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TopPerc (version 2.2.0.1)

Path to the percolator binary:

(-percolator_executable)

Input target file:

(-in_target)

Input decoy file:

(-in_decoy)

Type of enzyme: no_enzyme,elastase,pepsin,proteinasek,thermolysin,chymotrypsin,lys-n,lys-c,arg-c,asp-n,glu-c,trypsin:

(-enzyme)

read xml-input format (pin) from standard input:

(-e)

Include decoys (PSMs, peptides and/or proteins) in the xml-output:

(-Z) Only available if -X is used

Cpos, penalty for mistakes made on positive examples:

(-p) Set by cross validation if not specified

Cneg, penalty for mistakes made on negative examples:

(-n) Set by cross validation if not specified

False discovery rate threshold to define positive examples in training:

(-F) Set by cross validation if 0. Default is 0.01

False discovery rate threshold for evaluating best cross validation result and the reported end result:

(-t) Default is 0.01

Maximal number of iterations:

(-i)

Quicker execution by reduced internal cross-validation:

(-x)

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:

(-f) Set to 0.6 by default

Input file given in the deprecated pin-xml format generated by:

(-k) e.g. sqt2pin with the -k option

Read initial weights to the given file:

(-W)

The most informative feature given as the feature name, can be negated to indicate that a lower value is bette:

(-V)

Set verbosity of output: 0=no processing info, 5=all, default is 2:

(-v)

Use unit normalization [0-1] instead of standard deviation normalization:

(-u)

Measure performance on test set each iteration:

(-R)

Override error check and do not fall back on default score vector in case of suspect score vecto:

(-O)

Setting seed of the random number generato:

(-S) Default value is 1

Retention time features calculated as in Klammer et al:

(-K)

Include description of correct features:

(-D)

Do not remove redundant peptides, keep all PSMS and exclude peptide level probabilities:

(-U)

skip validation of input file against xml schema:

(-s)

output protein level probabilities:

(-A)

Probability with which a present protein emits an associated peptide (to be used jointly with the -A option):

(-a) Set by grid search if not specified

Probability of the creation of a peptide from noise (to be used jointly with the -A option):

(-b) Set by grid search if not specified

Prior probability of that a protein is present in the sample ( to be used with the -A option):

(-G) Set by grid search if not specified

treat ties as if it were one protein (Only valid if option -A is active):

(-g)

use pi_0 value when calculating empirical q-values (no effect if option Q is activated) (Only valid if option -A is active):

(-I)

output empirical q-values and p-values (from target-decoy analysis) (Only valid if option -A is active):

(-q)

disactivates the grouping of proteins with similar connectivity, for example if proteins P1 and P2 have the same peptides matching both of them, P1 and P2 will not be grouped as one protein (Only valid if option -A is active):

(-N)

Proteins graph will not be separated in sub-graphs (Only valid if option -A is active):

(-E)

it does not prune peptides with a very low score (~0.0) which means that if a peptide with a very low score is matching two proteins, when we prune the peptide,it will be duplicated to generate two new protein groups (Only valid if option -A is active):

(-C)

Setting depth 0 or 1 or 2 from low depth to high depth(less computational time) of the grid search for the estimation Alpha,Beta and Gamma parameters for fido(Only valid if option -A is active):

(-d) Default value is 0

Define the text pattern to identify the decoy proteins and/or PSMs, set this up if the label that identifies the decoys in the database is not the default (by default : random) (Only valid if option -A is active):

(-P)

Reduce the tree of proteins (removing low scored proteins) in order to estimate alpha,beta and gamma faster.(Only valid if option -A is active):

(-T)

Use target decoy competition to compute peptide probabilities.(recommended when using -A):

(-Y)

Q-value threshold that will be used in the computation of the MSE and ROC AUC score in the grid search (recommended 0.05 for normal size datasets and 0.1 for big size datasets).(Only valid if option -A is active):

(-H)

Proteins with a very low score (< 0.001) will be truncated (assigned 0.0 probability).(Only valid if option -A is active):

(-fido-truncation)

Uses protein group level inference, each cluster of proteins is either present or not, therefore when grouping proteins discard all possible combinations for each group.(Only valid if option -A is active and -N is inactive):

(-Q)

Advanced Options: