Evidence map›Paper›PMID 40263583›Full record

ArticleNature methods2025

Unifying the analysis of bottom-up proteomics data with CHIMERYS.

Martin Frejno, Michelle T Berger, Johanna Tüshaus, Alexander Hogrebe, Florian Seefried, Michael Graber, Patroklos Samaras, Samia Ben Fredj, Vishal Sukumar, Layla Eljagh and 8 more

Abstract read
In one paragraph

Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Preprocessing Large-Scale Proteomics Data.Methods in molecular biology (Clifton, N.J.) · 2027
    Article
  2. Mass spectrometry proteomics for studying mitostasis.Protein science : a publication of the Protein Society · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Carafe2 enables high qualitybioRxiv : the preprint server for biology · 2026
    Article
  9. Article
  10. Article
  11. Proteoforms as the true units of physiological function.European journal of applied physiology · 2026
    Review
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Martin Frejno *MSAID GmbH, Garching b. München, Germany. martin.frejno@msaid.de.ORCID http://orcid.org/0000-0002-6651-1773
Michelle T Berger *MSAID GmbH, Garching b. München, Germany.ORCID http://orcid.org/0000-0003-1438-3009
Johanna Tüshaus *School of Life Sciences, Technical University of Munich, Freising, Germany.ORCID http://orcid.org/0000-0001-9206-8093
Alexander Hogrebe *MSAID GmbH, Garching b. München, Germany.ORCID http://orcid.org/0000-0002-0203-6803
Florian SeefriedMSAID GmbH, Garching b. München, Germany.
Michael GraberMSAID GmbH, Garching b. München, Germany.
Patroklos SamarasMSAID GmbH, Garching b. München, Germany.
Samia Ben FredjMSAID GmbH, Garching b. München, Germany.
Vishal SukumarMSAID GmbH, Garching b. München, Germany.
Layla EljaghMSAID GmbH, Garching b. München, Germany.
Igor BronshteinMSAID GmbH, Garching b. München, Germany.
Lizi MamisashviliMSAID GmbH, Garching b. München, Germany.
Markus SchneiderMSAID GmbH, Garching b. München, Germany.
Siegfried GessulatMSAID GmbH, Garching b. München, Germany.
Tobias SchmidtMSAID GmbH, Garching b. München, Germany.ORCID http://orcid.org/0000-0002-1883-6514
Bernhard KusterSchool of Life Sciences, Technical University of Munich, Freising, Germany.ORCID http://orcid.org/0000-0002-9094-1677
Daniel P ZolgMSAID GmbH, Garching b. München, Germany.
Mathias WilhelmSchool of Life Sciences, Technical University of Munich, Freising, Germany. mathias.wilhelm@tum.de.ORCID http://orcid.org/0000-0002-9224-3258

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 031L0008ABundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 13GW0603BBundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 161L0214ABundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 16LW0243KEC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101077037
6 · The paper itself

Abstract

Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography-tandem mass spectrometry, creating thousands of spectra, most of which are chimeric and contain fragment ions from more than one peptide. Because of differences in data acquisition strategies such as data-dependent, data-independent or parallel reaction monitoring, separate software packages employing different analysis concepts are used for peptide identification and quantification, even though the underlying information is principally the same. Here, we introduce CHIMERYS, a spectrum-centric search algorithm designed for the deconvolution of chimeric spectra that unifies proteomic data analysis. Using accurate predictions of peptide retention time, fragment ion intensities and applying regularized linear regression, it explains as much fragment ion intensity as possible with as few peptides as possible. Together with rigorous false discovery rate control, CHIMERYS accurately identifies and quantifies multiple peptides per tandem mass spectrum in data-dependent, data-independent or parallel reaction monitoring experiments.

Indexed as

AlgorithmsProteomicsSoftwareChromatography, LiquidDatabases, ProteinPeptidesTandem Mass SpectrometryPeptides

Identifiers

PMID40263583
PMCPMC12074992

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.