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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 proteomics...

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Ribosome Profiling

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.
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Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Published on: April 18, 2025

Outlier-Based Differential Expression Analysis in Proteomics Studies.

Huy Vuong¹, Kerby Shedden, Yashu Liu

¹Bioinformatics Program, University of Michigan, MI, USA.

Journal of Proteomics & Bioinformatics

|September 28, 2011

Summary

This summary is machine-generated.

Statistical methods for cancer biomarker research are advancing. This study finds that outlier-based analysis requires strong differential expression patterns to outperform mean-based approaches in proteomics.

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Published on: September 18, 2021

Area of Science:

Biostatistics
Cancer Genomics
Proteomics

Background:

Cancer biomarker research focuses on identifying expression signatures for cancer heterogeneity.
Statistical methods like Cancer Outlier Profile Analysis (COPA) identify
cancer outlier genes
but their performance characteristics, especially in proteomics, are not fully understood.

Purpose of the Study:

To determine the required strength of outlier differential expression for outlier-based methods to surpass mean-based methods.
To propose a diagnostic method for characterizing differential expression across distribution tails and center.
To evaluate these methods in the context of typical proteomics study parameters.

Main Methods:

Simulation studies to compare outlier-based and mean-based differential expression analysis.
Development of a diagnostic procedure to assess expression distribution characteristics.
Application and analysis of proteomics data from a melanoma study.

Main Results:

Outlier-based analysis provides meaningful benefits only when the outlier pattern of differential expression is strong.
For typical proteomics sample and effect sizes, differential expression is often widespread rather than concentrated in the tails.
A minority of proteins exhibited outlier expression patterns in the melanoma data.

Conclusions:

The effectiveness of outlier-based statistical methods in proteomics is contingent on pronounced outlier expression.
Mean-based approaches may be more suitable than outlier-based methods in many proteomics scenarios due to widespread differential expression.
Further investigation into diagnostic procedures for expression data is warranted.