Evidence map›Paper›PMID 42020565›Full record

ReviewNature protocols2026

Differential protein expression analysis of quantitative mass spectrometry data using DEqMS.

Yafeng Zhu, Olena Berkovska, Lingshuo Wang, Mei Yang, Henrik J Johansson, Georgios Mermelekas, Mahshid Zarrineh, Dong Yin, Lukas M Orre, Janne Lehtiö

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Yafeng Zhu *Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. yafeng.zhu@outlook.com.ORCID http://orcid.org/0000-0003-1947-9026
Olena Berkovska *Department of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden.ORCID http://orcid.org/0000-0002-8811-0591
Lingshuo WangGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Mei YangGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Henrik J JohanssonDepartment of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden.
Georgios MermelekasDepartment of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden.
Mahshid ZarrinehDepartment of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden.
Dong YinGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Lukas M OrreDepartment of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden.ORCID http://orcid.org/0000-0002-0384-1003
Janne LehtiöDepartment of Oncology and Pathology, Karolinska Institutet, SciLifeLab, Solna, Sweden. janne.lehtio@ki.se.ORCID http://orcid.org/0000-0002-8100-9562

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DEqMS is an R package-based statistical tool for differential protein expression analysis in quantitative mass spectrometry-based proteomics. It implements a robust Bayesian method for accurate variance estimation that accounts for the number of mass spectrometry features used for protein quantification (number of peptide precursors or peptide spectrum matches). Originally validated for data-dependent acquisition proteomics, DEqMS now extends to data-independent acquisition workflows, as demonstrated using both spike-in and real-world datasets. Given a peptide- or protein-level quantification table with mass spectrometry feature count as inputs, DEqMS outputs a protein- or gene-level results table containing fold changes and multiple statistics (t-values, P value, among others) adjusted according to mass spectrometry feature count. Here we detail the use of the DEqMS R package. This updated workflow broadens DEqMS's applicability, enabling researchers with basic R programming knowledge to identify proteins with significantly altered abundance between sample groups across diverse quantitative proteomics datasets. DEqMS is available to install at https://bioconductor.org/packages/DEqMS/ .

Indexed as

Mass SpectrometryProteinsProteomicsSoftwareBayes TheoremProteins

Identifiers

What OpenQuestion holds

Textmetadata
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.