Evidence map›Paper›PMID 40622109›Full record

ArticleJournal of proteome research2025

ProMix: Enhancing Protein Quantification through Experimental Design and Statistical Normalization.

Huaying Fang, Mei-Chiung Shih, Lihua Jiang, Felipe da Veiga Leprevost, Ruiqi Jian, Alexey I Nesvizhskii, Michael P Snyder, Hua Tang

Abstract read
In one paragraph

Article in Journal of proteome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Huaying FangDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, United States.ORCID 0000-0002-7693-8260
Mei-Chiung ShihVA Cooperative Studies Program Coordinating Center, VA Palo Alto Health Care System, Palo Alto, California 94304, United States.
Lihua JiangDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, United States.
Felipe da Veiga LeprevostDepartment of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.
Ruiqi JianDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, United States.
Alexey I NesvizhskiiDepartment of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.ORCID 0000-0002-2806-7819
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, United States.
Hua TangDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, United States.

Funding

Genotype-Tissue-Protein: proteomic variation and quantitative trait loci (pQTL)U01HL131042 · NHLBI · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P., TANG, HUA · 2015 to 2016
$2.5M
Machine Learning for Decoding the Genetic Architecture of Complex Traits and Diseases Across PopulationsR35GM127063 · NIGMS · STANFORD UNIVERSITY · PI Hua Tang · 2018 to 2026
$2.2M
Trans-omics elucidation of genetic architecture underlying cardiovascular and HLBS diseasesR01HL142017 · NHLBI · STANFORD UNIVERSITY · PI KOOPERBERG, CHARLES L, TANG, HUA · 2019 to 2020
$1.1M
NHLBI NIH HHS R01 HL142017NHLBI NIH HHS U01 HL131042NIGMS NIH HHS R35 GM127063
6 · The paper itself

Abstract

Isobaric labeling of biospecimens followed by mass spectrometry (MS) has become the method of choice for large-scale, untargeted, quantitative proteomic profiling. However, subtle variation in experimental conditions can amplify sample variability and introduce systematic biases. Motivated by the challenges and opportunities arose in a recent proteogenomic study, we developed ProMix, a flexible analytical framework designed to improve protein normalization by leveraging two key experimental design features: (1) the inclusion of an additional reference sample to serve as an internal standard, and (2) the incorporation of replicates of each specimen. ProMix can utilize either or both features. Through applications to both simulated and real data sets, we demonstrate the improved performance of ProMix and highlight the advantages of the enhanced experimental design strategies.

Indexed as

ProteinsProteomeProteomicsResearch DesignSoftwareHumansMass SpectrometryReference StandardsProteinsProteomeexperimental designlinear mixed modelmultiplex mass spectrometryprotein normalizationquantitative proteomics

Identifiers

PMID40622109
PMCPMC12364727

What OpenQuestion holds

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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.