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Related Concept Videos

Proteomics01:33

Proteomics

9.1K
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...
9.1K

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses.

Shisheng Wang1, Wenxue Li2, Liqiang Hu1

  • 1West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China.

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|June 12, 2020
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Summary
This summary is machine-generated.

Missing values in mass spectrometry (MS) proteomics data hinder analysis. NAguideR software systematically evaluates 23 imputation algorithms, identifying optimal methods for data independent acquisition mass spectrometry (DIA-MS) experiments.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry (MS)-based quantitative proteomics experiments often yield data with missing values, impacting downstream analyses.
  • Existing imputation methods for missing values lack efficient, systematic, and user-friendly tools tailored for the proteomics community.

Purpose of the Study:

  • To develop a user-friendly software, NAguideR, for implementing and evaluating missing value imputation methods in proteomics.
  • To provide a systematic approach for assessing imputation performance using computational and novel proteomic empirical criteria.

Main Methods:

  • Developed NAguideR, a stand-alone software, to implement and evaluate 23 missing-value imputation algorithms.
  • Utilized classic computational criteria and unique proteomic empirical criteria (peptide charge-state consistency, protein-level consistency, protein complex/interaction consistency) for evaluation.
  • Applied NAguideR to three label-free DIA-MS datasets (peptide, protein, and phosphoproteomic levels) with biological replicates.

Main Results:

  • NAguideR successfully discriminated optimal imputation methods from sub-optimal ones for DIA-MS experiments.
  • The software demonstrated effectiveness across different data types (peptide, protein, phosphoproteomic) and acquisition methods.
  • Imputation performance was evaluated using both computational and novel empirical proteomic criteria.

Conclusions:

  • NAguideR is a powerful and user-friendly tool for the proteomics community to select appropriate missing value imputation strategies.
  • The software facilitates robust data analysis and interpretation by providing clear evaluations and downloadable results.
  • NAguideR aids in improving the reliability and accuracy of quantitative proteomics studies, particularly those using DIA-MS.