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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: Jul 30, 2025

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.

Abdul Rehman Basharat¹, Yong Zang², Liangliang Sun³

¹Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States.

Analytical Chemistry

|May 17, 2023

Summary

This summary is machine-generated.

Top-down mass spectrometry (MS) analysis requires accurate proteoform feature detection. A new software tool, TopFD, significantly improves the accuracy and reproducibility of identifying and quantifying intact proteoforms in complex samples.

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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

Area of Science:

Biochemistry
Analytical Chemistry
Computational Biology

Background:

Top-down liquid chromatography-mass spectrometry (LC-MS) is crucial for analyzing intact proteoforms.
Accurate proteoform feature detection is essential for reliable identification and quantification in MS data analysis.
Existing methods for proteoform feature detection have limitations in accuracy and reproducibility.

Purpose of the Study:

To develop and evaluate TopFD, a novel software tool for enhanced proteoform feature detection in top-down MS data.
To improve the accuracy, reproducibility, and abundance reproducibility of proteoform feature detection.
To provide a robust computational solution for top-down MS data analysis.

Main Methods:

Development of TopFD integrating algorithms for feature detection, boundary refinement, and machine learning-based evaluation.
Extensive benchmarking of TopFD against established tools (ProMex, FlashDeconv, Xtract).
Validation using seven diverse top-down MS datasets.

Main Results:

TopFD demonstrated superior performance compared to existing tools.
Significant improvements in feature accuracy and reproducibility were observed with TopFD.
Enhanced reproducibility in feature abundance quantification was achieved.

Conclusions:

TopFD represents a significant advancement in top-down MS data analysis.
The tool enhances the reliability of proteoform identification and quantification.
TopFD is a valuable resource for researchers utilizing top-down MS for proteomic studies.