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Large scale analysis with Philosopher pipeline
Felipe Leprevost edited this page Feb 18, 2020
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For this example we will see how to process and analyze the Clear Cell Renal Carcinoma cohort data from CPTAC 3 using MSFragger, Philosopher and TMT-Integrator. You will learn how to process a large dataset composed of multiple fractionated TMT-labeled samples. This tutorial will contain the details needed for you to reproduce the published results. (There may be some small differences in the results, as our tools continue to be improved.)
We will need:
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Philosopher (version 2.1.1 or higher)
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MSFragger (version 2.3 or higher)
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TMT-Integrator (version 2.3 or higher)
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Java 8 Runtime Environment (required by MSFragger)
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mzML files from the Clear Cell Renal Carcinoma data set from CPTAC 3 (instructions for download below)
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A protein sequence database (see this example)
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A computer server running GNU/Linux with at least 64GB of RAM
We ran this example on a Linux Red Hat 7, so the commands shown below are Linux compatible. To reproduce this on a Windows machine, you will need to adjust the folder separators ('\' for windows and '/' for Linux).
The CPTAC 3 data can be downloaded from the NIH/CPTAC data portal, which may require installation of the IBM Aspera Connect browser extension and application. You'll also need to agree to the terms of use.
Select the mzML files you want to download, in this example we will use the 'Proteome' (non-phospho enriched) fraction. We don't need to do any file conversion because we are already using the mzML files provided by the consortium, but you will need to unzip/decompress the files.
Start by creating a folder for the entire analysis that will be called 6_CPTAC3_Clear_Cell_Renal_Cell_Carcinoma, inside we will create a folder for the whole proteome data we've downloaded. Inside this directory, there should be 23 folders, each of which will contain all fractions from each multiplexed TMT-labeled sample.
Create a folder called bin for the software tools we will use, a folder called params for the parameter files, and a folder called database with our protein sequence FASTA file.
The workspace structure should look like this:
6_CPTAC3_Clear_Cell_Renal_Cell_Carcinoma
|── whole
| ├── 01CPTAC_CCRCC_W_JHU_20171007
| ├── 02CPTAC_CCRCC_W_JHU_20171003
| ├── 03CPTAC_CCRCC_W_JHU_20171022
| ├── 04CPTAC_CCRCC_W_JHU_20171026
| ├── 05CPTAC_CCRCC_W_JHU_20171030
| ├── 06CPTAC_CCRCC_W_JHU_20171120
| ├── 07CPTAC_CCRCC_W_JHU_20171127
| ├── 08CPTAC_CCRCC_W_JHU_20171205
| ├── 09CPTAC_CCRCC_W_JHU_20171215
| ├── 10CPTAC_CCRCC_W_JHU_20180119
| ├── 11CPTAC_CCRCC_W_JHU_20180126
| ├── 12CPTAC_CCRCC_W_JHU_20180202
| ├── 13CPTAC_CCRCC_W_JHU_20180215
| ├── 14CPTAC_CCRCC_W_JHU_20180223
| ├── 15CPTAC_CCRCC_W_JHU_20180315
| ├── 16CPTAC_CCRCC_W_JHU_20180322
| ├── 17CPTAC_CCRCC_W_JHU_20180517
| ├── 18CPTAC_CCRCC_W_JHU_20180521
| ├── 19CPTAC_CCRCC_W_JHU_20180526
| ├── 20CPTAC_CCRCC_W_JHU_20180602
| ├── 21CPTAC_CCRCC_W_JHU_20180621
| ├── 22CPTAC_CCRCC_W_JHU_20180625
| ├── 23CPTAC_CCRCC_W_JHU_20180629
├── bin
│ ├── MSFragger-2.3.jar
│ ├── philosopher
|── params
| ├── fragger.params
| ├── philosopher.yaml
|── database
| └── 2020-02-18-decoys-reviewed-contam-UP000005640.fas
Inside each one of these folders, place the mzML files corresponding to all fractions for that multiplexed sample, plus an annotation file for the TMT channels:
.
