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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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Related Experiment Video

Updated: Nov 20, 2025

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

A comparative study of evaluating missing value imputation methods in label-free proteomics.

Liang Jin¹, Yingtao Bi², Chenqi Hu¹

¹Drug Metabolism and Pharmacokinetics, AbbVie Bioresearch Center, Worcester, MA, 01605, USA.

Scientific Reports

|January 20, 2021

Summary

This summary is machine-generated.

Choosing the right imputation method is crucial for accurate label-free proteomics. A random forest approach excels, improving data completeness and downstream analysis reliability.

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Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

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

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Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

Area of Science:

Proteomics
Bioinformatics
Data Science

Background:

Missing values (MVs) in label-free quantitative proteomics reduce data completeness.
Imputation is essential for handling MVs, but method selection impacts accuracy.
Evaluating imputation methods is critical for reliable proteomic data analysis.

Purpose of the Study:

To compare the performance of seven popular imputation methods for label-free quantitative proteomics.
To assess the impact of missing not at random (MNAR) rates on imputation accuracy.
To identify the most suitable imputation method for label-free proteomics.

Main Methods:

Comparative evaluation of seven imputation methods using benchmark and immune cell datasets.
Simulation of missing values (MVs) with varying rates and MNAR conditions.
Assessment of imputation accuracy using Normalized Root Mean Square Error (NRMSE), true positive (TP) detection, and False Altered-Protein Discovery Rate (FADR).

Main Results:

Imputation accuracy is more sensitive to the missing not at random (MNAR) rate than the overall missing value (MV) rate.
A random forest-based imputation method demonstrated superior performance across all evaluated metrics.
The chosen imputation method significantly impacts downstream proteomic analyses, including pathway and signature gene identification.

Conclusions:

Random forest imputation is the most suitable method for label-free proteomics, offering improved accuracy and reliability.
Accurate imputation enhances the completeness of proteomic datasets and the validity of downstream analyses.
Understanding the influence of MNAR rates is key to selecting effective imputation strategies in proteomics.