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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Updated: Mar 22, 2026

Analysis of Histone Antibody Specificity with Peptide Microarrays

Analysis of Histone Antibody Specificity with Peptide Microarrays

Published on: August 1, 2017

Bayesian hierarchical modeling for subject-level response classification in peptide microarray immunoassays.

Gregory Imholte¹, Raphael Gottardo²

¹Department of Statistics, University of Washington, Seattle, Washington, U.S.A.

|April 11, 2016

Summary

This summary is machine-generated.

We developed pepBayes, a robust Bayesian model for analyzing noisy peptide microarray data. This method accurately identifies antibody responses to vaccines or infections, even with individual immune system variations.

Keywords:

Bayesian hierarchical model Classification Mixture modeling Peptide microarray

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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

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Published on: March 25, 2014

Area of Science:

Immunology
Bioinformatics
Statistical Modeling

Background:

Peptide microarrays are high-throughput tools for mapping antibody binding to pathogen antigens.
Assay steps can introduce noise and outliers, complicating data interpretation.
Individual immune responses to stimuli like vaccines vary significantly.

Purpose of the Study:

To develop a robust statistical model for analyzing peptide microarray data.
To accurately estimate the probability of antibody response for each subject and peptide.
To address noise, outliers, and inter-subject variability in immunoassay data.

Main Methods:

A Bayesian hierarchical model, pepBayes, was developed for peptide microarray experiments.
Heavy-tailed error distributions were used to handle outliers and extreme responses.
Random effect terms were incorporated to account for technical assay effects.

Main Results:

The pepBayes model demonstrated high sensitivity and specificity in detecting vaccine-induced antibody responses.
An adaptive thresholding classification method showed appropriate false discovery rate control.
Receiver operating characteristic analysis indicated clear separation between responses and non-responses.

Conclusions:

The pepBayes model provides a robust approach for analyzing peptide microarray data.
The model effectively handles technical variability and biological heterogeneity.
This method enhances the accuracy of identifying antibody responses in immunological studies.