Software:SuperHirn

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Contents

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

Developers

Other Stuff

Das SuperHirn:: http://www.youtube.com/watch?v=LPj6cfX_U9o

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