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Cited by 6Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsMetabolomics and Mass Spectrometry Studies

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Perspectives in computational mass spectrometry: recent developments and key challenges

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Sachsenberg, Timo, Pino, Lindsay K, Brunet, Marie, Bludau, Isabell, Kohlbacher, Oliver, Vizcaino, Juan Antonio, Bittremieux, Wout·2024·Bioinformatics Advances
DOI 10.1093/bioadv/vbaf301PMC PMC12715313

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Abstract

Summary: Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have reshaped the landscape of computational MS. This perspective reviews recent developments and highlights key challenges, including data harmonization, statistical confidence estimation, repository-scale analysis, multi-omics integration, and privacy in clinical MS. We also discuss the increasing importance of machine learning and the need to build corresponding literacy within the community. Finally, we reflect on the role of the Computational Mass Spectrometry (CompMS) Community of Special Interest of the International Society for Computational Biology in supporting collaboration, innovation, and knowledge exchange. With MS-based technologies now central to both basic and translational research, continued investment in robust and reproducible computational methods will be essential to realize their full potential.

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Research works printed but not linked: 107

  1. Adams, 2025, A 2025 perspective on the role of machine learning in clinical proteomics, Expert Rev Proteomics, 22, 363, 10.1080/14789450.2025.2545828 · DOI
  2. Alka, 2022, DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics, Nat Commun, 13, 1347, 10.1038/s41467-022-29006-z · DOI
  3. Bandeira, 2021, Data management of sensitive human proteomics data: current practices, recommendations, and perspectives for the future, Mol Cell Proteomics, 20, 100071, 10.1016/j.mcpro.2021.100071 · DOI
  4. Beimers, 2025, Technical evaluation of plasma proteomics technologies, J Proteome Res, 24, 3074, 10.1021/acs.jproteome.5c00221 · DOI
  5. Bennett, 2023, Single-cell proteomics enabled by next-generation sequencing or mass spectrometry, Nat Methods, 20, 363, 10.1038/s41592-023-01791-5 · DOI · Paper page
  6. Bielow, 2024, Communicating mass spectrometry quality information in mzQC with Python, R, and Java, J Am Soc Mass Spectrom, 35, 1875, 10.1021/jasms.4c00174 · DOI
  7. Bills, 2022, Novel real-time library search driven data acquisition strategy for identification and characterization of metabolites, Anal Chem, 94, 3749, 10.1021/acs.analchem.1c04336 · DOI
  8. Bittremieux, 2024, Deep learning methods for de novo peptide sequencing, Mass Spectrom Rev, 10.1002/mas.21919 · DOI
  9. Bittremieux, 2023, Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics, Nat Commun, 14, 8488, 10.1038/s41467-023-44035-y · DOI
  10. Bittremieux, 2018, Quality control in mass spectrometry-based proteomics, Mass Spectrom Rev, 37, 697, 10.1002/mas.21544 · DOI
  11. Bittremieux, 2017, The human proteome organization–proteomics standards initiative quality control working group: making quality control more accessible for biological mass spectrometry, Anal Chem, 89, 4474, 10.1021/acs.analchem.6b04310 · DOI
  12. Bouwmeester, 2021, DeepLC can predict retention times for peptides that carry as-yet unseen modifications, Nat Methods, 18, 1363, 10.1038/s41592-021-01301-5 · DOI
  13. Burankova, 2025, Privacy-preserving multicenter differential protein abundance analysis with FedProt, Nat Comput Sci, 5, 675, 10.1038/s43588-025-00832-7 · DOI
  14. Bushuiev, 2025, Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS, Nat Biotechnol, 10.1038/s41587-025-02663-3 · DOI
  15. Cai, 2025, Federated deep learning enables cancer subtyping by proteomics, Cancer Discov, 15, 1803, 10.1158/2159-8290.CD-24-1488 · DOI
  16. Choi, 2020, MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets, Nat Methods, 17, 981, 10.1038/s41592-020-0955-0 · DOI
  17. Claeys, 2023, lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation, Nat Commun, 14, 6743, 10.1038/s41467-023-42543-5 · DOI
  18. Dai, 2021, A proteomics sample metadata representation for multiomics integration and big data analysis, Nat Commun, 12, 5854, 10.1038/s41467-021-26111-3 · DOI
  19. Dai, 2024, Quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data, Nat Methods, 21, 1603, 10.1038/s41592-024-02343-1 · DOI
