Software:SuperHirn

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1 Description
2 Avaliablity
3 Reference
4 Developers
5 Other Stuff

Description

SuperHirn is a novel tool to quantitatively analyze multi dimensional LC-MS data in a label-free approach and was developed by the group of Prof. Ruedi Aebersold at the Institute of Molecular Systems Biology (ETHZ, Switzerland). The software is programmed in C++ and is compatible with Unix platforms (tested on Linux and OSX). LC-MS data are preprocessed by a MS1 feature extraction routine and the different LC-MS runs are then combined by a multi dimensional LC-MS alignment into a general repository called MasterMap. SuperHirn then offers several modules for post data analysis of the MasterMap:

LC-MS similarity analysis: Binary similarity analysis of LC-MS runs (intensity reproducibility, feature overlap)
Feature intensity normalization: global MS1 feature intensity normalization across LC-MS runs
Unsupervised feature profiling: Kmeans cluster analysis of MS1 features
Targeted peptide/protein profiling: Correlate peptide/protein profile vs. a given target profile
MS1 feature annotation: Annotation of MS1 features in the MasterMap (inclusion list etc.)

Avaliablity

The source code of SuperHirn can now be downloaded from the download page: go to download page

Supporting material to this software:

To access the benchmark Latin Square profiling data from the SuperHirn technical manuscript (Mueller et al.), follow this link.
For more details about SuperHirn, please read the corresponding publication (Mueller et al.) or download the SuperHirn User Manual.
For an example data set of SuperHirn, please download from this link: Example Test Set.
For additional readings for experimental wetlab procedures in combination with SuperHirn data processing: Experimental Tips.

Reference

Software Article:

Mueller, LN, Rinner, O, Schmidt, A, Letarte, S, Bodenmiller, B, Brusniak, MY, Vitek, O, Aebersold, R and Muller, M, SuperHirn - a novel tool for high resolution LC-MS based peptide/protein profiling, Proteomics, accepted for publication (2007) go to article

Applications of SuperHirn:

Mueller, LN and Rinner, O, Hubálek, M, Müller, M, Gstaiger, M and Aebersold, R, An integrated mass spectrometric and computational framework for the comprehensive analysis of protein interaction networks, Nature Biotechnology 25, 345 - 352 (2007) go to article
Bodenmiller, B, Mueller, LN, Mueller, M, Domon, B and Aebersold, R, Reproducible Isolation of Distinct, Overlapping Segments of the Phospho-Proteome. Nature Methods - 4, 231 - 237 (2007) go to article
Rinner, O, Seebacher, J, Walzthoeni, T, Mueller, LN, Beck, M, Schmidt, A, Mueller, M, Aebersold, R, Identification of cross-linked peptides from large sequence databases. Nature Methods - 5, 315 - 323 (2008) go to article
Schmidt A, Gehlenborg N, Bodenmiller B, Mueller LN, Campbell D, Mueller M, Aebersold R, Domon B., An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures. MCP, 2008 Nov;7(11):2138-5 go to article
Schiess R, Mueller LN, Mueller M, Wollscheid, B, Aebersold R, Analysis of cell surface proteome changes via label-free, quantitative mass spectrometry. MCP, 2008 Nov;7(11):2138-5 go to article
Mueller LN, Brusniak M, Mani DR, Aebersold R, An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res. 2008 Jan;7(1):51-61 go to article
Urwyler S, Nyfeler Y, Ragaz C, Lee H, Müller L, Aebersold R, Hilbi H, Proteome analysis of Legionella vacuoles purified by magnetic immuno-separation reveals secretory and endosomal GTPases. Traffic 2008 Oct 29, AOP, go to article
Letarte S, Brusniak M, Campbell D, Eddes J, Kemp C, Lau H, Mueller L, Schmidt A, Shannon P, Kelly-Spratt, Vitek O, Zhang H, Aebersold R, Watts J, Differential Plasma Glycoproteome of p19ARF Skin Cancer Mouse Model Using the Corra Label-Free LC-MS Proteomics Platform. Clinical Proteomics, Volume 4, Numbers 3-4 / December, 2008, go to article