anesvi
commented
4 years ago PeptideProphet modeling failed because you do not have enough high confidence PSMs identified by MsFragger. Something wrong with your MSFragegr search parameters. Is it MS3 TMT data? If so (as I suspect), you need to change precursor tolerance from -+20ppm to 0.6 Da
From: Ciara Judge notifications@github.com Sent: Thursday, June 25, 2020 11:14 AM To: Nesvilab/philosopher philosopher@noreply.github.com Cc: Subscribed subscribed@noreply.github.com Subject: [Nesvilab/philosopher] TMT Pipeline Error in Protein Prophet due to Peptide Prophet 'no data' (#140)
External Email - Use Caution
Hi, further to another issuehttps://github.com/Nesvilab/philosopher/issues/139 I was discussing which I now believe to be resolved, I am using the TMT pipeline to analyse databases that I am downloading from PRIDE and converting to mzML using MSConvert. After the operation of PeptideProphet (during which I get a number of warnings about failed mixture model quality tests), ProteinProphet fails, citing a suggestion that PeptideProphet did not run correctly or at all.
I am new to this type of analysis so I assume it is something I am doing wrong, any direction would be appreciated.
This is the complete report from the linux command line:
INFO[15:18:26] Executing Workspace v3.2.7
INFO[15:18:26] Creating workspace
WARN[15:18:26] A meta data folder was found and will not be overwritten.
INFO[15:18:26] Done
INFO[15:18:26] Executing Pipeline v3.2.7
INFO[15:18:26] Initiating the workspace on PXD019087_0
INFO[15:18:26] Creating workspace
INFO[15:18:26] Processing database
INFO[15:18:29] Running the Database Search on all data
MSFragger version MSFragger-3.0
Batmass-IO version 1.17.4
(c) University of Michigan
RawFileReader reading tool. Copyright (c) 2016 by Thermo Fisher Scientific, Inc. All rights reserved.
System OS: Linux, Architecture: amd64
Java Info: 1.8.0_201, Java HotSpot(TM) 64-Bit Server VM, Oracle Corporation
JVM started with 14 GB memory
Checking database...
Checking /home/DATA2/trips/scamp/PXD019087_0/TS_Miwi2-HA+RNAse_2.mzML...
Checking /home/DATA2/trips/scamp/PXD019087_0/TS_Miwi2-HA+RNAse_3.mzML...
Checking /home/DATA2/trips/scamp/PXD019087_0/TS_Miwi2-HA+RNase_1.mzML...
****MAIN SEARCH****
Checking database...
Parameters:
num_threads = 24
database_name = /home/DATA2/trips/scamp/proteomes/2020-06-24-decoys-contam-mus_musculus_proteome.fa
decoyprefix = rev
precursor_mass_lower = -20.0
precursor_mass_upper = 20.0
precursor_mass_units = 1
precursor_true_tolerance = 20.0
precursor_true_units = 1
fragment_mass_tolerance = 20.0
fragment_mass_units = 1
calibrate_mass = 0
write_calibrated_mgf = false
isotope_error = -1/0/1/2/3
mass_offsets = 0
labile_search_mode = OFF
precursor_mass_mode = SELECTED
localize_delta_mass = false
delta_mass_exclude_ranges = (-1.5, 3.5)
fragment_ion_series = b,y
diagnostic_intensity_filter = 0.0
Y_type_masses = 0/203.07937/406.15874/568.21156/730.26438/892.3172/349.137279
diagnostic_fragments = 204.086646/186.076086/168.065526/366.139466/144.0656/138.055/126.055/163.060096/512.197375/292.1026925/274.0921325/657.2349/243.026426/405.079246/485.045576/308.09761
search_enzyme_name = Trypsin
search_enzyme_cutafter = KR
search_enzyme_butnotafter = P
num_enzyme_termini = 2
allowed_missed_cleavage = 2
clip_nTerm_M = true
allow_multiple_variable_mods_on_residue = true
max_variable_mods_per_peptide = 3
max_variable_mods_combinations = 5000
output_file_extension = pepXML
output_format = pepXML
output_report_topN = 3
output_max_expect = 50.0
report_alternative_proteins = false
override_charge = false
precursor_charge_low = 1
precursor_charge_high = 6
digest_min_length = 7
digest_max_length = 50
digest_mass_range_low = 500.0
digest_mass_range_high = 5000.0
max_fragment_charge = 2
deisotope = 0
track_zero_topN = 0
zero_bin_accept_expect = 0.0
zero_bin_mult_expect = 1.0
add_topN_complementary = 0
minimum_peaks = 15
use_topN_peaks = 150
minIonsScoring = 3
min_matched_fragments = 4
minimum_ratio = 0.01
intensity_transform = 0
remove_precursor_peak = 0
remove_precursor_range = -1.5,1.5
clear_mz_range_low = 125.5
clear_mz_range_high = 131.5
excluded_scan_list_file =
mass_diff_to_variable_mod = 0
variable_mod_01 = 15.99490 M 3
variable_mod_02 = 42.01060 [^ 1
variable_mod_03 = 229.162932 n^ 1
variable_mod_04 = 229.162932 S 1
add_A_alanine = 0.000000
add_C_cysteine = 57.021464
add_Cterm_peptide = 0.0
add_Cterm_protein = 0.0
add_D_aspartic_acid = 0.000000
add_E_glutamic_acid = 0.000000
add_F_phenylalanine = 0.000000
add_G_glycine = 0.000000
add_H_histidine = 0.000000
add_I_isoleucine = 0.000000
add_K_lysine = 229.162932
add_L_leucine = 0.000000
add_M_methionine = 0.000000
add_N_asparagine = 0.000000
add_Nterm_peptide = 0.0
add_Nterm_protein = 0.0
add_P_proline = 0.000000
add_Q_glutamine = 0.000000
add_R_arginine = 0.000000
add_S_serine = 0.000000
add_T_threonine = 0.000000
add_V_valine = 0.000000
add_W_tryptophan = 0.000000
add_Y_tyrosine = 0.000000
Selected fragment tolerance 0.10 Da.
4658294454 fragments to be searched in 8 slices (69.41 GB total)
Operating on slice 1 of 8:
Fragment index slice generated in 42.52 s
001. TS_Miwi2-HA+RNAse_2.mzML 43.0 s
[progress: 116032/116032 (100%) - 3357 spectra/s] 34.6s
002. TS_Miwi2-HA+RNAse_3.mzML 27.0 s
[progress: 64463/64463 (100%) - 4226 spectra/s] 15.3s
003. TS_Miwi2-HA+RNase_1.mzML 27.0 s
[progress: 58007/58007 (100%) - 5810 spectra/s] 10.0sOperating on slice 2 of 8:
Fragment index slice generated in 12.36 s
001. TS_Miwi2-HA+RNAse_2.mzML 38.6 s
[progress: 116032/116032 (100%) - 4530 spectra/s] 25.6s
002. TS_Miwi2-HA+RNAse_3.mzML 26.8 s
[progress: 64463/64463 (100%) - 8171 spectra/s] 7.9s
003. TS_Miwi2-HA+RNase_1.mzML 26.1 s
[progress: 58007/58007 (100%) - 7921 spectra/s] 7.3sOperating on slice 3 of 8:
Fragment index slice generated in 11.35 s
001. TS_Miwi2-HA+RNAse_2.mzML 36.0 s
[progress: 116032/116032 (100%) - 5307 spectra/s] 21.9s
002. TS_Miwi2-HA+RNAse_3.mzML 26.7 s
[progress: 64463/64463 (100%) - 8196 spectra/s] 7.9s
003. TS_Miwi2-HA+RNase_1.mzML 26.9 s
