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Related Concept Videos

Proteomics01:33

Proteomics

7.6K
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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

Updated: Aug 6, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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Statistical and Computational Methods for Proteogenomic Data Analysis.

Xiaoyu Song1,2,3

  • 1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. xiaoyu.song@mountsinai.org.

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a pipeline for high-quality proteomics data analysis, including preprocessing and statistical methods for biological discovery. It demonstrates these techniques using lung cancer proteogenomic data.

Keywords:
Integrative proteogenomic analysisMass spectrometryPreprocessing and quality controlProteomics

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Area of Science:

  • Proteomics
  • Genomics
  • Bioinformatics

Background:

  • Proteins are key to cellular processes and drug targets, but their levels and modifications (PTM) poorly correlate with mRNA expression.
  • Proteomics data analysis is crucial for understanding biological mechanisms and developing therapeutics.
  • Current mass spectrometry-based proteomics yields relative data prone to errors like outliers, batch effects, and missing values.

Purpose of the Study:

  • To present a robust data preprocessing and quality control pipeline for proteomics.
  • To describe statistical methods for scientific discovery using processed proteomics data, integrating genomics and transcriptomics.
  • To demonstrate these methods with real-world proteogenomic data from lung squamous cell carcinoma.

Main Methods:

  • Data preprocessing: normalization, outlier detection/removal, batch effect handling, missing data imputation.
  • Statistical analysis: association analysis, network construction, clustering, cell-type deconvolution.
  • Application: utilizing Clinical Proteomic Tumor Analysis Consortium (CPTAC) lung squamous cell carcinoma proteogenomic data.

Main Results:

  • A comprehensive pipeline for high-quality proteomics data analysis was established.
  • Statistical methods were detailed for generating discoveries through integrated multi-omics data.
  • Demonstration with lung cancer data provided sample code for access and analysis.

Conclusions:

  • High-quality proteomics data analysis is essential for biological insights and therapeutic development.
  • Integrated proteogenomic analysis offers powerful approaches for scientific discovery.
  • The presented pipeline and methods are applicable to complex cancer proteogenomic datasets.