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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 10, 2025

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Published on: June 14, 2017

PrecisionProDB: improving the proteomics performance for precision medicine.

Xiaolong Cao¹, Jinchuan Xing¹

¹Department of Genetics, Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

Bioinformatics (Oxford, England)

|March 31, 2021

Summary

This summary is machine-generated.

Generating personalized protein databases using PrecisionProDB enhances proteomics by improving peptide identification. This approach addresses limitations of standard databases for individual genomics and transcriptomics data.

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Last Updated: Nov 10, 2025

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

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Published on: June 14, 2017

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

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Area of Science:

Proteomics
Genomics
Transcriptomics

Background:

Next-generation sequencing is widely used in research and clinical settings.
Proteomics faces challenges due to individual-specific genetic variations impacting protein identification.
Standard protein databases lack personalization, limiting accuracy in individual proteomics analysis.

Purpose of the Study:

To develop a computational tool for creating personalized protein databases.
To bridge the gap between genomics/transcriptomics and proteomics data analysis.
To improve peptide detection power and accuracy in proteomics.

Main Methods:

Designed and implemented PrecisionProDB, a Python package for personalized protein database generation.
Supported multiple file formats and reference databases.
Generated population-specific and cell line-specific protein databases.

Main Results:

PrecisionProDB generated personalized databases rapidly.
Human population-specific databases improved peptide identification by 0.34% on average.
Incorporating cell line variants improved peptide identification by 0.71% in Jurkat cells.

Conclusions:

Personalized protein databases significantly enhance proteomics data analysis.
PrecisionProDB offers a practical solution for researchers and clinicians to improve peptide search performance.
Adopting representative and personalized databases minimizes effort while maximizing identification accuracy.