[progress: 58007/58007 (100%) - 8419 spectra/s] 6.9sOperating on slice 4 of 8:
Fragment index slice generated in 13.53 s
001. TS_Miwi2-HA+RNAse_2.mzML 37.4 s
[progress: 116032/116032 (100%) - 5243 spectra/s] 22.1s
002. TS_Miwi2-HA+RNAse_3.mzML 26.7 s
[progress: 64463/64463 (100%) - 8522 spectra/s] 7.6s
003. TS_Miwi2-HA+RNase_1.mzML 26.8 s
[progress: 58007/58007 (100%) - 9244 spectra/s] 6.3sOperating on slice 5 of 8:
Fragment index slice generated in 13.58 s
001. TS_Miwi2-HA+RNAse_2.mzML 38.4 s
[progress: 116032/116032 (100%) - 5067 spectra/s] 22.9s
002. TS_Miwi2-HA+RNAse_3.mzML 26.3 s
[progress: 64463/64463 (100%) - 9055 spectra/s] 7.1s
003. TS_Miwi2-HA+RNase_1.mzML 26.8 s
[progress: 58007/58007 (100%) - 10093 spectra/s] 5.7sOperating on slice 6 of 8:
Fragment index slice generated in 12.96 s
001. TS_Miwi2-HA+RNAse_2.mzML 38.9 s
[progress: 116032/116032 (100%) - 5787 spectra/s] 20.1s
002. TS_Miwi2-HA+RNAse_3.mzML 23.1 s
[progress: 64463/64463 (100%) - 17479 spectra/s] 3.7s
003. TS_Miwi2-HA+RNase_1.mzML 24.4 s
[progress: 58007/58007 (100%) - 8882 spectra/s] 6.5sOperating on slice 7 of 8:
Fragment index slice generated in 12.60 s
001. TS_Miwi2-HA+RNAse_2.mzML 39.2 s
[progress: 116032/116032 (100%) - 5469 spectra/s] 21.2s
002. TS_Miwi2-HA+RNAse_3.mzML 25.9 s
[progress: 64463/64463 (100%) - 9953 spectra/s] 6.5s
003. TS_Miwi2-HA+RNase_1.mzML 26.1 s
[progress: 58007/58007 (100%) - 8778 spectra/s] 6.6sOperating on slice 8 of 8:
Fragment index slice generated in 13.02 s
001. TS_Miwi2-HA+RNAse_2.mzML 38.5 s
[progress: 116032/116032 (100%) - 5635 spectra/s] 20.6s | postprocessing 52.1 s
002. TS_Miwi2-HA+RNAse_3.mzML 27.0 s
[progress: 64463/64463 (100%) - 9607 spectra/s] 6.7s | postprocessing 26.3 s
003. TS_Miwi2-HA+RNase_1.mzML 26.0 s
[progress: 58007/58007 (100%) - 10636 spectra/s] 5.5s | postprocessing 27.1 sMAIN SEARCH DONE IN 21.525 MIN
***TOTAL TIME 21.688 MIN****
INFO[15:40:15] Running the validation and inference on PXD019087_0
INFO[15:40:15] Executing PeptideProphet on PXD019087_0
file 1: /home/DATA2/trips/scamp/PXD019087_0/TS_Miwi2-HA+RNAse_2.pepXML
file 2: /home/DATA2/trips/scamp/PXD019087_0/TS_Miwi2-HA+RNAse_3.pepXML
file 3: /home/DATA2/trips/scamp/PXD019087_0/TS_Miwi2-HA+RNase_1.pepXML
processed altogether 91871 results
INFO: Results written to file: /home/DATA2/trips/scamp/PXD019087_0/interact.pep.xml
-
/home/DATA2/trips/scamp/PXD019087_0/interact.pep.xml
-
Building Commentz-Walter keyword tree...
-
Searching the tree...
-
Linking duplicate entries...
-
Printing results...
using Accurate Mass Bins
using PPM mass difference
Using Decoy Label "rev_".
Decoy Probabilities will be reported.
Using non-parametric distributions
(X! Tandem) (using Tandem's expectation score for modeling)
adding ACCMASS mixture distribution
using search_offsets in ACCMASS mixture distr: 0
init with X! Tandem trypsin
MS Instrument info: Manufacturer: UNKNOWN, Model: UNKNOWN, Ionization: UNKNOWN, Analyzer: UNKNOWN, Detector: UNKNOWN
INFO: Processing standard MixtureModel ...
