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Related Experiment Videos

Large scale analysis of MASCOT results using a Mass Accuracy-based THreshold (MATH) effectively improves data

Paul A Rudnick1, Yueju Wang, Erin Evans

  • 1Calibrant Biosystems, 7507 Standish Pl., Rockville, MD 20855, USA. paul.rudnick@calibrant.com

Journal of Proteome Research
|August 9, 2005
PubMed
Summary

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Optimizing mass tolerance settings and database size is crucial for MASCOT sensitivity and specificity in proteomics. An empirical threshold improves identification accuracy by reducing false negatives and positives.

Area of Science:

  • Proteomics
  • Bioinformatics
  • Mass Spectrometry

Background:

  • MASCOT is a widely used search engine for peptide identification in mass spectrometry.
  • Mass tolerance settings and database size significantly impact the accuracy of MASCOT results.
  • The MASCOT Identity Threshold is used to assess the confidence of peptide identifications.

Purpose of the Study:

  • To investigate the impact of mass tolerance and database size on MASCOT sensitivity and specificity.
  • To evaluate the effectiveness of the MASCOT Identity Threshold for QqTOF data.
  • To develop and assess an improved method for identifying false positives and negatives.

Main Methods:

  • Heuristic approach to analyze MASCOT performance with varying parameters.
  • Application of Bayes' Law to calculate predictive values.

Related Experiment Videos

  • Development of an empirical threshold using reversed database searches and a novel false positive identification algorithm.

    • Main Results:

    • Increased mass tolerances negatively affect MASCOT specificity and sensitivity.
    • A small search database (Human IPI) combined with MASCOT Identity Threshold yielded a high positive predictive value (0.993).
    • An empirical threshold reduced false negative identifications by 17% and limited false positives to below 5%.

    Conclusions:

    • MASCOT performance is sensitive to mass tolerance settings and database size.
    • An empirical threshold strategy significantly enhances the reliability of peptide identifications.
    • This approach offers a more robust method for large-scale proteomic data analysis.