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Target-decoy false discovery rate estimation using Crema.

Andy Lin1, Donavan See2, William E Fondrie3

  • 1Chemical and Biological Signatures, Pacific Northwest National Laboratory, Seattle, Washington, USA.

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|February 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Crema, a Python tool for estimating false discovery rates (FDR) in proteomics. It simplifies target-decoy competition (TDC) analysis for more reliable experimental interpretation.

Keywords:
FDR controlfalse discovery ratetarget‐decoy competition

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Area of Science:

  • Proteomics
  • Computational Biology
  • Statistical Analysis

Background:

  • Statistical confidence estimates are crucial for interpreting tandem mass spectrometry proteomics results.
  • Target-decoy competition (TDC) is a common method for estimating these confidence levels.
  • Existing TDC implementations can be complex and prone to errors.

Purpose of the Study:

  • To present Crema, an open-source Python tool for robust false discovery rate (FDR) estimation.
  • To simplify and standardize the application of TDC methods in proteomics research.
  • To improve the reliability of FDR estimation at spectrum, peptide, and protein levels.

Main Methods:

  • Crema implements various target-decoy competition (TDC) methods.
  • The tool supports FDR estimation at spectrum, peptide, and protein levels.
  • Crema is designed for compatibility with diverse database search tools.

Main Results:

  • Crema provides a straightforward approach to obtaining reliable FDR estimates.
  • The tool addresses common implementation pitfalls in TDC procedures.
  • Facilitates principled interpretation of proteomics experimental outcomes.

Conclusions:

  • Crema offers a valuable resource for the proteomics community.
  • Standardized and robust FDR estimation enhances the validity of proteomics discoveries.
  • The tool aids in assessing the cost-benefit of experimental follow-up.