Percolator version 1.08, Build Date Jan 12 2009 16:12:26
Copyright (c) 2006-9 University of Washington. All rights reserved.
Written by Lukas Käll (lukall@u.washington.edu) in the
Department of Genome Sciences at the University of Washington.

Usage:
percolator [options] normal shuffle [[shuffled_treshhold] shuffled_test]
or percolator [options] -P pattern normal_and_shuffled.sqt
or percolator [options] -g gist.data gist.label

where normal is the normal sqt-file,
shuffle the shuffled sqt-file used in the training,
shuffle_test is an otional second shuffled sqt-file for q-value calculation
shuffle_treshhold is an otional shuffled sqt-file for determine q-value treshold

To be able to merge small data set one can replace the sqt-files with meta
files. Meta files are text files containing the paths of sqt-files, one path
per line. For successful result, the different runs should be generated under
similair condition. Particulary, they need to be generated with the same protease.
Options:

-h, --help	Display this message
-o <filename>, --sqt-out <filename>	Create an SQT file with the specified name from the given target SQT file, replacing the XCorr value the learned score and Sp with the negated q-value.
-s <filename>, --shuffled <filename>	Same as -o, but for the decoy SQT file
-P <pattern>, --pattern <pattern>	Option for single SQT file mode defining the name pattern used for shuffled data base. Typically set to random_seq
-p <value>, --Cpos <value>	Cpos, penalty for mistakes made on positive examples. Set by cross validation if not specified.
-n <value>, --Cneg <value>	Cneg, penalty for mistakes made on negative examples. Set by cross validation if not specified or -p not specified.
-F <value>, --trainFDR <value>	False discovery rate threshold to define positive examples in training. Set by cross validation if 0. Default is 0.01.
-t <value>, --testFDR <value>	False discovery rate threshold for evaluating best cross validation result and the reported end result. Default is 0.01.
-i <number>, --maxiter <number>	Maximal number of iterations
-m <number>, --matches <number>	Maximal number of matches to take in consideration per spectrum when using sqt-files
-f <value>, --train-ratio <value>	Fraction of the negative data set to be used as train set when only providing one negative set, remaining examples will be used as test set. Set to 0.6 by default.
-G <trunc name>, --gist-out <trunc name>	Output the computed features to the given file in tab-delimited format. A file with the features, named .data, and a file with the labels named .label will be created
-g, --gist-in	Input files are given as gist files. In this case first argument should be a file name of the data file, the second the label file. Labels are interpreted as 1 -- positive train and test set, -1 -- negative train set, -2 -- negative in test set.
-J <file name>, --tab-out <file name>	Output the computed features to the given file in tab-delimited format. A file with the features with the given file name will be created
-j, --tab-in	Input files are given as a tab delimited file. In this case the only argument should be a file name of the data file. The tab delimited fields should be id label feature1 ... featureN peptide proteinId1 .. proteinIdM Labels are interpreted as 1 -- positive train and test set, -1 -- negative train set, -2 -- negative in test set.When the --doc option the first and second feature (third and fourth column) should contain the retention time and difference between observed and calculated mass
-w <filename>, --weights <filename>	Output final weights to the given file
-W <filename>, --init-weights <filename>	Read initial weights from the given file
-V <featureNum>, --default-direction <featureNum>	The most informative feature given as feature number, can be negated to indicate that a lower value is better.
-v <level>, --verbose <level>	Set verbosity of output: 0=no processing info, 5=all, default is 2
-r <filename>, --result <filename>	Output result file (score ranked labels) to given filename
-u, --unitnorm	Use unit normalization [0-1] instead of standard deviation normalization
-a, --aa-freq	Calculate amino acid frequency features
-b, --PTM	Calculate feature for number of post-translational modifications
-d, --DTASelect	Add an extra hit to each spectra when writing sqt files
-R, --test-each-iteration	Measure performance on test set each iteration
-Q, --quadratic	Calculate quadratic feature terms
-O, --override	Override error check and do not fall back on default score vector in case of suspect score vector
-I, --intra-set	Depricated switch --- Turn Off calculation of intra-set features
-y, --notryptic	Turn off calculation of tryptic/chymo-tryptic features.
-c, --chymo	Replace tryptic features with chymo-tryptic features.
-e, --elastase	Replace tryptic features with elastase features.
-x, --whole-xval	Select hyper parameter cross validation to be performed on whole iterating procedure, rather than on each iteration step.
-S <value>, --seed <value>	Setting seed of the random number generator. Default value is 0
-2 <filename>, --ms2-file <filename>	File containing spectra and retention time. The file could be in mzXML, MS2 or compressed MS2 file.
-M, --isotope	Mass difference calculated to closest isotope mass rather than to the average mass.
-K, --klammer	Retention time features calculated as in Klammer et al.
-D, --doc	Include description of correct features.
-B <filename>, --decoy-results <filename>	Output results for decoys into a tab delimited file
-X <filename>, --xml-output <filename>	Output results in xml-format into a file