Software:SuperHirn

SuperHirnLogoThumb.jpg

News

SuperHirn v0.3 is now officially available since end of this january. The software is still maintained by the group of Prof. Ruedi Aebersold at the Institute of Molecular Systems Biology (ETHZ, Switzerland). We would like to acknowledge all people who helped to test the new version, provided use with ideas and inputs to improve SuperHirn and reported back bugs. Below is a list of new features which are now available in version 0.3:

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:

Avaliablity

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

Supporting material to this software:

Reference

Software Article:

Prot.jpg

Applications of SuperHirn:

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

-