PeptideProphet (TPP v5.2.1-dev Flammagenitus, Build 201906251008-exported (Linux-x86_64)) AKeller@ISB
read in 0 1+, 32020 2+, 50838 3+, 7804 4+, 966 5+, 230 6+, and 13 7+ spectra.
Initialising statistical models ...
Found 42793 Decoys, and 49078 Non-Decoys
Iterations: .........10.........20.........30.
WARNING: Mixture model quality test failed for charge (1+).
WARNING: Mixture model quality test failed for charge (2+).
WARNING: Mixture model quality test failed for charge (3+).
WARNING: Mixture model quality test failed for charge (4+).
WARNING: Mixture model quality test failed for charge (5+).
WARNING: Mixture model quality test failed for charge (6+).
WARNING: Mixture model quality test failed for charge (7+).
model complete after 32 iterations
INFO[15:44:41] Creating combined protein inference
ProteinProphet (C++) by Insilicos LLC and LabKey Software, after the original Perl by A. Keller (TPP v5.2.1-dev Flammagenitus, Build 201906251008-exported (Linux-x86_64))
(no FPKM) (using degen pep info)
Reading in /home/DATA2/trips/scamp/PXD019087_0/interact.pep.xml...
did not find any PeptideProphet results in input data! Did you forget to run PeptideProphet?
...read in 0 1+, 0 2+, 0 3+, 0 4+, 0 5+, 0 6+, 0 7+ spectra with min prob 0.05
WARNING: no data - output file will be empty
FATA[15:44:41] Cannot execute program. There was an error with ProteinProphet, please check your parameters and input files
This is my parameter file:
Philosopher pipeline configuration file.
#
The pipeline mode automates the processing done by Philosopher. First, check
the steps you want to execute in the commands section and change them to
'yes'. For each selected command, go to its section and adjust the parameters
accordingly to your analysis.
#
If you want to include MSFragger and TMT-Integrator into your analysis, you will
haver o download them separately and then add their location tot their configuration
#
Usage:
philosopher pipeline --config [list_of_data_set_folders]
analytics: true # reports when a workspace is created for usage estimation (default true)
slackToken: # specify the Slack API token
slackChannel: # specify the channel name
commands:
workspace: yes # manage the experiment workspace for the analysis
database: yes # target-decoy database formatting
comet: no # peptide spectrum matching with Comet
msfragger: yes # peptide spectrum matching with MSFragger
peptideprophet: yes # peptide assignment validation
ptmprophet: no # PTM site localization
proteinprophet: no # protein identification validation
filter: yes # statistical filtering, validation and False Discovery Rates assessment
freequant: no # label-free Quantification
labelquant: no # isobaric Labeling-Based Relative Quantification
bioquant: no # protein report based on Uniprot protein clusters
report: yes # multi-level reporting for both narrow-searches and open-searches
abacus: yes # combined analysis of LC-MS/MS results
tmtintegrator: no # integrates channel abundances from multiple TMT samples
database:
protein_database: /home/DATA2/trips/scamp/proteomes/2020-06-24-decoys-contam-mus_musculus_proteome.fa # path to the target-decoy protein database
decoytag: rev # prefix tag used added to decoy sequences
comet:
noindex: true # skip raw file indexing
param: # comet parameter file (default "comet.params.txt")
raw: mzML # format of the spectra file
msfragger: # v2.3
path: /home/DATA2/trips/scamp/MSFragger/MSFragger.jar # path to MSFragger jar
memory: 16 # how much memory in GB to use
param: # MSFragger parameter file
raw: mzML # spectra format
num_threads: 0 # 0=poll CPU to set num threads; else specify num threads directly (max 64)
precursor_mass_lower: -20 # lower bound of the precursor mass window
precursor_mass_upper: 20 # upper bound of the precursor mass window
precursor_mass_units: 1 # 0=Daltons, 1=ppm
precursor_true_tolerance: 20 # true precursor mass tolerance (window is +/- this value)
precursor_true_units: 1 # 0=Daltons, 1=ppm
fragment_mass_tolerance: 20 # fragment mass tolerance (window is +/- this value)
fragment_mass_units: 1 # fragment mass tolerance units (0 for Da, 1 for ppm)
calibrate_mass: 0 # 0=Off, 1=On, 2=On and find optimal parameters
deisotope: 0 # activates deisotoping.
