Tool to estimate the probability of peptide hits to be incorrectly assigned.
| potential predecessor tools |  IDPosteriorErrorProbability | potential successor tools |
| MascotAdapter (or other ID engines) | ConsensusID |
- Experimental classes:
- This tool has not been tested thoroughly and might behave not as expected!
By default an estimation is performed using the (inverse) Gumbel distribution for incorrectly assigned sequences and a Gaussian distribution for correctly assigned sequences. The probabilities are calculated by using Bayes' law, similar to PeptideProphet. Alternatively, a second Gaussian distribution can be used for incorrectly assigned sequences. At the moment, IDPosteriorErrorProbability is able to handle X!Tandem, Mascot, MyriMatch and OMSSA scores.
No target/decoy information needs to be provided, since the model fits are done on the mixed distribution.
In order to validate the computed probabilities one can adjust the fit_algorithm subsection.
There are three parameters for the plot: The parameter 'output_plots' is by default false. If set to true the plot will be created. The scores are plotted in form of bins. Each bin represents a set of scores in a range of (highest_score - smallest_score)/number_of_bins (if all scores have positive values). The midpoint of the bin is the mean of the scores it represents. Finally, the parameter output_name should be used to give the plot a unique name. Two files are created. One with the binned scores and one with all steps of the estimation. If top_hits_only is set, only the top hits of each PeptideIndentification are used for the estimation process. Additionally, if 'top_hits_only' is set, target_decoy information are available and a False Discovery Rate run was performed before, an additional plot will be plotted with target and decoy bins(output_plot must be true in fit_algorithm subsection). A peptide hit is assumed to be a target if its q-value is smaller than fdr_for_targets_smaller.
Actually, the plots are saved as a gnuplot file. Therefore, to visualize the plots one has to use gnuplot, e.g. gnuplot file_name. This should output a postscript file which contains all steps of the estimation.
The command line parameters of this tool are:
IDPosteriorErrorProbability -- Estimates probabilities for incorrectly assigned peptide sequences and a set of search engine scores using a mixture model.
Version: 1.10.0 Mar 7 2013, 08:30:22, Revision: unknown
Usage:
IDPosteriorErrorProbability <options>
This tool has algoritm parameters which can only be used via an INI file and are not accessible from the command line!
Options (mandatory options marked with '*'):
-in <file>* Input file (valid formats: 'idXML')
-out <file>* Output file (valid formats: 'idXML')
-output_name <file>* Gnuplot file as txt (valid formats: 'txt')
-split_charge The search engine scores are split by charge if this flag is set. Thus, for each char
ge state a new model will be computed.
-top_hits_only If set only the top hits of every PeptideIdentification will be used
-ignore_bad_data If set errors will be written but ignored. Useful for pipelines with many datasets
where only a few are bad, but the pipeline should run through.
-prob_correct If set scores will be calculated as 1-ErrorProbabilities and can be interpreted as
probabilities for correct identifications.
Common TOPP options:
-ini <file> Use the given TOPP INI file
-threads <n> Sets the number of threads allowed to be used by the TOPP tool (default: '1')
-write_ini <file> Writes the default configuration file
--help Shows options
--helphelp Shows all options (including advanced)
The following configuration subsections are valid:
- fit_algorithm Algorithm parameter subsection
You can write an example INI file using the '-write_ini' option.
Documentation of subsection parameters can be found in the
doxygen documentation or the INIFileEditor.
Have a look at OpenMS documentation for more information.
INI file documentation of this tool:
For the parameters of the algorithm section see the algorithms documentation:
fit_algorithm