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Related Concept Videos

Proteomics01:33

Proteomics

9.3K
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...
9.3K

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

Jeffrey S Morris

    Statistics and Its Interface
    |July 2, 2013
    PubMed
    Summary

    This paper reviews computational methods for comparative proteomics, focusing on experimental design, data preprocessing, and statistical analysis for biomarker discovery. It highlights feature extraction and functional modeling approaches for mass spectrometry and 2D gel electrophoresis data.

    Keywords:
    2D Gel ElectrophoresisBayesian MethodsBiomarkersClassificationFalse Discovery RateFunctional Data AnalysisFunctional Mixed ModelsMALDI-TOFMass SpectrometryMultiple TestingNonparametric RegressionProteomicsReproducibilityRobust RegressionWavelets

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    Area of Science:

    • Molecular Biotechnology
    • Proteomics
    • Biomarker Discovery

    Background:

    • Biomarker discovery is crucial for medical outcomes, with proteomics playing a key role.
    • Proteomic technologies generate complex data requiring specialized analytical approaches.
    • Effective comparative proteomics studies face challenges in experimental design, preprocessing, and statistical analysis.

    Purpose of the Study:

    • To review computational aspects of comparative proteomic studies.
    • To summarize contributions in analyzing proteomics data for biomarker discovery.
    • To discuss challenges and methods in analyzing high-dimensional proteomic data.

    Main Methods:

    • Overview of comparative proteomics technologies.
    • Discussion of experimental design and preprocessing considerations.
    • Description of feature extraction (e.g., Cromwell for mass spectrometry, Pinnacle for 2D gels) and functional modeling (e.g., wavelet-based functional mixed models).

    Main Results:

    • Methods illustrated with mass spectrometry and 2D gel electrophoresis data.
    • Demonstration of feature extraction and functional modeling techniques.
    • Application of computational principles to specific proteomic technologies.

    Conclusions:

    • Computational methods are essential for effective comparative proteomics and biomarker discovery.
    • Both feature extraction and functional modeling offer valuable approaches to proteomics data analysis.
    • The discussed methods and principles are broadly applicable to various expression proteomics technologies.