A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information.

Abstract

BACKGROUND: The goal of many proteomics experiments is to determine the abundance of proteins in biological samples, and the variation thereof in various physiological conditions. High-throughput quantitative proteomics, specifically label-free LC-MS/MS, allows rapid measurement of thousands of proteins, enabling large-scale studies of various biological systems. Prior to analyzing these information-rich datasets, raw data must undergo several computational processing steps. We present a method to address one of the essential steps in proteomics data processing--the matching of peptide measurements across samples. RESULTS: We describe a novel method for label-free proteomics data alignment with the ability to incorporate previously unused aspects of the data, particularly ion mobility drift times and product ion information. We compare the results of our alignment method to PEPPeR and OpenMS, and compare alignment accuracy achieved by different versions of our method utilizing various data characteristics. Our method results in increased match recall rates and similar or improved mismatch rates compared to PEPPeR and OpenMS feature-based alignment. We also show that the inclusion of drift time and product ion information results in higher recall rates and more confident matches, without increases in error rates. CONCLUSIONS: Based on the results presented here, we argue that the incorporation of ion mobility drift time and product ion information are worthy pursuits. Alignment methods should be flexible enough to utilize all available data, particularly with recent advancements in experimental separation methods.

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Provenance

Citation

Published Version (Please cite this version)

10.1186/1471-2105-14-364

Publication Info

Benjamin, Ashlee M, J Will Thompson, Erik J Soderblom, Scott J Geromanos, Ricardo Henao, Virginia B Kraus, M Arthur Moseley, Joseph E Lucas, et al. (2013). A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information. BMC Bioinformatics, 14. p. 364. 10.1186/1471-2105-14-364 Retrieved from https://hdl.handle.net/10161/10867.

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Scholars@Duke

Thompson

J. Will Thompson

Adjunct Assistant Professor in the Department of Pharmacology & Cancer Biology

Dr. Thompson's research focuses on the development and deployment of proteomics and metabolomics mass spectrometry techniques for the analysis of biological systems. He served as the Assistant Director of the Proteomics and Metabolomics Shared Resource in the Duke School of Medicine from 2007-2021. He currently maintains collaborations in metabolomics and proteomics research at Duke, and develops new tools for chemical analysis as a Principal Scientist at 908 Devices in Carrboro, NC.

Soderblom

Erik James Soderblom

Associate Research Professor of Cell Biology

Director, Proteomics and Metabolomics Core Facility

Henao

Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics
Moseley

Martin Arthur Moseley

Adjunct Professor in the Department of Cell Biology

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