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Related Concept Videos

Proteomics01:33

Proteomics

9.3K
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...
9.3K

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical

Jessica M Conforti1, Constantine C Breus1, Elyssia S Gallagher1

  • 1Department of Chemistry and Biochemistry, Baylor University, One Bear Place #97348, Waco, Texas 76798, United States.

Journal of Proteome Research
|October 8, 2025
PubMed
Summary

Searching proteomics samples together improves identification rates but can skew results due to alignment and imputation algorithms. Careful search design is crucial for accurate biomarker discovery in comparative proteomics.

Keywords:
database searchingimputationmissing proteinsproteomicsretention-time alignmentstochasticity

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Bioinformatics

Background:

  • Comparative proteomics aims to identify differentially expressed proteins between biological samples.
  • Workflow variability necessitates algorithms like retention-time alignment and imputation in data processing software.
  • These algorithms are intended to improve quantification and reduce processing time.

Purpose of the Study:

  • To investigate the impact of searching proteomics samples together versus separately on statistical comparisons.
  • To test the hypothesis that combined searching alters statistical comparisons due to alignment and imputation algorithms.

Main Methods:

  • Proteomics data from different cleanup methods and single proteins were searched using Progenesis Qi software.
  • Data were analyzed both separately and together to compare outcomes.
  • Statistical comparisons were performed to assess protein abundance and differential expression.

Main Results:

  • Searching samples together increased protein identifications per sample and enhanced protein similarity.
  • Combined searching led to false transfers and altered protein abundance and differential expression.
  • Retention-time alignment and imputation algorithms were identified as key factors influencing these changes.

Conclusions:

  • Searching proteomics samples together can introduce biases affecting biomarker discovery.
  • The design of the database search strategy significantly impacts the reliability of comparative proteomics results.
  • Careful consideration of search parameters is essential for accurate biomarker identification.