  20. D’Altri, 2025, The federated European genome–phenome archive as a global network for sharing human genomics data, Nat Genet, 57, 481, 10.1038/s41588-025-02101-9 · DOI
  21. Damiani, 2025, A universal language for finding mass spectrometry data patterns, Nat Methods, 22, 1247, 10.1038/s41592-025-02660-z · DOI
  22. Degroeve, 2013, MS2PIP: a tool for MS/MS peak intensity prediction, Bioinformatics, 29, 3199, 10.1093/bioinformatics/btt544 · DOI
  23. Demichev, 2022, dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts, Nat Commun, 13, 3944, 10.1038/s41467-022-31492-0 · DOI
  24. Desaire, 2022, How (not) to generate a highly predictive biomarker panel using machine learning, J Proteome Res, 21, 2071, 10.1021/acs.jproteome.2c00117 · DOI
  25. Desiere, 2006, The PeptideAtlas project, Nucleic Acids Res, 34, D655, 10.1093/nar/gkj040 · DOI · Paper page
  26. Deutsch, 2023, Proteomics standards initiative at twenty years: current activities and future work, J Proteome Res, 22, 287, 10.1021/acs.jproteome.2c00637 · DOI
  27. Devreese, 2025, Collisional cross-section prediction for multiconformational peptide ions with IM2Deep, Anal Chem, 97, 15113, 10.1021/acs.analchem.5c01142 · DOI
  28. Di Tommaso, 2017, Nextflow enables reproducible computational workflows, Nat Biotechnol, 35, 316, 10.1038/nbt.3820 · DOI · Paper page
  29. Drake, 2022, Protein complex prediction using rosetta, AlphaFold, and mass spectrometry covalent labeling, Nat Commun, 13, 7846, 10.1038/s41467-022-35593-8 · DOI · Paper page
  30. Dührkop, 2019, SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information, Nat Methods, 16, 299, 10.1038/s41592-019-0344-8 · DOI
  31. El Abiead, 2025, Enabling pan-repository reanalysis for big data science of public metabolomics data, Nat Commun, 16, 4838, 10.1038/s41467-025-60067-y · DOI
  32. Elias, 2007, Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry, Nat Methods, 4, 207, 10.1038/nmeth1019 · DOI · Paper page
  33. Frejno, 2025, Unifying the analysis of bottom-up proteomics data with CHIMERYS, Nat Methods, 22, 1017, 10.1038/s41592-025-02663-w · DOI
  34. Gessulat, 2019, Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning, Nat Methods, 16, 509, 10.1038/s41592-019-0426-7 · DOI
  35. Geyer, 2021, Plasma proteomes can be reidentifiable and potentially contain personally sensitive and incidental findings, Mol Cell Proteomics, 20, 100035, 10.1074/mcp.RA120.002359 · DOI
  36. Goldman, 2023, Annotating metabolite mass spectra with domain-inspired chemical formula transformers, Nat Mach Intell, 5, 965, 10.1038/s42256-023-00708-3 · DOI · Paper page
  37. Griss, 2016, Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets, Nat Methods, 13, 651, 10.1038/nmeth.3902 · DOI
  38. Hasin, 2017, Multi-omics approaches to disease, Genome Biol, 18, 83, 10.1186/s13059-017-1215-1 · DOI · Paper page
  39. Haug, 2013, MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data, Nucleic Acids Res, 41, D781, 10.1093/nar/gks1004 · DOI
  40. Heil, 2023, Evaluating the performance of the astral mass analyzer for quantitative proteomics using data-independent acquisition, J Proteome Res, 22, 3290, 10.1021/acs.jproteome.3c00357 · DOI
  41. Huber, 2021, MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra, J Cheminform, 13, 84, 10.1186/s13321-021-00558-4 · DOI
  42. Jarmusch, 2020, ReDU: a framework to find and reanalyze public mass spectrometry data, Nat Methods, 17, 901, 10.1038/s41592-020-0916-7 · DOI · Paper page
  43. Jeong, 2020, FLASHDeconv: ultrafast, high-quality feature deconvolution for top-down proteomics, Cell Syst, 10, 213, 10.1016/j.cels.2020.01.003 · DOI · Paper page
  44. Johnson, 2007, Adjusting batch effects in microarray expression data using empirical bayes methods, Biostatistics, 8, 118, 10.1093/biostatistics/kxj037 · DOI · Paper page
  45. Kalhor, 2024, Rescoring peptide spectrum matches: boosting proteomics performance by integrating peptide property predictors into peptide identification, Mol Cell Proteomics, 23, 100798, 10.1016/j.mcpro.2024.100798 · DOI
  46. Käll, 2007, Semi-supervised learning for peptide identification from shotgun proteomics datasets, Nat Methods, 4, 923, 10.1038/nmeth1113 · DOI · Paper page
  47. Karczewski, 2018, Integrative omics for health and disease, Nat Rev Genet, 19, 299, 10.1038/nrg.2018.4 · DOI · Paper page
  48. Keane, 2021, The growing need for controlled data access models in clinical proteomics and metabolomics, Nat Commun, 12, 5787, 10.1038/s41467-021-26110-4 · DOI
