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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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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Analyzing Peptide Microarray Data with the R pepStat Package.

Gregory Imholte¹, Renan Sauteraud¹, Raphael Gottardo²

¹Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, Seattle, WA, 98109, USA.

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

|October 23, 2015

Summary

This summary is machine-generated.

This chapter details using R Bioconductor packages for analyzing paired peptide microarray data from vaccine trials. It introduces signal amplification methods and decision rules for subject responses.

Keywords:

Background correction Baseline correction Data visualization Decision rule False discovery rate Normalization Sliding mean Smoothing

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Area of Science:

Bioinformatics
Immunology
Statistical Analysis

Background:

Peptide microarrays are crucial for vaccine development, enabling high-throughput analysis of immune responses.
Analyzing complex microarray data requires robust statistical methods and specialized software tools.

Purpose of the Study:

To demonstrate the application of R Bioconductor packages (pepStat and Pviz) for analyzing paired peptide microarray data.
To present methods for data preprocessing, signal enhancement, and response interpretation in vaccine trial datasets.

Main Methods:

Data import, background correction, normalization, and summarization using R Bioconductor packages.
Implementation of a sliding mean method for signal amplification and noise reduction.
Development of a decision rule for subject/peptide response assessment using control or placebo data.

Main Results:

Successful application of pepStat and Pviz for processing and visualizing paired peptide microarray data.
Demonstration of improved signal-to-noise ratio through the sliding mean method.
Establishment of a practical method for identifying significant subject/peptide responses.

Conclusions:

R Bioconductor packages provide a powerful framework for analyzing peptide microarray data in vaccine research.
The sliding mean method enhances data quality and aids in identifying meaningful immune responses.
The proposed decision rule facilitates objective interpretation of subject responses in clinical trials.