isotope_error: -1/0/1/2/3 # 0=off, -1/0/1/2/3 (standard C13 error)
mass_offsets: 0 # allow for additional precursor mass window shifts. Multiplexed with isotope_error. mass_offsets = 0/79.966 can be use$
precursor_mass_mode: selected # selected or isolated
localize_delta_mass: 0 # this allows shifted fragment ions - fragment ions with mass increased by the calculated mass difference, to be includ$
delta_mass_exclude_ranges: (-1.5,3.5) # exclude mass range for shifted ions searching
fragment_ion_series: b,y # ion series used in search
search_enzyme_name: Trypsin # name of enzyme to be written to the pepXML file
search_enzyme_cutafter: KR # residues after which the enzyme cuts
search_enzyme_butnotafter: P # residues that the enzyme will not cut before
num_enzyme_termini: 2 # 2 for enzymatic, 1 for semi-enzymatic, 0 for nonspecific digestion
allowed_missed_cleavage: 2 # maximum value is 5
clip_nTerm_M: 1 # specifies the trimming of a protein N-terminal methionine as a variable modification (0 or 1)
variable_mod_01: 15.99490 M 3 # variable modification
variable_mod_02: 42.01060 [^ 1 # variable modification
variable_mod_03: 229.162932 n^ 1 # variable modification
variable_mod_04: 229.162932 S 1 # variable modification
variable_mod_05: # variable modification
variable_mod_06: # variable modification
variable_mod_07: # variable modification
allow_multiple_variable_mods_on_residue: 1 # static mods are not considered
max_variable_mods_per_peptide: 3 # maximum of 5
max_variable_mods_combinations: 5000 # maximum of 65534, limits number of modified peptides generated from sequence
output_file_extension: pepXML # file extension of output files
output_format: pepXML # file format of output files (pepXML or tsv)
output_report_topN: 3 # reports top N PSMs per input spectrum
output_max_expect: 50 # suppresses reporting of PSM if top hit has expectation greater than this threshold
report_alternative_proteins: 0 # 0=no, 1=yes
precursor_charge: 1 6 # assume range of potential precursor charge states. Only relevant when override_charge is set to 1
override_charge: 0 # 0=no, 1=yes to override existing precursor charge states with precursor_charge parameter
digest_min_length: 7 # minimum length of peptides to be generated during in-silico digestion
digest_max_length: 50 # maximum length of peptides to be generated during in-silico digestion
digest_mass_range: 500.0 5000.0 # mass range of peptides to be generated during in-silico digestion in Daltons
max_fragment_charge: 2 # maximum charge state for theoretical fragments to match (1-4)
track_zero_topN: 0 # in addition to topN results, keep track of top results in zero bin
zero_bin_accept_expect: 0 # boost top zero bin entry to top if it has expect under 0.01 - set to 0 to disable
zero_bin_mult_expect: 1 # disabled if above passes - multiply expect of zero bin for ordering purposes (does not affect reported expect)
add_topN_complementary: 0 # inserts complementary ions corresponding to the top N most intense fragments in each experimental spectra
minimum_peaks: 15 # required minimum number of peaks in spectrum to search (default 10)
use_topN_peaks: 150 # pre-process experimental spectrum to only use top N peaks
min_fragments_modelling: 3 # minimum number of matched peaks in PSM for inclusion in statistical modeling
min_matched_fragments: 4 # minimum number of matched peaks for PSM to be reported
minimum_ratio: 0.01 # filters out all peaks in experimental spectrum less intense than this multiple of the base peak intensity
clear_mz_range: 125.5 131.5 # for iTRAQ/TMT type data; will clear out all peaks in the specified m/z range
remove_precursor_peak: 0 # remove precursor peaks from tandem mass spectra. 0=not remove; 1=remove the peak with precursor charge; 2=remove the $
remove_precursor_range: -1.5,1.5 # m/z range in removing precursor peaks. Unit: Da.
