepCentric fast-tracks peptide searching and validation with repository-scale mass spectrometry data. 21th Annual US HUPO Conference (US HUPO 2025). Philadelphia, Pennsylvania. (oral presentation).
  5. Yu, F., Li, K., Deng, Y., Hsiao, Y., Polasky, D.A., Shen, J., Yang, K., Teo, G.C., & Nesvizhskii, A. I. (Februry 22-26, 2025). New developments in FragPipe computational platform. 21th Annual US HUPO Conference (US HUPO 2025). Philadelphia, Pennsylvania. (poster presentation).
  6. Yu, F., Yang, K. L., Jagtap, P., Wagner, R., Griffin, T. J., & Nesvizhskii, A. I. (September 16-19, 2024). Enhanced MSBooster for sensitive HLA peptide identification. NIH/NCI Informatics Technology for Cancer Research (ITCR) Annual Meeting 2024 (ITCR 2024). Indianapolis, Indiana. (poster presentation).
  7. Yu, F., Deng, Y., Polasky, D. A., Yang, K. L., Li, K., Teo, G. C., & Nesvizhskii, A. I. (June 2-6, 2024). FragPipe advancements for optimized DDA, DDA+, and DIA mass spectrometry data analysis. 72nd ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2024). Anaheim, California. (poster presentation).
  8. Yu, F. (June 2, 2024). Integration of Skyline into FragPipe for Streamlined Visualization. 2024 User Group Meeting at ASMS. Anaheim, California. (oral presentation).
  9. Yu, F., Yang, K. L., Teo, G. C., Li, K., & Nesvizhskii, A. I. (March 9-13, 2024). The development of MSFragger-DDA+ and MSFragger-DIA for multiplexed DDA and DIA data analysis. 20th Annual US HUPO Conference (US HUPO 2024). Portland, Oregon. (oral presentation).
  10. Yu, F., Polasky, D. A., Yang, K. L., Deng, Y., Teo, G. C., & Nesvizhskii, A. I. (October 13-16, 2023). FragPipe enhancements: Integrating MSFragger-DIA and MSFragger-WWA for advanced single-cell and bulk-cell proteomics analysis. 38th Asilomar Conference on Mass Spectrometry. Pacific Grove, California (poster presentation).
  11. Yu, F., Truong, T., Kelly, R., & Nesvizhskii, A. I. (September 17-21, 2023). MSFragger-WWA coupled with FragPipe enables fast and easy wide-window acquisition data analysis. 22th Human Proteome Organization World Congress (HUPO 2023). Busan, South Korea. (poster presentation).
  12. Yu, F., Polasky, D. A., Yang, K. L., Kong, A. T., & Nesvizhskii, A. I. (August 28-31, 2023). MSFragger-DIA and MSFragger-WWA facilitate fast and easy single-cell proteomics data analysis. 3rd CASMS Virtual Conference. Virtual. (poster presentation, best poster award).
  13. Yu, F., Polasky, D. A., Kong, A. T., Teo, G. C., Yang, K. L., & Nesvizhskii, A. I. (June 4-8, 2023). Updates in the MSFragger search engine: facilitating bulk-cell and single-cell proteomics data analysis. 71st ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2023). Houston, Texas. (poster presentation).
  14. Yu, F., Li, K., Yang, K., Polasky, D., & Nesvizhskii, A. (March 4-8, 2023). Comprehensive single-cell proteomics data analysis using FragPipe. 19th Annual US HUPO Conference (US HUPO 2023). Chicago Illinois. (oral presentation).
  15. Yu, F., Teo, G.C., da Veiga Leprevost, F., Polasky, D., Geiszler, D., Yang, K., Li, K., & Nesvizhskii, A. (October 17-21, 2022). FragPipe enables one-stop data analysis for bottom-up proteomics. 3rd CASMS Virtual Conference. Virtual. (poster presentation, best poster award).
  16. Yu, F., Teo, G.C., Avtonomov. D., Haynes, S., da Veiga Leprevost, F., Geiszler, D., Polasky, D., Yang, K., & Nesvizhskii, A. (July 17-23, 2022). FragPipe in headless mode enables seamlessly analyzing LC-MS data in Galaxy platform. 2022 Galaxy Community Conference (GCC 2022). Minneapolis, Minnesota. (oral presentation).
  17. Yu, F., Kong, A., Patil, S., Avtonomov, D., & Nesvizhskii, A. (June 5-9, 2022). Pepcentric enables fast peptide searching against public data repositories. 70th ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2022). Minneapolis, Minnesota. (poster presentation, http://pepcentric.arsci.com:8080).
  18. Yu, F., Yang, K., Demichev, V., Haynes, S., Ralser, M., & Nesvizhskii, A. (Feburary 26 - March 2, 2022). One-stop DIA data analysis using MSFragger-DIA coupled with FragPipe. 18th Annual US HUPO Conference (US HUPO 2022). Charleston, Sourth Carolina. (poster presentation).
  19. Yu, F., Teo, G. C., Haynes, S., Li, G. X., & Nesvizhskii, A. (October 31 - November 4, 2021). Direct peptide identification from DIA data with MSFragger DIA. 69th ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2021). Philadelphia, Pennsylvania. (oral presentation).
  20. Yu, F., Haynes, S., & Nesvizhskii, A. (March 4-12, 2021). False discovery rate-controlled match-between-runs enables accurate and sensitive label free quantification. 17th Annual US HUPO Conference (US HUPO 2021). Virtual. (poster presentation).
  21. Yu, F., Teo, G. C., Kong, A., Haynes, S., Avtonomov, D., Geiszler, D., & Nesvizhskii, A. (July 13-16, 2020). Improved identification of modified peptides using localization-aware open search. 28th Conference on Intelligent Systems for Molecular Biology (ISMB 2020). Virtual. (poster presentation).
  22. Yu, F., Haynes, S., Avtonomov, D., Kong, A., da Veiga Leprevost, F., & Nesvizhskii, A. (June 1-12,2020). Breaking the logjam: fast peptide identification and quantification in timsTOF PASEF data. 68th ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2020). Virtual. (poster presentation).
  23. Yu, F., Haynes, S., Teo, G. C., Kong, A., Avtonomov, D., da Veiga Leprevost, F., Chang, H.-Y., Geiszler, D., Polasky, D., & Nesvizhskii, A. (March 6-10, 2020). Fast and quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. 16th Annual US HUPO Conference (US HUPO 2020). Virtual. (poster presentation).
  24. Yu, F., Teo, G. C., Kong, A., da Veiga Leprevost, F., Avtonomov, D., Chang, H., Geiszler, D., Haynes, S., Polasky, D., & Nesvizhskii, A. (September 15-19, 2019). MSFragger: fast and sensitive peptide identification in diverse proteomic datasets. 18th Human Proteome Organization World Congress (HUPO 2019). Adelaide, Australia. (poster presentation).
  25. Yu, F., Teo, G. C., Kong, A., da Veiga Leprevost, F., Chang, H., & Nesvizhskii, A. (June 2-6, 2019). Comparison of open search tools. 67th ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2019). Atlanta, Georgia. (poster presentation).
  26. Yu, F., Li, N., & Yu, W. (August 22-23, 2018). PTM-invariant peptide identification. 5th China Workshop on Computational Proteomics (CNCP 2018). Beijing China, (oral presentation).
  27. Yu, F., Li, N., & Yu, W. (August 13-18, 2017). PIPI: PTM-invariant peptide identification using coding method. Post-translational Modification Networks, Gordon Research Conference. Hong Kong, China. (poster presentation).
  28. Yu, F., Li, N., & Yu, W. (August, 2017). PIPI: PTM-invariant peptide identification using coding method. 11th International Conference on Computational Systems Biology. Shenzhen, China. (oral presentation)
  29. Yu, F., Li, N., & Yu, W. (May 3-7, 2017). Exhaustively identifying cross-linked peptides with a linear complexity. 21st Annual International Conference on Research in Computational Molecular Biology (RECOMB 2017). Hong Kong, China. (poster presentation).
  30. Yu, F., Liu, H., & Shi, P. (January 5-7, 2012). PET image reconstruction based on particle filter framework. In Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on (pp. 851-853). IEEE.
  31. Yu, F., Liu, H., & Shi, P. (September 11-14, 2011). PET image reconstruction: GPU-accelerated particle filter framework. In Image Processing (ICIP), 2011 18th IEEE International Conference on (pp. 417-420). IEEE.