  49. Konermann, 2024, Hydrogen/deuterium exchange mass spectrometry: fundamentals, limitations, and opportunities, Mol Cell Proteomics, 23, 100853, 10.1016/j.mcpro.2024.100853 · DOI
  50. Kong, 2017, MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics, Nat Methods, 14, 513, 10.1038/nmeth.4256 · DOI
  51. Korsunsky, 2019, Fast, sensitive and accurate integration of single-cell data with harmony, Nat Methods, 16, 1289, 10.1038/s41592-019-0619-0 · DOI · Paper page
  52. Kou, 2016, TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization, Bioinformatics, 32, 3495, 10.1093/bioinformatics/btw398 · DOI · Paper page
  53. Lazear, 2023, Sage: an open-source tool for fast proteomics searching and quantification at scale, J Proteome Res, 22, 3652, 10.1021/acs.jproteome.3c00486 · DOI
  54. LeDuc, 2022, Proteomics standards initiative’s ProForma 2.0: unifying the encoding of proteoforms and peptidoforms, J Proteome Res, 21, 1189, 10.1021/acs.jproteome.1c00771 · DOI
  55. Leek, 2010, Tackling the widespread and critical impact of batch effects in high-throughput data, Nat Rev Genet, 11, 733, 10.1038/nrg2825 · DOI · Paper page
  56. Levitsky, 2023, Massive proteogenomic reanalysis of publicly available proteomic datasets of human tissues in search for protein recoding via adenosine-to-inosine RNA editing, J Proteome Res, 22, 1695, 10.1021/acs.jproteome.2c00740 · DOI
  57. Li, 2023, Flash entropy search to query all mass spectral libraries in real time, Nat Methods, 20, 1475, 10.1038/s41592-023-02012-9 · DOI
  58. Lin, 2004, Genomic research and human subject privacy, Science, 305, 183, 10.1126/science.1095019 · DOI
  59. Ma, 2019, iProX: an integrated proteome resource, Nucleic Acids Res, 47, D1211, 10.1093/nar/gky869 · DOI
  60. Mann, 2021, Artificial intelligence for proteomics and biomarker discovery, Cell Syst, 12, 759, 10.1016/j.cels.2021.06.006 · DOI
  61. Mann, 2021, Ethical principles, constraints, and opportunities in clinical proteomics, Mol Cell Proteomics, 20, 100046, 10.1016/j.mcpro.2021.100046 · DOI
  62. Martens, 2011, mzML—a community standard for mass spectrometry data, Mol Cell Proteomics, 10, R110.000133, 10.1074/mcp.R110.000133 · DOI
  63. Marx, 2024, Inside the chase after those elusive proteoforms, Nat Methods, 21, 158, 10.1038/s41592-024-02170-4 · DOI
  64. Mendes, 2024, Eleven shades of PASEF, Expert Rev Proteomics, 21, 367, 10.1080/14789450.2024.2413092 · DOI
  65. Messner, 2020, Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection, Cell Syst, 11, 11, 10.1016/j.cels.2020.05.012 · DOI · Paper page
  66. 2024, spatial proteomics, Nat Methods, 21, 2195, 10.1038/s41592-024-02565-3 · DOI
  67. Mohanty, 2024, The underappreciated diversity of bile acid modifications, Cell, 187, 1801, 10.1016/j.cell.2024.02.019 · DOI
  68. Mongia, 2024, Fast mass spectrometry search and clustering of untargeted metabolomics data, Nat Biotechnol, 42, 1672, 10.1038/s41587-023-01985-4 · DOI
  69. Moriya, 2019, The jPOST environment: an integrated proteomics data repository and database, Nucleic Acids Res, 47, D1218, 10.1093/nar/gky899 · DOI
  70. Neely, 2021, Cloudy with a chance of peptides: accessibility, scalability, and reproducibility with Cloud-Hosted environments, J Proteome Res, 20, 2076, 10.1021/acs.jproteome.0c00920 · DOI
  71. Omenn, 2024, The 2023 report on the proteome from the HUPO human proteome project, J Proteome Res, 23, 532, 10.1021/acs.jproteome.3c00591 · DOI
  72. O’Reilly, 2018, Cross-linking mass spectrometry: methods and applications in structural, molecular and systems biology, Nat Struct Mol Biol, 25, 1000, 10.1038/s41594-018-0147-0 · DOI
  73. Palmblad, 2022, Interpretation of the DOME recommendations for machine learning in proteomics and metabolomics, J Proteome Res, 21, 1204, 10.1021/acs.jproteome.1c00900 · DOI
  74. Pedregosa, 2011, Scikit-learn: machine learning in python, J Mach Learn Res, 12, 2825
  75. Perez-Riverol, 2022, The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences, Nucleic Acids Res, 50, D543, 10.1093/nar/gkab1038 · DOI · Paper page
  76. Qin, 2021, A multi-scale map of cell structure fusing protein images and interactions, Nature, 600, 536, 10.1038/s41586-021-04115-9 · DOI · Paper page
  77. Ramasamy, 2020, Scop3P: a comprehensive resource of human phosphosites within their full context, J Proteome Res, 19, 3478, 10.1021/acs.jproteome.0c00306 · DOI
  78. Rehfeldt, 2023, ProteomicsML: an online platform for community-curated data sets and tutorials for machine learning in proteomics, J Proteome Res, 22, 632, 10.1021/acs.jproteome.2c00629 · DOI