intensity_transform: 0 # transform peaks intensities with sqrt root. 0=not transform; 1=transform using sqrt root.
add_Cterm_peptide: 0.000000 # c-term peptide fixed modifications
add_Cterm_protein: 0.000000 # c-term protein fixed modifications
add_Nterm_peptide: 0.000000 # n-term peptide fixed modifications
add_Nterm_protein: 0.000000 # n-term protein fixed modifications
add_A_alanine: 0.000000 # alanine fixed modifications
add_C_cysteine: 57.021464 # cysteine fixed modifications
add_D_aspartic_acid: 0.000000 # aspartic acid fixed modifications
add_E_glutamic_acid: 0.000000 # glutamic acid fixed modifications
add_F_phenylalanine: 0.000000 # phenylalanine fixed modifications
add_G_glycine: 0.000000 # glycine fixed modifications
add_H_histidine: 0.000000 # histidine fixed modifications
add_I_isoleucine: 0.000000 # isoleucine fixed modifications
add_K_lysine: 229.162932 # lysine fixed modifications
add_L_leucine: 0.000000 # leucine fixed modifications
add_M_methionine: 0.000000 # methionine fixed modifications
add_N_asparagine: 0.000000 # asparagine fixed modifications
add_P_proline: 0.000000 # proline fixed modifications
add_Q_glutamine: 0.000000 # glutamine fixed modifications
add_R_arginine: 0.000000 # arginine fixed modifications
add_S_serine: 0.000000 # serine fixed modifications
add_T_threonine: 0.000000 # threonine fixed modifications
add_V_valine: 0.000000 # valine fixed modifications
add_W_tryptophan: 0.000000 # tryptophan fixed modifications
add_Y_tyrosine: 0.000000 # tyrosine fixed modifications
peptideprophet: # v5.2
extension: pepXML # pepXML file extension
clevel: 0 # set Conservative Level in neg_stdev from the neg_mean, low numbers are less conservative, high numbers are more conse$
accmass: true # use Accurate Mass model binning
decoyprobs: true # compute possible non-zero probabilities for Decoy entries on the last iteration
enzyme: trypsin # enzyme used in sample (optional)
exclude: false # exclude deltaCn, Mascot, and Comet results from results (default Penalize results)
expectscore: true # use expectation value as the only contributor to the f-value for modeling
forcedistr: false # bypass quality control checks, report model despite bad modeling
glyc: false # enable peptide Glyco motif model
icat: false # apply ICAT model (default Autodetect ICAT)
instrwarn: false # warn and continue if combined data was generated by different instrument models
leave: false # leave alone deltaCn, Mascot, and Comet results from results (default Penalize results)
maldi: false # enable MALDI mode
masswidth: 5 # model mass width (default 5)
minpeplen: 7 # minimum peptide length not rejected (default 7)
minpintt: 2 # minimum number of NTT in a peptide used for positive pI model (default 2)
minpiprob: 0.9 # minimum probability after first pass of a peptide used for positive pI model (default 0.9)
minprob: 0.05 # report results with minimum probability (default 0.05)
minrtntt: 2 # minimum number of NTT in a peptide used for positive RT model (default 2)
minrtprob: 0.9 # minimum probability after first pass of a peptide used for positive RT model (default 0.9)
neggamma: false # use Gamma distribution to model the negative hits
noicat: false # do no apply ICAT model (default Autodetect ICAT)
nomass: false # disable mass model
nonmc: false # disable NMC missed cleavage model
nonparam: true # use semi-parametric modeling, must be used in conjunction with --decoy option
nontt: false # disable NTT enzymatic termini model
optimizefval: false # (SpectraST only) optimize f-value function f(dot,delta) using PCA
phospho: false # enable peptide Phospho motif model
pi: false # enable peptide pI model
ppm: true # use PPM mass error instead of Dalton for mass modeling
zero: false # report results with minimum probability 0
ptmprophet: # v5.2
autodirect: false # use direct evidence when the lability is high, use in combination with LABILITY
cions: # use specified C-term ions, separate multiple ions by commas (default: y for CID, z for ETD)
direct: false # use only direct evidence for evaluating PTM site probabilities
em: 2 # set EM models to 0 (no EM), 1 (Intensity EM Model Applied) or 2 (Intensity and Matched Peaks EM Models Applied)
