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Updated: Mar 31, 2026

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

Published on: May 4, 2022

Data Analysis Strategies for Protein Modification Identification.

¹National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road 55, Beijing, 100190, China. yfu@amss.ac.cn.

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

|November 1, 2015

Summary

This summary is machine-generated.

This chapter details mass spectrometry data analysis for identifying protein modifications. It covers advanced strategies like variable modification searches and false discovery rate control for accurate results.

Keywords:

False discovery rate Modification discovery Modification localization Protein modification s Variable modification search

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Area of Science:

Proteomics
Computational Biology
Biochemistry

Background:

Mass spectrometry (MS) is crucial for large-scale protein analysis.
Identifying protein modifications requires robust statistical and computational methods.
Effective data analysis is essential for accurate modification detection.

Purpose of the Study:

To present common and advanced data analysis strategies for protein modification identification using MS.
To provide insights into variable modification searches and unrestrictive discovery approaches.
To discuss false discovery rate (FDR) estimation and control for reliable results.

Main Methods:

Variable modification search algorithms.
Unrestrictive approaches for novel modification discovery.
False discovery rate (FDR) estimation and control methodologies.
Tools for protein modification site localization.

Main Results:

Detailed explanation of data analysis strategies for MS-based proteomics.
Guidance on implementing advanced search parameters for modification identification.
Methods for assessing and controlling the reliability of identified modifications.

Conclusions:

Effective statistical and computational analysis is paramount for MS-based protein modification identification.
Advanced strategies enhance the scope and accuracy of modification discovery.
Proper FDR control ensures the reliability of identified protein modifications and their sites.