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f01.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f02.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f03.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f04.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f05.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f06.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f07.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f08.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f09.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f10.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f11.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f12.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f13.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f14.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f15.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f16.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f17.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f18.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f19.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f20.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f21.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f22.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f23.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_f24.mzML
├── 01CPTAC_CCRCC_W_JHU_20171007_LUMOS_fA.mzML
├── annotation.txt
The annotation file is a simple text file with mappings between the TMT channels and the sample labels, which is needed to generate the final reports. Each data set folder should contain a text file called annotation.txt with the mapping. Below is an example of the annotation file for the data set #01:
126 CPT0079430001
127N CPT0023360001
127C CPT0023350003
128N CPT0079410003
128C CPT0087040003
129N CPT0077310003
129C CPT0077320001
130N CPT0087050003
130C CPT0002270011
131N pool01
The given labels for each cohort and data set can also be found on the NIH CPTAC data portal (in the CPTAC_CCRCC_metadata folder).
We will use the parameter file displayed below for our analysis. More details about each parameter can be found in the MSFragger wiki.
num_threads = 0 # Number of CPU threads to use.
database_name = /workspace/6_CPTAC3_Clear_Cell_Renal_Cell_Carcinoma/database/2020-02-18-decoys-reviewed-contam-UP000005640.fas # Path to the protein database file in FASTA format.
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 # Precursor mass tolerance units (0 for Da, 1 for ppm).
precursor_true_tolerance = 20 # True precursor mass tolerance (window is +/- this value).
precursor_true_units = 1 # True precursor mass tolerance units (0 for Da, 1 for 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 # Perform mass calibration (0 for OFF, 1 for ON, 2 for ON and find optimal parameters).
write_calibrated_mgf = 0 # Write calibrated MS2 scan to a MGF file (0 for No, 1 for Yes).
decoy_prefix = rev_ # Prefix added to the decoy protein ID.
isotope_error = 0/1/2 # Also search for MS/MS events triggered on specified isotopic peaks.
mass_offsets = 0 # Creates multiple precursor tolerance windows with specified mass offsets.
precursor_mass_mode = selected # One of isolated/selected/recalculated.
localize_delta_mass = 0 # Include fragment ions mass-shifted by unknown modifications (recommended for open
# and mass offset searches) (0 for OFF, 1 for ON).
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, specify any of a,b,c,x,y,z (comma separated).
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 # 0 for non-enzymatic, 1 for semi-enzymatic, and 2 for fully-enzymatic.
allowed_missed_cleavage = 1 # Allowed number of missed cleavages per peptide. Maximum value is 5.
clip_nTerm_M = 1 # Specifies the trimming of a protein N-terminal methionine as a variable modification (0 or 1).
# maximum of 16 mods - amino acid codes, * for any amino acid,
# [ and ] specifies protein termini, n and c specifies
# peptide termini
variable_mod_01 = 15.9949 M 3
variable_mod_02 = 42.0106 [^ 1
variable_mod_03 = 229.162932 n^ 1
variable_mod_04 = 229.162932 S 1
allow_multiple_variable_mods_on_residue = 0 # Allow each residue to be modified by multiple variable modifications (0 or 1).
max_variable_mods_per_peptide = 3 # Maximum total number of variable modifications per peptide.
max_variable_mods_combinations = 50000 # Maximum number of modified forms allowed for each peptide (up to 65534).
output_file_extension = pepXML # File extension of output files.
output_format = pepXML # File format of output files (pepXML or tsv).
output_report_topN = 1 # Reports top N PSMs per input spectrum.
output_max_expect = 50 # Suppresses reporting of PSM if top hit has expectation value greater than this threshold.
report_alternative_proteins = 0 # Report alternative proteins for peptides that are found in multiple proteins (0 for no, 1 for yes).
precursor_charge = 1 4 # Assumed range of potential precursor charge states. Only relevant when override_charge is set to 1.
override_charge = 0 # Ignores precursor charge and uses charge state specified in precursor_charge range (0 or 1).
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).
