ArticleJournal of proteome research2023
urPTMdb/TeaProt: Upstream and Downstream Proteomics Analysis.
Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed, 9 citations in OpenAlex.
- Quantitative interactome mapping of skeletal muscle insulin resistance.Molecular systems biology · 2026Article
- Intrinsic muscle stem cell dysfunction underlies functional deficits in models of type 1 diabetes.NPJ Regenerative medicine · 2026Article
- Phosphoproteomics of aged insulin-resistant bone identifies P70S6K phosphorylation of AFF4 as a gene-specific transcriptional regulator.Nature communications · 2025Article
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.Nature and science of sleep · 2024Article
- Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function.eLife · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have developed the underrepresented post-translational modification (PTM) database (urPTMdb), a PTM gene set database to accelerate the discovery of enriched protein modifications in experimental data. urPTMdb provides curated lists of proteins reported to be substrates of underrepresented modifications. Their enrichment in proteomics datasets can reveal unexpected PTM regulations. urPTMdb can be implemented in existing workflows, or used in TeaProt, an online Shiny tool that integrates upstream transcription factor enrichment analysis with downstream pathway analysis through an easy-to-use interactive interface. TeaProt annotates user-uploaded data with drug-gene interactions, subcellular localizations, phenotypic functions, gene-disease associations, and enzyme-gene interactions. TeaProt enables gene set enrichment analysis (GSEA) to discover enrichments in gene sets from various resources, including MSigDB, CHEA, and urPTMdb. We demonstrate the utility of urPTMdb and TeaProt through the analysis of a previously published Western diet-induced remodeling of the tongue proteome, which revealed altered cellular processes associated with energy metabolism, interferon alpha/gamma response, adipogenesis, HMGylation substrate enrichment, and transcription regulation through PPARG and CEBPA. Additionally, we analyzed the interactome of ADP-ribose glycohydrolase TARG1, a key enzyme that removes mono-ADP-ribosylation. This analysis identified an enrichment of ADP-ribosylation, ribosomal proteins, and proteins localized in the nucleoli and endoplasmic reticulum. TeaProt and urPTMdb are accessible at https://tea.coffeeprot.com/.
Indexed as
Identifiers
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.