  79. Roberts, 2024, Top-down proteomics, Nat Rev Methods Primer, 4, 38, 10.1038/s43586-024-00318-2 · DOI · Paper page
  80. Rocca-Serra, 2016, Data standards can boost metabolomics research, and if there is a will, there is a way, Metabolomics, 12, 14, 10.1007/s11306-015-0879-3 · DOI
  81. Savitski, 2015, A scalable approach for protein false discovery rate estimation in large proteomic data sets, Mol Cell Proteomics, 14, 2394, 10.1074/mcp.M114.046995 · DOI
  82. Schär, 2025, A flexible end-to-end automated sample preparation workflow enables reproducible large-scale bottom-up proteomics, Anal Chem, 97, 22116, 10.1021/acs.analchem.5c03829 · DOI
  83. Scheubert, 2017, Significance estimation for large scale metabolomics annotations by spectral matching, Nat Commun, 8, 1494, 10.1038/s41467-017-01318-5 · DOI
  84. Schweppe, 2020, Full-Featured, Real-Time database searching platform enables fast and accurate multiplexed quantitative proteomics, J Proteome Res, 19, 2026, 10.1021/acs.jproteome.9b00860 · DOI
  85. Serang, 2015, Solution to statistical challenges in proteomics is more statistics, not less, J Proteome Res, 14, 4099, 10.1021/acs.jproteome.5b00568 · DOI
  86. Sharma, 2018, Panorama public: a public repository for quantitative data sets processed in skyline, Mol Cell Proteomics, 17, 1239, 10.1074/mcp.RA117.000543 · DOI
  87. Stahl, 2023, Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning, Nat Biotechnol, 41, 1810, 10.1038/s41587-023-01704-z · DOI
  88. Storey, 2011, International Encyclopedia of Statistical Science, 504, 10.1007/978-3-642-04898-2_248 · DOI
  89. Stravs, 2022, MSNovelist: de novo structure generation from mass spectra, Nat Methods, 19, 865, 10.1038/s41592-022-01486-3 · DOI · Paper page
  90. Sud, 2016, Metabolomics workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools, Nucleic Acids Res, 44, D463, 10.1093/nar/gkv1042 · DOI
  91. Sun, 2025, Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence, Nat Methods
  92. Teleman, 2014, Numerical compression schemes for proteomics mass spectrometry data, Mol Cell Proteomics, 13, 1537, 10.1074/mcp.O114.037879 · DOI
  93. Uhlen, 2010, Towards a knowledge-based human protein atlas, Nat Biotechnol, 28, 1248, 10.1038/nbt1210-1248 · DOI · Paper page
  94. Van Den Bossche, 2025, mzPeak: designing a scalable, interoperable, and future-ready mass spectrometry data format, J Proteome Res, 24, 5329, 10.1021/acs.jproteome.5c00435 · DOI
  95. Vizcaíno, 2014, ProteomeXchange provides globally coordinated proteomics data submission and dissemination, Nat Biotechnol, 32, 223, 10.1038/nbt.2839 · DOI
  96. Wadie, 2024, METASPACE-ML: context-specific metabolite annotation for imaging mass spectrometry using machine learning, Nat Commun, 15, 9110, 10.1038/s41467-024-52213-9 · DOI
  97. Walsh, 2021, DOME: recommendations for supervised machine learning validation in biology, Nat. Methods, 18, 1122, 10.1038/s41592-021-01205-4 · DOI · Paper page
  98. Wang, 2021, CFM-ID 4.0: more accurate ESI-MS/MS spectral prediction and compound identification, Anal Chem, 93, 11692, 10.1021/acs.analchem.1c01465 · DOI
  99. Wang, 2016, Sharing and community curation of mass spectrometry data with global natural products social molecular networking, Nat Biotechnol, 34, 828, 10.1038/nbt.3597 · DOI · Paper page
  100. Wang, 2022, High-throughput proteomics of nanogram-scale samples with zeno SWATH MS, Elife, 11, e83947, 10.7554/eLife.83947 · DOI
  101. Watanabe, 2021, GlycoPOST realizes FAIR principles for glycomics mass spectrometry data, Nucleic Acids Res, 49, D1523, 10.1093/nar/gkaa1012 · DOI
  102. Webel, 2024, Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning, Nat Commun, 15, 5405, 10.1038/s41467-024-48711-5 · DOI
  103. Wen, 2025, Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment, Nat Methods, 22, 1454, 10.1038/s41592-025-02719-x · DOI
  104. Wörheide, 2021, Multi-omics integration in biomedical research—a metabolomics-centric review, Anal Chim Acta, 1141, 144, 10.1016/j.aca.2020.10.038 · DOI
  105. Young, 2024, Tandem mass spectrum prediction for small molecules using graph transformers, Nat Mach Intell, 6, 404, 10.1038/s42256-024-00816-8 · DOI
  106. Zeng, 2022, AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics, Nat Commun, 13, 7238, 10.1038/s41467-022-34904-3 · DOI
  107. Zuo, 2021, MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition, Bioinformatics, 37, i231, 10.1093/bioinformatics/btab279 · DOI

Metadata

Authors
Sachsenberg, T., Pino, L. K., Brunet, M., Bludau, I., Kohlbacher, O., Vizcaino, J. A., & Bittremieux, W.
Affiliations
  • University of Tübingen (DE)
  • Talus Bio , Seattle, WA 98122,
  • Université de Sherbrooke (CA)
  • University Hospital Heidelberg (DE)
  • Universitätsklinikum Tübingen (DE)
  • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Hinxton CB10 1SD,
  • University of Antwerp (BE)
Corresponding author
Bittremieux, Wout
ORCID
Venue
Bioinformatics Advances
Publisher
Oxford University Press (OUP)
Published
2024-12-26
Volume
5
Issue
1
Pages
vbaf301-vbaf301
ISSN
2635-0041 (ISSN-L)
DOI
10.1093/bioadv/vbaf301
PMC
PMC12715313
OpenAlex
W4417501327
Cited by
6
References
13
Citations by year
2026: 6
FWCI
0.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics Advances (publishedVersion, cc-by)
  • ChemRxiv (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Universitätsbibliothek Tübingen
Funders
  • University of Antwerp’s Research Fund
  • Research Foundation—Flanders (G087625N, G0AHY25N)
  • Federal Ministry of Research, Technology and Space in the frame of de.NBI/ELIXIR-DE (W-DE.NBI-022)
  • Ministry of Science, Research and Arts Baden-Württemberg
  • EMBL core funding
  • Wellcome (223745/Z/21/Z)
  • BBSRC (BB/W000156/1, BB/Y513829/1, BB/X001911/1, BB/V018779/1)
  • EPSRC (EP/Y035984/1)
  • Chan Zuckerberg Initiative (2024–350548)
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
multi-omics integration, proteomics, metabolomics, lipidomics, glycomics

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