# excluded_scan_list_file = # Text file containing a list of scan names to be ignored in the search.
track_zero_topN = 0 # Track top N unmodified peptide results separately from main results internally for boosting features. Should be
# set to a number greater than output_report_topN if zero bin boosting is desired.
zero_bin_accept_expect = 0.00 # Ranks a zero-bin hit above all non-zero-bin hit if it has expectation less than this value.
zero_bin_mult_expect = 1.00 # Multiplies expect value of PSMs in the zero-bin during results ordering (set to less than 1 for boosting).
add_topN_complementary = 0 # Inserts complementary ions corresponding to the top N most intense fragments in each experimental spectra.
minimum_peaks = 15 # Minimum number of peaks in experimental spectrum for matching.
use_topN_peaks = 150 # Pre-process experimental spectrum to only use top N peaks.
deisotope = 1 # Perform deisotoping or not (0=no, 1=yes and assume singleton peaks single charged, 2=yes and assume singleton
# peaks single or double charged).
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 # Removes peaks in this m/z range prior to matching.
remove_precursor_peak = 0 # Remove precursor peaks from tandem mass spectra. 0 = not remove; 1 = remove the peak with precursor charge;
# 2 = remove the peaks with all charge states.
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.
# Fixed modifications
add_Cterm_peptide = 0.000000
add_Nterm_peptide = 0.000000
add_Cterm_protein = 0.000000
add_Nterm_protein = 0.000000
add_G_glycine = 0.000000
add_A_alanine = 0.000000
add_S_serine = 0.000000
add_P_proline = 0.000000
add_V_valine = 0.000000
add_T_threonine = 0.000000
add_C_cysteine = 57.021464
add_L_leucine = 0.000000
add_I_isoleucine = 0.000000
add_N_asparagine = 0.000000
add_D_aspartic_acid = 0.000000
add_Q_glutamine = 0.000000
add_K_lysine = 229.162932
add_E_glutamic_acid = 0.000000
add_M_methionine = 0.000000
add_H_histidine = 0.000000
add_F_phenylalanine = 0.000000
add_R_arginine = 0.000000
add_Y_tyrosine = 0.000000
add_W_tryptophan = 0.000000
add_B_user_amino_acid = 0.000000
add_J_user_amino_acid = 0.000000
add_O_user_amino_acid = 0.000000
add_U_user_amino_acid = 0.000000
add_X_user_amino_acid = 0.000000
add_Z_user_amino_acid = 0.000000
For the Philosopher analysis we are going to run it using the automated pipeline mode, this mode will automatically run all the necessary steps for us, since we have multiple folders, it would be difficult to run them all manually. For doing so we first need to set the philosopher.yaml configuration file. The configuration file is divided in two sections; the upper part contains a list of all the commands the program is able to automate, the following sections are the individual commands parameter lists. We will set each of the desired commands to yes on the upper part, then we will configure the individual steps. The example below is what we will use for the analysis. You can check on the documentation page the meaning of each parameter and how to adjust them for your analysis.
# Philosopher pipeline configuration file.
#
# The pipeline mode automates the processing done by Philosopher and other tools. 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
# have to download them separately, and then add their location in their
# configuration section.
#
# Usage:
# philosopher pipeline --config <this_configuration_file> [list_of_data_set_folders]
analytics: false # 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: yes # label-free Quantification
labelquant: yes # 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: /workspace/6_CPTAC3_Clear_Cell_Renal_Cell_Carcinoma/database/2020-02-18-decoys-reviewed-contam-UP000005640.fas # path to the target-decoy protein database
decoy_tag: 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: /workspace/6_CPTAC3_Clear_Cell_Renal_Cell_Carcinoma/bin/MSFragger-2.3.jar # path to MSFragger jar
memory: 60 # how much memory in GB to use
param: /workspace/6_CPTAC3_Clear_Cell_Renal_Cell_Carcinoma/params/fragger.params # 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: -50 # lower bound of the precursor mass window
precursor_mass_upper: 50 # 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: 2 # 0=Off, 1=On, 2=On and find optimal parameters
deisotope: 1 # activates deisotoping.
isotope_error: 0/1/2 # 0=off, 0/1/2 (standard C13 error)
mass_offsets: 0 # allow for additional precursor mass window shifts. Multiplexed with isotope_error. mass_offsets = 0/79.966 can be used as a restricted ‘open’ search that looks for unmodified and phosphorylated peptides (on any residue)
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 included in scoring
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: 1 # 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: # variable modification
variable_mod_04: # variable modification
variable_mod_05: # variable modification
variable_mod_06: # variable modification
variable_mod_07: # variable modification
allow_multiple_variable_mods_on_residue: 0 # 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: 1 # 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 4 # 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: 100 # pre-process experimental spectrum to only use top N peaks
min_fragments_modelling: 2 # 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: 0.0 0.0 # 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 peaks with all charge states.