static: false # use static fragppmtol for all PSMs instead of dynamically estimates offsets and tolerances
fragppmtol: 15 # when computing PSM-specific mass_offset and mass_tolerance, use specified default +/- MS2 mz tolerance on fragment io$
ifrags: false # use internal fragments for localization
keepold: false # retain old PTMProphet results in the pepXML file
lability: false # compute Lability of PTMs
massdiffmode: false # use the Mass Difference and localize
massoffset: 0 # adjust the massdiff by offset (0 = use default)
maxfragz: 0 # limit maximum fragment charge (default: 0=precursor charge, negative values subtract from precursor charge)
maxthreads: 4 # use specified number of threads for processing
mino: 0 # use specified number of pseudo-counts when computing Oscore (0 = use default)
minprob: 0 # use specified minimum probability to evaluate peptides
mods: # specify modifications
nions: # use specified N-term ions, separate multiple ions by commas (default: a,b for CID, c for ETD)
nominofactor: false # disable MINO factor correction when MINO= is set greater than 0 (default: apply MINO factor correction)
ppmtol: 1 # use specified +/- MS1 ppm tolerance on peptides which may have a slight offset depending on search parameters
verbose: false # produce Warnings to help troubleshoot potential PTM shuffling or mass difference issues
proteinprophet: # v5.2
accuracy: false # equivalent to --minprob 0
allpeps: false # consider all possible peptides in the database in the confidence model
confem: false # use the EM to compute probability given the confidence
delude: false # do NOT use peptide degeneracy information when assessing proteins
excludezeros: false # exclude zero prob entries
fpkm: false # model protein FPKM values
glyc: false # highlight peptide N-glycosylation motif
icat: false # highlight peptide cysteines
instances: false # use Expected Number of Ion Instances to adjust the peptide probabilities prior to NSP adjustment
iprophet: false # input is from iProphet
logprobs: false # use the log of the probabilities in the Confidence calculations
maxppmdiff: 20 # maximum peptide mass difference in PPM (default 20)
minprob: 0.05 # peptideProphet probabilty threshold (default 0.05)
mufactor: 1 # fudge factor to scale MU calculation (default 1)
nogroupwts: false # check peptide's Protein weight against the threshold (default: check peptide's Protein Group weight against threshold)
nonsp: false # do not use NSP model
nooccam: false # non-conservative maximum protein list
noprotlen: false # do not report protein length
normprotlen: false # normalize NSP using Protein Length
protmw: false # get protein mol weights
softoccam: false # peptide weights are apportioned equally among proteins within each Protein Group (less conservative protein count est$
unmapped: false # report results for UNMAPPED proteins
filter:
psmFDR: 0.01 # psm FDR level (default 0.01)
peptideFDR: 0.01 # peptide FDR level (default 0.01)
ionFDR: 0.01 # peptide ion FDR level (default 0.01)
proteinFDR: 0.01 # protein FDR level (default 0.01)
peptideProbability: 0.7 # top peptide probability threshold for the FDR filtering (default 0.7)
proteinProbability: 0.5 # protein probability threshold for the FDR filtering (not used with the razor algorithm) (default 0.5)
peptideWeight: 0.9 # threshold for defining peptide uniqueness (default 1)
razor: true # use razor peptides for protein FDR scoring
picked: true # apply the picked FDR algorithm before the protein scoring
mapMods: true # map modifications acquired by an open search
models: true # print model distribution
sequential: true # alternative algorithm that estimates FDR using both filtered PSM and Protein lists
freequant:
peakTimeWindow: 0.4 # specify the time windows for the peak (minute) (default 0.4)
retentionTimeWindow: 3 # specify the retention time window for xic (minute) (default 3)
tolerance: 10 # m/z tolerance in ppm (default 10)
isolated: true # use the isolated ion instead of the selected ion for quantification
labelquant:
annotation: annotation.txt # annotation file with custom names for the TMT channels