Awards

  • CASMS (Chinese American Society for Mass Spectrometry) Young Investigator Award (2024)
  • CASMS (Chinese American Society for Mass Spectrometry) Best Poster Award (2023)
  • CASMS (Chinese American Society for Mass Spectrometry) Best Poster Award (2022)
  • US HUPO (United States Human Proteome Organization) Travel Stipend Award (2020)
  • UGC Research Travel Grant Award (2018-19)
  • UGC Research Travel Grant Award (2018-19)
  • UGC Research Travel Grant Award (2017-18)
  • UGC Research Travel Grant Award (2016-17)
  • Graduate Student Research Award Program of HKMHDIA (Hong Kong Medical & Healthcare Device Industries Association Limited) (2015-16)

Professional Activities

Journal reviewer

Journal Years Reviewed
Science 2025 1
Nature Methods 2024 2
Nature Communications 2021, 2022, 2026 4
Nature Computational Science 2020 3
Nucleic Acids Research 2026 1
Journal of the American Chemical Society 2024,2025 2
GigaScience 2025 1
Mass Spectrometry Reviews 2024 3
Scientific Data 2023 2
Briefings in Bioinformatics 2020, 2021, 2022 13
Bioinformatics 2019, 2021, 2023, 2024, 2025, 2026 19
Analytical Chemistry 2020, 2021, 2023, 2025, 2026 16
Journal of Proteome Research 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026 29
Proteomics 2019, 2020, 2024 7
Journal of Proteomics 2021 1
ACS Chemical Biology 2024 2
ACS Omega 2021, 2024 3
BMC Bioinformatics 2014, 2016, 2019, 2020 8
BMC Genomics 2019 1
Rapid Communications in Mass Spectrometry 2021 1
Bioinformatics Advances 2021, 2023, 2025 7
Communications Biology 2023 1
Communications Chemistry 2024 3
Genomics, Proteomics & Bioinformatics 2024, 2026 4
Journal of American Society for Mass Spectrometry 2024, 2025 5
International Journal of Mass Spectrometry 2022 2
Small Methods 2024 1
PLOS Computational Biology 2022 1
total: 143




Conference Committee

Conferences Years
ISMB CompMS program committee 2023, 2025, 2026




Conference Committee

Editorial Board Years
Genome Biology, Advances in Proteomics and Metabolomics Collection 2025

Teaching Activities

  • Biological Proteomics for Beginners - Workshop @UCSD, September 9, 2025
  • Biological Proteomics for Beginners: 2-Day Workshop @UCSF Mission Bay, July 12, 2024
  • US HUPO 2023 short course: "Design and Analysis of Quantitative Proteomics Experiments with TMT Labeling: case studies using FragPipe and MSstatsTMT", May 4-5, 2023
  • University of Michigan, BIOINF 551 FA 2022 (teaching assistant)
  • EMBO practical course: "Quantitative Proteomics: Strategies and Tools to Probe Biology", May 3-7, 2021