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Proteomics

Proteomics

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: Jun 15, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.

Yu Yan^1,2,3, Baradwaj Simha Sankar^1,3, Bilal Mirza^1,2

¹Departments of Physiology and Medicine, University of California, Los Angeles (UCLA) School of Medicine, Los Angeles, California 90095, United States.

Journal of Proteome Research

|August 27, 2024

Summary

This summary is machine-generated.

Missing values in temporal proteomics data hinder analysis. A novel Data Multiple Imputation (DMI) pipeline effectively addresses these gaps, improving protein turnover rate detection and revealing new biological insights.

Keywords:

data imputation longitudinal data multiple imputation protein turnover rate

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Published on: April 4, 2018

Area of Science:

Proteomics
Bioinformatics
Systems Biology

Background:

Temporal proteomics data frequently contain missing values, complicating the analysis of dynamic biological processes.
Missing data can lead to inaccurate measurements and obscure critical biological events, limiting the understanding of protein turnover rates.
Accurate quantification of protein turnover is crucial for deciphering cellular mechanisms and identifying disease biomarkers.

Purpose of the Study:

To introduce and validate a Data Multiple Imputation (DMI) pipeline for handling missing values in temporal proteomics data.
To enhance the accuracy and robustness of protein turnover rate quantification in time-series proteomic datasets.
To demonstrate the pipeline's ability to uncover novel biological insights from complex proteomic data.

Main Methods:

Development of a novel Data Multiple Imputation (DMI) pipeline specifically for temporal proteomics data.
Application of the DMI pipeline to murine cardiac and human plasma temporal proteomics datasets.
Comparative analysis of DMI against single imputation methods (DSI) using benchmark datasets.

Main Results:

The DMI pipeline significantly improved the detection of protein turnover rates in both tested datasets.
Imputed data revealed new protein representations, enhancing the understanding of biological pathways and protein complex dynamics.
DMI demonstrated superior performance compared to single imputation methods in benchmark datasets.
The pipeline facilitated the identification of novel biomarker-disease associations.

Conclusions:

The developed DMI pipeline effectively overcomes challenges posed by missing values in temporal proteome dynamics studies.
DMI enables more comprehensive and robust analysis of temporal proteomics data, leading to augmented biological discoveries.
This approach provides a valuable tool for researchers studying dynamic biological systems and seeking to identify reliable biomarkers.