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Immunoprecipitation

Immunoprecipitation

Immunoprecipitation, or IP, is a widely used technique that employs protein-antibody interactions to isolate proteins or protein complexes in their native state for studying protein-protein interactions, quaternary structures, or supramolecular complexes. Various modifications of the technique, including chromatin IP, cross-linking IP, and fluorescence IP, are commonly used.
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Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Instance based algorithm for posterior probability calculation by target-decoy strategy to improve protein

Xinning Jiang¹, Xiaoli Dong, Mingliang Ye

¹National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.

Analytical Chemistry

|June 25, 2009

Summary

This study introduces a new method to calculate the posterior probability of peptide identification using local false-discovery rate (FDR) from target-decoy searches, improving proteome data analysis sensitivity.

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Published on: November 3, 2011

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Target-decoy database searches are standard for global false-discovery rate (FDR) in proteomics.
Current methods often fail to provide confidence scores for individual peptide identifications.

Purpose of the Study:

To develop a method for calculating the posterior probability of individual peptide identifications.
To enhance the accuracy and sensitivity of peptide and protein identification in proteome research.

Main Methods:

Developed an approach to calculate posterior probability from local false-discovery rate (local FDR).
Weighted peptide identification scores using Shannon information entropy for discriminating power.
Calculated local FDR based on decoy identifications within a defined score space.

Main Results:

The calculated posterior probabilities precisely matched actual probabilities.
The method demonstrated strong discrimination between true and false positive peptide identifications.
Significantly improved sensitivity for both peptide and protein identification across various datasets.

Conclusions:

The proposed algorithm offers a reliable method for posterior probability calculation in proteomics.
This instance-based strategy is robust and adaptable to diverse proteome datasets.
Enhanced confidence in peptide identification leads to more accurate proteome analysis.