FeatureFinderMultiplex

charge = 1-7

isotopes_per_peptide = 3:6

RT typicalalthough described otherwise this seems to be an upper bound. At the moment (01/2018) 90s or higher semms to work fine for our Q-Exactive data, shorter RT typical leads to feature splitting of longer features, which is problematic for feature linking of unlabeled data

RT min = 3s

ìntensity cutoff = 1000

peptide similarity = 0.7

averagine similarity =0.6

averagine similarity scaling = 0.95 only relevant if knock-out option is selected (if lower, too many singlets will be reported)

mz_tolerance = 10 ppm When knock_out detection is switched on, never use a tolerance below the accuracy of the machine.

knock out = true true for double/triple labelled data, false for label-free data

missed cleavages = 3 2 or 3 for dimethylation data, 0 for label free data

IDMapper

rt_tolerance = 20 sec (about 1/2 of feature elution time)

MF: For labelled experiments it could be benefical to increase rt_tolerance, because the controided position in the consensusXML is used

mz_tolerance = 10 ppm

MF: For labelled experiments it could be benefical to increase mz_tolerance, because the controided position in the consensusXML is used

MFA: For TMT both tolerances could be set to 0, since ID´s and Intensities are in the same spectra.

mz_reference = peptides

MFA: Using precursor led to ID´s mapped on false features

see https://github.com/OpenMS/OpenMS/issues/2468

use_centroided_mz = true

MFA: Use true in combination with mz_reference = peptides, to avoid mapping ID´s on isotopic features (Labelfree data).

use_centroided_RT = false

For consensusXML (labelled experiments) mz and RT are automatically centroided, therefore both centroided options do not have any effect.

use subelements = true use true for labelled data and false for label-free data

annotate ids with subelement = true use true for labelled data (when MultiplexResolver will be used) and false for label-free data

XTandemAdapter

xtandem_executable: C:\tandem-for-OpenMS\Alanine

missed_cleavages = 0 (if samples digested separately)

precursor_mass_tolerance = 10 ppm

fragment_mass_tolerance = 20 ppm

no_isotope_error = true (allow-isotope_error: no in older versions)

MSGFplusAdapter

precursor_mass_tolerance = 10 ppm

isotope error range 0:0when HighRes Precursor Mass Correector was used before search engine (already finds the correct position of the monoisotopic peak)

instrument = Q_exactive

add_features = TRUE might be a good option as this is needed for downstream analysis with PercolatorAdapter. Otherwise the search has to be done again

max_mods for labelled data (e.g. dimethylated) no less than 3. Take into consideration the more mods are considered the longer it will take to finish

Add Output for mzid files (see below)

Care should be taken with IDPosteriorErrorProbability when using MSGF+ and in an ideal setting check for the fit. Alternatively, PeptideProphet/iProphet (from the TPP) can be used (checking as well the fit) and then converting the pep.xml file to .idxml with IDFileConverter and filtering for an specific FDR with IDFilter using the PeptideProphet/iProphet minimum probability needed to achieve the maximum desired FDR. (The complete ROC values are written at the beginning of the pep.xml file)

PeptideIndexer

decoy_string = dec_we agreed to generate all decoy databases with prefix: dec_

write_protein_sequence = TRUE necessary to obtain sequence coverage (e.g. as a column in mzTab exporter and text exporter). All protein sequences are stored as well, what increases file size and computation times. Peptide indexer can also be run again after protein inference to obtain the sequence coverage (MF)

write_protein_description = TRUE to obtain the protein name (e.g. as a column in mzTab exporter and text exporter)(MF)

FIDO adapter

greedy group resolution = TRUE

group level= TRUE

Accuracy = STRICT (or left empty)

For big data sets searched with MSGF+ the time required to complete might be extremely long

FalseDiscoveryRate

q-value = true in IDFilter afterwards select: score pep = 0.01and score prot = 0.01for publication