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Related Concept Videos

Proteomics01:33

Proteomics

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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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Related Experiment Video

Updated: Apr 14, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Can "normal" protein expression ranges be estimated with high-throughput proteomics?

Roger Higdon1,2, Eugene Kolker1,2,3,4

  • 1†Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington 98101, United States.

Journal of Proteome Research
|April 17, 2015
PubMed
Summary
This summary is machine-generated.

Defining normal protein expression is crucial for biological discovery. This study quantizes protein variability in normal tissues, establishing a foundation for accurate proteomics comparisons.

Keywords:
gene expressionnormal rangenormal tissueprotein expressionproteomicsspectral countsvariability

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Area of Science:

  • Proteomics
  • Molecular Biology
  • Systems Biology

Background:

  • Understanding normal biological states is fundamental for scientific discovery.
  • High-throughput proteomics studies provide data to assess normal protein expression ranges.
  • Limited knowledge exists regarding normal protein expression levels and variability.

Purpose of the Study:

  • To estimate technical and biological variability in protein expression using data from Human Proteome studies.
  • To compare protein expression variability in normal tissues with other datasets.
  • To establish a foundation for defining normal protein ranges in proteomics.

Main Methods:

  • Utilized data from two Nature-featured Human Proteome studies.
  • Estimated technical and biological variability in protein expression.
  • Compared variability across different tissue experiments and datasets.
  • Analyzed the impact of unique peptide identifications on measurement variability.
  • Selected housekeeping proteins and genes for normalization utility.

Main Results:

  • Measured protein expression in same-tissue replicates varied by ±4- to 10-fold for most proteins.
  • Coefficients of variation (CV) ranged from 62% to 117%, reduced by up to 50% after adjusting for technical variation.
  • CV decreased by 33% on average for proteins with ≥3 unique peptide identifications.
  • Identified 13 housekeeping proteins/genes with low variability across tissues.

Conclusions:

  • This study provides the first estimates of normal protein ranges by quantifying expression variability.
  • Adjusting for technical variation and using proteins with more peptide identifications reduces measurement variability.
  • Replicates of normal tissues and standardized protocols are essential for estimating normal protein ranges.
  • The findings support the development of a valuable resource for normal cellular physiology and comparative proteomics.