Search variant peptide

[XiaofengxBjmu]

Dear team,
I want search for thousands of variant peptides that contain single amino acid substitutions. I use philosopher default pipeline, database search, peptideprophet, proteinprophet and fdr filter, but there were only a few results. Do you have any suggestions?
If I only want PSM level FDR, is follow the right param? (By the way, what is the difference between psmFDR and peptideFDR?)
Thanks a lot.

psmFDR: 0.01
peptideFDR: 1
ionFDR: 1
proteinFDR: 1

This is one of the variant peptide database:

sp|Q9UPT6|JIP3_HUMAN Q9UPT6,16_1748672_A,389,A/T,358,434 OS=Homo sapiens OX=9606 GN=MAPK8IP3 PE=1 SV=3
LDRTGSSPTQGIVNKAFGINTDSLYHELSTTGSEVIGDVDEGADLLGEFSVRDDFFGMGKEVGNLLLENSQLLETK

And it's reference peptide:

sp|Q9UPT6|JIP3_HUMAN C-Jun-amino-terminal kinase-interacting protein 3 OS=Homo sapiens OX=9606 GN=MAPK8IP3 PE=1 SV=3
LDRTGSSPTQGIVNKAFGINTDSLYHELSTAGSEVIGDVDEGADLLGEFSVRDDFFGMGKEVGNLLLENSQLLETK

[prvst]

Hi @XiaofengxBjmu. It's a little hard to access the entire situation by the brief description you have given us. There can be multiple reasons that explain why your searches are not returning more identifications. They can be technical, like using the wrong parameters, or experimental, caused by low abundance proteins, and in both cases, the biology behind your experiment will dictate how difficult will be for you to find these rare peptides. This is specially difficult because you are searching for point mutations, and in order to find them, the instrument needs to sequence the right peptides containing these mutations. What I can tell you is that for the FDR filtering, the so-called layers (i.e., PSM, peptides, ions, proteins), are filtered individually, meaning that if you are interested in the PSM alone, then you don't need to change the other thresholds. What you can try is to move the PSM filtering threshold up, allowing more PSMs to be approved, maybe try using a 3% (--psmFDR 0.03) or 5% (--psmFDR 0.05) threshold. This way you'll increase the number of PSMs that get approved, increasing the chances of finding them.

[prvst]

did you run the report too?

[XiaofengxBjmu]

I had Individual Reports: yes in pipeline, is this right?

[prvst]

Yes, so the tables will be different. If not, then you're missing something. Have you tried running FragPipe instead?

[XiaofengxBjmu]

Let me try, and is there a parameter for handling CPTAC CCRCC proteome and phosphoproteome so that I can check my error. Thanks a lot.
The method section of the article contains only a small portion of one parameter.

[prvst]

Yes, check the workflows inside the program. The other parameters were not mentioned because they were the default ones.

[XiaofengxBjmu]

Dear @prvst , I have one more question.
I got the ion.tsv, localization.tsv, modifications.tsv, peptide.tsv and psm.tsv after completing the pipeline of philosopher. I only want to get the corresponding PSM of the variant peptides that I'm interested in, so I use the psm.tsv so far.
If FDR filtering are filtered individually and I want to get the results passing 1% PSM-level FDR, 1% protein-level FDR and 1% site-level FDR, which file can I get the final result. Thanks a lot.

[prvst]

use the psm.tsv for the PSMs, peptide.tsv for peptides, and so on.

[XiaofengxBjmu]

Sorry for asking a basic question, will some PSM that not passing 1% protein-level FDR be removed? That is, how do I determine which PSM in psm.tsv passing 1% protein-level FDR and which does not?
Thanks a lot.

[prvst]

only the FDR-approved PSMs are allowed to be included in the tables.

[pwilmart]

Hi All, Jumping in here to recommend that the TMT reagents be specified as static modifications instead of variable modifications. I also never specify protein N-terminal acetylation in any searches I do. You want to keep theoretical search space as simple (and small) as possible when doing a phospho search. Phospho really enlarges the theoretical search space. Cheers, Phil

