A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information.
Repository Usage Stats
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.
Escherichia coli Proteins
Spectrometry, Mass, Electrospray Ionization
Tandem Mass Spectrometry
Published Version (Please cite this version)10.1186/1471-2105-14-364
Publication InfoBenjamin, Ashlee M; Thompson, J Will; Soderblom, Erik J; Geromanos, Scott J; Henao, Ricardo; Kraus, Virginia B; ... Lucas, Joseph E (2013). A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information. BMC Bioinformatics, 14. pp. 364. 10.1186/1471-2105-14-364. Retrieved from https://hdl.handle.net/10161/10867.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record
Assistant Professor in Biostatistics and Bioinformatics
Professor of Medicine
My special area of expertise is as a clinician scientist investigating osteoarthritis. Osteoarthritis is the most common form of joint disease in man and its incidence increases with age. It is a problem of increasing concern to the medical community due to the increasing longevity of the population. Trained as a molecular biologist and a Rheumatologist, I endeavor to study this disease from bedside to bench. The work in this laboratory focuses on osteoarthritis and deals w
Associate Research Professor in the Social Science Research Institute
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Associate Professor in Medicine
Assistant Research Professor of Cell Biology
Assistant Research Professor 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 is the Assistant Director of the Proteomics and Metabolomics Shared Resource in the Duke School of Medicine. In this role, he enjoys utilizing mass spectrometry 'omics techniques in research collaborations with investigators throughout the Duke community.
Alphabetical list of authors with Scholars@Duke profiles.