ArticleBMC bioinformatics2023
Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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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.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
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- Proximity labeling strategies in Caenorhabditis elegans: a comprehensive review.Cell communication and signaling : CCS · 2026Review
- Degron models: a toolbox for rapid in vivo depletion of essential proteins regulating mRNA metabolism.Communications biology · 2026Article
- Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE).Proteomics. Clinical applications · 2026Article
- Probiotic Engraftment of Akkermansia muciniphila in an In Vitro Synthetic Microbial Community.Microbial ecology · 2025Article
- PLAA/UFD-3 regulates P-bodies through its intrinsic disordered domain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- ProteoPlotter: An Executable Proteomics Visualization Tool Compatible with Perseus.Journal of proteome research · 2025Article
- Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.Briefings in bioinformatics · 2025Article
- Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples.Journal of proteome research · 2025Article
- The variegatedComputational and structural biotechnology journal · 2024Article
- OmicScope unravels systems-level insights from quantitative proteomics data.Nature communications · 2024Article
- Single-tissue proteomics inProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Quantitative proteomics of dorsolateral prefrontal cortex reveals an early pattern of synaptic dysmaturation in children with idiopathic autism.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Functional characterization ofFrontiers in cellular and infection microbiology · 2024Article
- Proteomics analysis reveals the differential impact of the p97 inhibitor CB-5083 on protein levels in various cellular compartments of the HL-60 cell line.microPublication biology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object.
resultsThe R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms.
conclusionsTidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.
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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.