ArticleProteomics. Clinical applications2026
Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE).
Article in Proteomics. Clinical applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE).Proteomics. Clinical applications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In label-free mass spectrometry experiments, the data output is typically a proteome table that requires further processing, quality testing, and visualization to fully interpret the captured proteomic signals. Currently, post-quantification analysis of these tables often relies on complex programmatic pipelines, which can become challenging to use. Here, we introduce the Proteomics Eye (ProtE), a single-function R package designed to streamline the analysis of proteome tables generated by commonly used software tools (DIA-NN, ProteomeDiscoverer, and MaxQuant). ProtE provides a broad range of options for data processing, preparation, and statistical testing. It also performs gene set enrichment analysis and offers a comprehensive suite of visualization plots to assess data quality and facilitate biological interpretation. Given a categorical variable with two or more groups, ProtE enables group-wide and pairwise statistical comparisons across all group combinations, using both traditional statistical tests and linear models for differential expression analysis. By integrating all these features into a single, user-friendly R function, ProtE simplifies the analysis of large-scale label-free DDA and DIA datasets, making advanced proteomic analysis accessible to both experienced researchers and beginners.
Indexed as
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What OpenQuestion holds
Registered trials
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.