ReviewProteomics2025
Data Processing and Analysis in Positional Proteomics.
Review in Proteomics, 2025. 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.
- Data Processing and Analysis in Positional Proteomics.Proteomics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Proteolytic cleavage is an irreversible post-translational modification (PTM), and dysregulation of protease activity is often a hallmark in disease. Aberrant proteolysis can alter protein abundance or function, disturbing cellular state and resulting in disease-specific biomarkers or therapeutic targets. Positional proteomics facilitates global identification and precise quantification of position-specific peptides, such as those located N- or C-terminal in the protein sequence. These techniques enable the study of both natural and neo-protein termini, as well as associated PTMs. Despite its importance, proteolysis remains understudied due to experimental challenges and complex data processing. In this review, we outline key strategies for data analysis and processing in positional proteomics, emphasizing how identification, quantification, and interpretation of proteolytic cleavage sites differ from standard proteomics data analysis pipelines. We discuss differences in common approaches for terminomics-focused workflows, comparing N- versus C-terminomics, as well as different labeling strategies and acquisition methods. Additionally, we highlight considerations for proper normalization approaches, specifically the need to normalize cleavage abundances relative to protein and protease abundance. We explain the importance of integrating structural data, solvent accessibility, and tissue expression profiles during data analysis to better evaluate the biological significance of experimental results.
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.