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

• Published in: Proteomics (Weinheim) Vol. 7; no. 19; pp. 3470 - 3480
• Main Authors: Mueller, Lukas N, Rinner, Oliver, Schmidt, Alexander, Letarte, Simon, Bodenmiller, Bernd, Brusniak, Mi-Youn, Vitek, Olga, Aebersold, Ruedi, Müller, Markus
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley-VCH Verlag 01.10.2007; WILEY-VCH Verlag; WILEY‐VCH Verlag; Wiley-VCH
• Subjects: Analytical, structural and metabolic biochemistry; Animals; Biological and medical sciences; Chromatography, Liquid - instrumentation; Chromatography, Liquid - methods; Databases, Protein; Fundamental and applied biological sciences. Psychology; Humans; Label-free quantification; LC-MS; Mass Spectrometry - instrumentation; Mass Spectrometry - methods; Miscellaneous; Peptides - analysis; Protein profiling; Proteins; Proteins - analysis; Proteome - analysis; Proteomics - instrumentation; Proteomics - methods; Quantitative proteomics; Software; LC-MS; Quantitative proteomics; Peptides; Proteomics; Protein profiling; Protein; Label-free quantification; Quantitative analysis
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.200700057

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.