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Related Concept Videos

Proteomics01:33

Proteomics

7.5K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Updated: Jul 27, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization.

Jeff Jones1,2, Elliot J MacKrell3, Ting-Yu Wang4

  • 1Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, 91125, USA. jeffj@caltech.edu.

BMC Bioinformatics
|June 6, 2023
PubMed
Summary
This summary is machine-generated.

The R package tidyproteomics simplifies quantitative proteomics data analysis by providing a standardized framework and flexible workflows. This tool enhances data interoperability and streamlines complex analyses for researchers.

Keywords:
AnalysisAnnotation enrichmentImputationNormalizationPipelineProtein expressionProteomicsQuantitativeWorkflow

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative proteomics data analysis is complex due to diverse platforms and reporting formats.
  • Lack of standardized post-processing analyses hinders data interpretation and reproducibility.
  • Existing challenges include sample group statistics, quantitative variation assessment, and data filtering.

Purpose of the Study:

  • To develop a user-friendly R package, tidyproteomics, for simplifying quantitative proteomics data analysis.
  • To improve data interoperability across different mass spectrometry platforms.
  • To facilitate the integration of new post-processing algorithms and standardize analysis workflows.

Main Methods:

  • Development of the R package tidyproteomics.
  • Implementation of a simplified data-object for standardized data representation.
  • Creation of discrete, connectable functions for building analysis workflows.
  • Inclusion of options for customizing analysis order and incorporating custom algorithms.

Main Results:

  • tidyproteomics provides a framework for standardizing quantitative proteomics data.
  • The package enables the creation of end-to-end analysis workflows with discrete functions.
  • Researchers can string functions together in any order, select options, and incorporate custom algorithms.
  • Datasets are structured for easy biological annotation and manipulation.

Conclusions:

  • tidyproteomics simplifies data exploration and provides control over analysis steps.
  • It facilitates the assembly of complex, repeatable processing workflows.
  • The package offers a consistent data structure and accessible tools, saving researchers time on data manipulation.
  • tidyproteomics supports the development of additional analysis tools and enhances biological insights.