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Updated: Mar 31, 2026

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

False Discovery Rate Estimation in Proteomics.

Suruchi Aggarwal¹, Amit Kumar Yadav²

¹Immunology Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India.

Methods in Molecular Biology (Clifton, N.J.)

|November 1, 2015

Summary

This summary is machine-generated.

Massively increasing proteomics data requires automated analysis. The chapter explains False Discovery Rate (FDR) for validating mass spectrometry results and controlling errors in high-throughput proteomics.

Keywords:

False discovery rate Peptide spectrum matches Posterior error probability Shotgun proteomics Statistical validation Target-decoy

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Last Updated: Mar 31, 2026

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Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

Area of Science:

Proteomics
Computational Biology
Mass Spectrometry

Background:

Advancements in proteomics separation techniques and mass analyzers lead to exponentially growing datasets.
High-throughput proteomics data analysis necessitates automated computational tools for efficient processing and statistical control.
Database search algorithms used in proteomics can produce false positive matches, requiring statistical validation for reliable biological interpretation.

Purpose of the Study:

To introduce the concept and importance of False Discovery Rate (FDR) in the context of large-scale proteomics datasets.
To explain the application of FDR in validating mass spectrometry-based proteomics data.
To cover fundamental principles and methods for estimating FDR in proteomics.

Main Methods:

Discussion of database search algorithms commonly employed in proteomics data analysis.
Explanation of the statistical challenge posed by the overlap between true and false positives.
Introduction to False Discovery Rate (FDR) as a metric for assessing global confidence in proteomics datasets.

Main Results:

FDR provides a statistical estimate to control the proportion of false positives within accepted matches.
Effective application of FDR is crucial for ensuring the validity of biological inferences derived from proteomics experiments.
Understanding FDR is essential for reliable interpretation of complex, high-volume proteomics data.

Conclusions:

Automated computational tools and statistical validation methods like FDR are indispensable for modern high-throughput proteomics.
FDR serves as a critical metric for global confidence assessment, mitigating misleading biological interpretations.
This chapter provides foundational knowledge on FDR, its estimation, and its vital role in proteomics.