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SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling


Mueller, Lukas N; Rinner, Oliver; Schmidt, Alexander; Letarte, Simon; Bodenmiller, Bernd; Brusniak, Mi-Youn; Vitek, Olga; Aebersold, Ruedi; Müller, Markus (2007). SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics, 7(19):3470-3480.

Abstract

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

Abstract

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2007
Deposited On:24 Apr 2013 11:21
Last Modified:18 Feb 2018 13:07
Publisher:Wiley-Blackwell
ISSN:1615-9853
OA Status:Closed
Publisher DOI:https://doi.org/10.1002/pmic.200700057
PubMed ID:17726677

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