…

On Mar 2, 2022, at 7:08 AM, XiaofengxBjmu ***@***.***> wrote: And philosopher like this: analytics: true # reports when a workspace is created for usage statistics slackToken: # specify the Slack API token (how to generate a token: https://api.slack.com/legacy/custom-integrations/legacy-tokens <https://api.slack.com/legacy/custom-integrations/legacy-tokens>) slackChannel: # specify the channel name, or slackUserID: # specify a user ID for a direct message Steps: Database Search: yes # peptide to spectrum matching with Comet or MSFragger Peptide Validation: yes # peptide assignment validation with PeptideProphet PTM Localization: yes # PTM site localization with PTMProphet Protein Inference: yes # protein identification validation with ProteinProphet Label-Free Quantification: no # precursor label-free quantification inspired by moFF Isobaric Quantification: no # isobaric labeling-based relative quantification for TMT and iTRAQ Bio Cluster Quantification: no # protein report based on Uniprot protein clusters FDR Filtering: yes # statistical filtering, validation and false discovery r ates assessment Individual Reports: yes # multi-level reporting for both narrow-searches and open-searches Integrated Reports: no # combined analysis of LC-MS/MS results inspired by Abacus Integrated Isobaric Quantification: no # integrates channel abundances from multiple isobaric-tagged samples with TMT-Integrator Database Search: # MSFragger 3.1 & Comet protein_database: /gpfs/share/home/1108_CPTAC/philosopher/db/2022-03-01-decoys-contam-var_01.fa.fas # path to the target-decoy protein database decoy_tag: rev_ # prefix tag used added to decoy sequences search_engine: comet # search engine options include "comet" and "msfragger" comet: # Comet v2019011 noindex: true # skip mzML file indexing param: comet_var_TMT_HL.params # comet parameter file (default "comet.params.txt") raw: mzML # format of the spectra file msfragger: # MSFragger v3.0 path: # path to MSFragger jar memory: 128 # 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: -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. mass_diff_to_variable_mod: 0 # Put mass diff as a variable modification. 0 for no; 1 for yes and change the original mass diff and the calculated mass accordingly; 2 for yes but do not change the original mass diff. labile_search_mode: "off" # type of search (nglycan, labile, or off). Off means non-labile/typical search. restrict_deltamass_to: all # Specify amino acids on which delta masses (mass offsets or search modifications) can occur. Allowed values are single letter codes (e.g. ACD), must diagnostic_intensity_filter: 0 # [nglycan/labile search_mode only]. Minimum relative intensity for SUM of all detected oxonium ions to achieve for spectrum to contain diagnostic fragment evidence. Calculated relative to spectrum base peak. 0 <= value. Y_type_masses: # [nglycan/labile search_mode only]. Specify fragments of labile mods that are commonly retained on intact peptides (e.g. Y ions for glycans). Only used if 'Y' is included in fragment_ion_series. diagnostic_fragments: # [nglycan/labile search_mode only]. Specify diagnostic fragments of labile mods that appear in the low m/z region. Only used if diagnostic_intensity_filter > 0. 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 Peptide Validation: # PeptideProphet v5.2 concurrent: true # Concurrent execution of multiple instaces extension: pep.xml # 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 PTM Localization: # PTMProphet v6.0 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 excludemassdiffmin: 0 # Minimum mass difference excluded for MASSDIFFFMODE analysis (default=0) excludemassdiffmax: 0 # Maximun mass difference excluded for MASSDIFFFMODE analysis (default=0) 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: 16 # 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: STY:79.9663,M:15.9949,S:229.1629 # 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 Protein Inference: # 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 estimate) unmapped: false # report results for UNMAPPED proteins Label-Free Quantification: # 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) Isobaric Quantification: # Labelquant 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 plex: # number of channels purity: 0.5 # ion purity threshold (default 0.5) removeLow: 0.0 # 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 brand: tmt # isobaric labeling brand (tmt, itraq) Bio Cluster Quantification: # BioQuant organismUniProtID: # UniProt proteome ID level: 0.9 # cluster identity level (default 0.9) FDR Filtering: # 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: 1 # 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: false # print model distribution sequential: true # alternative algorithm that estimates FDR using both filtered PSM and Protein lists Individual Reports: # Report msstats: false # create an output compatible to MSstats withDecoys: false # add decoy observations to reports mzID: true # create a mzID output Integrated Reports: # Abacus protein: false # 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 Integrated Isobaric Quantification: # TMT-Integrator v1.1.10 path: # path to TMT-Integrator jar memory: 128 # 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 reference; 2: use median as the reference) max_pep_prob_thres: 0 # the threshold for maximum peptide probability min_ntt: 0 # minimum allowed number of enzymatic termini — Reply to this email directly, view it on GitHub <#320 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFZNQSUK5LM2HANX72ZMFADU5575NANCNFSM5PV7JRDQ>. 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[prvst]

Thanks for the comment, Phil

[XiaofengxBjmu]

Thanks a lot!