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: 0.000000 # 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 conservative
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 ions
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: 1000000 # 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 estimate)
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: false # 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: false # 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: false # 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
brand: # isobairic labeling brand (tmt, itraq)
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: true # global level peptide report
proteinProbability: 0.9 # 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: 6 # 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: Bridge # 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: generate reports at all levels)
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 with all normalization options)
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 in the lowest 5% of all PSMs)
unique_pep: false # allow PSMs with unique peptides only (if true) or unique plus razor peptides (if false), as classified by Philosopher and defined in PSM.tsv files
unique_gene: 0 # additional, gene-level uniqueness filter (0: allow all PSMs; 1: remove PSMs mapping to more than one GENE with evidence of expression in the dataset; 2:remove all PSMs mapping to more than one GENE in the fasta file)
best_psm: true # keep the best PSM only (highest summed TMT intensity) among all redundant PSMs within the same LC-MS run
prot_exclude: none # exclude proteins with specified tags at the beginning of the accession number (e.g. none: no exclusion; sp|,tr| : exclude protein with sp| or tr|)
allow_overlabel: true # allow PSMs with TMT on S (when overlabeling on S was allowed in the database search)
allow_unlabeled: true # 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[167],T[181],Y[243]: for Phospho; K[170]: for K-Acetyl)
min_site_prob: -1 # site localization confidence threshold (-1: for Global; 0: as determined by the search engine; above 0 (e.g. 0.75): PTMProphet probability, to be used with phosphorylation only)
ms1_int: true # use MS1 precursor ion intensity (if true) or MS2 summed TMT reporter ion intensity (if false) as part of the reference sample abundance estimation
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 combined reference sample abundance estimate)
add_Ref: -1 # add an artificial reference channel if there is no reference channel (-1: don't add the reference; 0: use summation as the reference; 1: use average as the
To start the pipeline, we need to run Philosopher using the pipeline command, passing each of the data sets we wish to process together.
$ bin/philosopher pipeline --config params/philosopher.yaml 01CPTAC_CCRCC_W_JHU_20171007 02CPTAC_CCRCC_W_JHU_20171003 03CPTAC_CCRCC_W_JHU_20171022 04CPTAC_CCRCC_W_JHU_20171026 05CPTAC_CCRCC_W_JHU_20171030 06CPTAC_CCRCC_W_JHU_20171120 07CPTAC_CCRCC_W_JHU_20171127 08CPTAC_CCRCC_W_JHU_20171205 09CPTAC_CCRCC_W_JHU_20171215 10CPTAC_CCRCC_W_JHU_20180119 11CPTAC_CCRCC_W_JHU_20180126 12CPTAC_CCRCC_W_JHU_20180202 13CPTAC_CCRCC_W_JHU_20180215 14CPTAC_CCRCC_W_JHU_20180223 15CPTAC_CCRCC_W_JHU_20180315 16CPTAC_CCRCC_W_JHU_20180322 17CPTAC_CCRCC_W_JHU_20180517 18CPTAC_CCRCC_W_JHU_20180521 19CPTAC_CCRCC_W_JHU_20180526 20CPTAC_CCRCC_W_JHU_20180602 21CPTAC_CCRCC_W_JHU_20180621 22CPTAC_CCRCC_W_JHU_20180625 23CPTAC_CCRCC_W_JHU_20180629
Each step will be executed consecutively, and no other commands or input from the user are necessary.
When the analysis is done, we will have individual results for each multiplexed TMT sample as well as the combined protein expression matrix containing all TMT channels labeled according to the annotation.txt file. You should have new .tsv files in your workspace, which contain the filtered PSM, peptide, ion, and protein identifications.