Software:SuperHirn

From SPCTools

Revision as of 14:15, 30 January 2009; view current revision
←Older revision | Newer revision→
Jump to: navigation, search

Contents

NEWS

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.)

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:

  • For questions, suggestions and general comments visit the Google Groups "SuperHirn" .
  • 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, Mueller LN, 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 LN, 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


About SuperHirn Parameters

Description about the most important SuperHirn parameters:

The following table describes the most important SuperHirn processing parameters. These are stored in the Root-Parameter file and mostly optimized for FT profile data. To modify a parameter, do as following:

1.) copy the parameter in this format to your param.def file:

MS1 retention time tolerance=1.0

2.) adjust the parameters as you wish:

MS1 retention time tolerance=2.0

3.) run SuperHirn

4.) if you do not need the parameter anymore, just delete it or comment it out by //MS1 retention time tolerance ...


Parameters:

Parameter name Description Suggested Value Comment
General:
MS1 retention time tolerance RT tolerance between MS1 features used for the alignment (min) 1.0 -
MS1 m/z tolerance Mass tolerance between MS1 features used for the alignment (ppm) 10 -
MS2 PPM m/z tolerance Mass tolerance for annotation of MS1 features with MS/MS identifications (ppm) 20 -
MS2 mass matching modus if theoretical peptide mass (1) or precursor mass (0) used to mapp MS/MS ids to MS1 features 0 -
Peptide Prophet Threshold Peptide Prophet Threshold, see peptide prophet paper 0.9 -
MS2 SCAN tolerance Scan tolerance for annotation of MS1 features with MS/MS identifications (# scans) 100 -
MS2 retention time tolerance RT tolerance for annotation of MS1 features with MS/MS identifications (min), if set to -1.0, then value from MS1 retention time tolerance parameter used -1.0 -
INCLUSIONS LIST MS2 SCAN tolerance Scan tolerance for annotation of MS1 features with inclusion list MS/MS identifications (# scans) 100000 -
LC-MS Alignment:
retention time window RT window to search for common MS1 feature across LC-MS runs before the alignment, i.e. maximal RT shift possible (min) 5.0 -
mass / charge window Mass window to search for common MS1 feature across LC-MS runs before the alignment, i.e. maximal mass shift possible (ppm) 20 -
Peak Detection:
MS1 external isotopic distribution file Path to XML file containing external Isotopic Peptide Distributions, use only if abnormal peptide distributions expected "" -
MS1 data centroid data If MS1 data is in centroid (1) or raw (0) format 0 -
Save MS/MS sequenced MS1 monoisotopic peaks Option to keep detected Monoisotopic peaks which have been selected for MS/MS but do NOT fullfill the LC elution peak criteria (i.e. times detected, ∆RT between the detected peaks): on(1) or off(0) 1 -
Precursor detection scan levels MS scan from which level should be submitted for peak extraction, example for scan level 1 and 2: 1,2 1 -
FT peak detect MS1 m/z tolerance Mass tolerance to cluster detected monoisotopic peaks into RT elution clusters, i.e. MS1 featuers (ppm) 10 -
MS1-to-MS2 precursor tolerance Mass tolerance to associate MS/MS precursors to extracted mono isotopic peaks (ppm) 15 -
FT peak detect MS1 min nb peak members Minimal number of detected mono isotopic peaks for a MS1 feature 4 -
MS1 max inter scan distance Maximal allowed RT distance between detected mono isotopic peak (min) 0.2 -
FT peak detect MS1 intensity min threshold Minimal intensity of a detected mono isotopic peak (counts) 1000 -
Absolute isotope mass precision Mass precision used in the detection of isotopic distributions (Da) 0.01 -
Relative isotope mass precision Mass precision used in the detection of isotopic distributions (ppm) 10 -
IntensityCV Coefficient of variance used to correlate a theoretical isotopic distributions to the observed one, i.e. the closer to one the better these two distributions need to coorelate 0.9 -
Detectable isotope factor At which % of the highest Isotope other isotopes to not need to be detected anymore 0.1 -
Min. RAW MS Signal Intensity Minimal intensity of a raw signal (before centroiding) 10 -
Minimal peak height Minimal intensity of a peak signal (after centroiding) 0 -
MS1 Feature Merging:
Activation of MS1 feature merging post processing Turn peak merging on(1) or off(0) 1 -
PPM value for the m/z clustering of merging candidates Mass tolerance to merge MS1 features (ppm) 10 -
Initial Apex Tr tolerance RT window to search for MS1 feature candidates which should be merged (min) 5.0 -
MS1 feature Tr merging tolerance Final RT tolerance for MS1 feature to be merged (min), measured from their start/end elution points 1.0 -
Percentage of intensity variation between LC border peaks Maximal log10 Intensity variation between start/end elution points of 2 features which should be merged 50.0 -
KMeans Profile Clustering Parameters :
number of clusters Number of initial start clusters, really depends on how many number of profile trends (or biological groups) you expect from your data -
min. nb. of profile data points How many profile points a MS1 features needs (same as how many times aligned) to be integrated into the clustering analysis -
min. nb. of cluster members How many members a build cluster needs minimally to survive the next clustering iteration -
Personal tools