bestPSM: true # select the best PSMs for protein quantification
level: 2 # ms level for the quantification
minProb: 0.7 # only use PSMs with a minimum probability score
plex: 10 # number of channels
purity: 0.5 # ion purity threshold (default 0.5)
removeLow: 0.05 # ignore the lower 3% PSMs based on their summed abundances
tolerance: 20 # m/z tolerance in ppm (default 20)
uniqueOnly: false # report quantification based on only unique peptides
report:
msstats: false # create an output compatible to MSstats
withDecoys: false # add decoy observations to reports
mzID: false # create a mzID output
bioquant:
organismUniProtID: # UniProt proteome ID
level: 0.9 # cluster identity level (default 0.9)
abacus:
protein: true # global level protein report
peptide: false # global level peptide report
proteinProbability: 0.05 # minimum protein probability (default 0.9)
peptideProbability: 0.5 # minimum peptide probability (default 0.5)
uniqueOnly: false # report TMT quantification based on only unique peptides
reprint: false # create abacus reports using the Reprint format
tmtintegrator: # v1.1.2
path: # path to TMT-Integrator jar
memory: 100 # memory allocation, in Gb
output: # the location of output files
channel_num: 10 # number of channels in the multiplex (e.g. 10, 11)
ref_tag: pool # unique tag for identifying the reference channel (Bridge sample added to each multiplex)
groupby: -1 # level of data summarization(0: PSM aggregation to the gene level; 1: protein; 2: peptide sequence; 3: PTM site; -1: g$
psm_norm: false # perform additional retention time-based normalization at the PSM level
outlier_removal: true # perform outlier removal
prot_norm: -1 # normalization (0: None; 1: MD (median centering); 2: GN (median centering + variance scaling); -1: generate reports w$
min_pep_prob: 0.9 # minimum PSM probability threshold (in addition to FDR-based filtering by Philosopher)
min_purity: 0.5 # ion purity score threshold
min_percent: 0.05 # remove low intensity PSMs (e.g. value of 0.05 indicates removal of PSMs with the summed TMT reporter ions intensity i$
unique_pep: false # allow PSMs with unique peptides only (if true) or unique plus razor peptides (if false), as classified by Philosopher$
unique_gene: 0 # additional, gene-level uniqueness filter (0: allow all PSMs; 1: remove PSMs mapping to more than one GENE with eviden$
best_psm: true # keep the best PSM only (highest summed TMT intensity) among all redundant PSMs within the same LC-MS run
prot_exclude: sp|,tr| # exclude proteins with specified tags at the beginning of the accession number (e.g. none: no exclusion; sp|,tr| : exc$
allow_overlabel: false # allow PSMs with TMT on S (when overlabeling on S was allowed in the database search)
allow_unlabeled: false # allow PSMs without TMT tag or acetylation on the peptide n-terminus
mod_tag: none # PTM info for generation of PTM-specific reports (none: for Global data; S(79.9663),T(79.9663),Y(79.9663): for Phospho$
min_site_prob: -1 # site localization confidence threshold (-1: for Global; 0: as determined by the search engine; above 0 (e.g. 0.75): P$
ms1_int: true # use MS1 precursor ion intensity (if true) or MS2 summed TMT reporter ion intensity (if false) as part of the referenc$
top3_pep: true # use top 3 most intense peptide ions as part of the reference sample abundance estimation
print_RefInt: false # print individual reference sample abundance estimates for each multiplex in the final reports (in addition to the com$
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Hi, further to another issue I was discussing which I now believe to be resolved, I am using the TMT pipeline to analyse databases that I am downloading from PRIDE and converting to mzML using MSConvert. After the operation of PeptideProphet (during which I get a number of warnings about failed mixture model quality tests), ProteinProphet fails, citing a suggestion that PeptideProphet did not run correctly or at all.
I am new to this type of analysis so I assume it is something I am doing wrong, any direction would be appreciated.
This is the complete report from the linux command line:
This is my parameter file: