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Research papers from the quantms team and studies using quantms

Main Publication

quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data

Dai C, Pfeuffer J, Wang H, Zheng P, Käll L, Sachsenberg T, Demichev V, Bai M, Kohlbacher O, Perez-Riverol Y.

Nature Methods. 2024 Sep;21(9):1603-1607.

DOI: 10.1038/s41592-024-02343-1

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All Publications

  • 2026

    quantms-rescoring: ML-powered PSM rescoring for quantitative proteomics

    Dai C, Pfeuffer J, Sachsenberg T, et al.

    bioRxiv (preprint).

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  • 2026

    pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics

    Yue QX, Dai C, Kamatchinathan S, Bandla C, Webel H, et al.

    Molecular & Cellular Proteomics, 101530.

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  • 2025

    Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

    Zheng P, et al.

    Journal of Proteomics, 317:105440.

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  • 2024

    OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data

    Pfeuffer J, et al.

    Nature Methods, 21(3):365-367.

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  • 2024

    Proteogenomics analysis of human tissues using pangenomes

    Wang D, et al.

    bioRxiv 2024.05.24.595489.

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  • 2023

    Tissue-based absolute quantification using large-scale TMT and LFQ experiments

    Wang H, et al.

    Proteomics, 23(20):e2300188.

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  • 2023

    LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

    Bai M, et al.

    Journal of Proteome Research, 22(6):2114-2123.

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  • 2022

    Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides

    Umer HM, et al.

    Bioinformatics, 38(5):1470-1472.

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  • 2021

    A proteomics sample metadata representation for multiomics integration and big data analysis

    Dai C, et al.

    Nature Communications, 12(1):5854.

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  • 2026

    Development of PROTACs for targeted degradation of oncogenic TRK fusions

    Kumar S, et al.

    RSC Chemical Biology. 2026.

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  • 2025

    A Comprehensive Proteomic and Bioinformatic Analysis of Human Spinal Cord Injury

    Bernardo Harrington GM, et al.

    Journal of Neurotrauma, 42(3-4):292-306.

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  • 2025

    Multi-omics insights into the response of Aspergillus parasiticus

    Siaperas R, et al.

    Environmental Pollution, 376:126386.

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  • 2025

    Elucidating the enzymatic response of Abortiporus biennis

    Taxeidis G, et al.

    Environmental Pollution, 374:126214.

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  • 2024

    Alternative splicing decouples local from global PRC2 activity

    Arecco N, et al.

    Molecular Cell.

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  • 2023

    Triqler for Protein Summarization of Data from DIA Mass Spectrometry

    Truong P, et al.

    Journal of Proteome Research, 22(4):1359-1366.

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How to Cite

If you use quantms or any of its component tools in your research, please cite the main publication:

Dai C, Pfeuffer J, Wang H, Zheng P, Käll L, Sachsenberg T, Demichev V, Bai M, Kohlbacher O, Perez-Riverol Y. quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data. Nature Methods. 2024 Sep;21(9):1603-1607. doi: 10.1038/s41592-024-02343-1

If you use specific component tools (mokume, qpx, pmultiqc, ibaqpy), please also cite their respective publications where applicable.