ArticleMethods in molecular biology (Clifton, N.J.)2027
Integrated Analysis of Posttranslational Modifications and Total Proteome: Methods for Distinguishing Abundance from Usage Changes.
Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Posttranslational modifications (PTMs), particularly phosphorylation, regulate protein function at substoichiometric levels, making their quantitative analysis technically challenging. A critical limitation in PTM studies is distinguishing between changes in modification abundance due to altered protein expression versus genuine changes in modification stoichiometry (the fraction of protein molecules that are modified). This chapter presents a comprehensive workflow integrating Tandem-Mass-Tag (TMT)-based quantitative proteomics with specialized computational analysis to distinguish differential PTM abundance (DPA) from differential PTM usage (DPU) using the prolfqua, prolfquapp, and prophosqua R packages. The workflow introduces a robust statistical framework for integrated PTM-protein analysis; comprehensive visualization tools, including N-terminus to C-terminus (N-to-C) plots for protein-centric PTM mapping and sequence motif analysis for kinase prediction. This integrated approach enables researchers to identify actual signaling changes in PTM studies, prioritize functionally relevant modification sites, understand pathway-level regulation in disease contexts, and generate robust datasets suitable for publication in high-impact journals.
Indexed as
Identifiers
42878759What 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.