Hi Jimmy,
Thank you for your response!
I worked with kind of samples like b1369p080_sample_01_a . There are 14 of
them, each divided to fractions _a and _b. I merged the fractions and did
search on 14 raw files.
I did not take into account that those are high-res MS/MS. Thank you for a
valuable notice and suggested parameters.
The fact that you also identified just a few high-scoring PSMs suggests
that maybe my input parameters were not far from yours.I attach comet.params
file. I will appreciate your comments on it.
Valeriia
# comet_version 2021.01 rev. 0
# Comet MS/MS search engine parameters file.
# Everything following the '#' symbol is treated as a comment.
database_name =
decoy_search = 1 # 0=no (default), 1=concatenated
search, 2=separate search
peff_format = 0 # 0=no (normal fasta, default),
1=PEFF PSI-MOD, 2=PEFF Unimod
peff_obo = # path to PSI Mod or Unimod OBO file
num_threads = 0 # 0=poll CPU to set num threads;
else specify num threads directly (max 128)
#
# masses
#
peptide_mass_tolerance = 20.00
peptide_mass_units = 2 # 0=amu, 1=mmu, 2=ppm
mass_type_parent = 1 # 0=average masses, 1=monoisotopic
masses
mass_type_fragment = 1 # 0=average masses, 1=monoisotopic
masses
precursor_tolerance_type = 1 # 0=MH+ (default), 1=precursor m/z;
only valid for amu/mmu tolerances
isotope_error = 1 # 0=off, 1=0/1 (C13 error), 2=0/1/2,
3=0/1/2/3, 4=-8/-4/0/4/8 (for +4/+8 labeling)
#
# search enzyme
#
search_enzyme_number = 1 # choose from list at end of this
params file
search_enzyme2_number = 0 # second enzyme; set to 0 if no
second enzyme
num_enzyme_termini = 2 # 1 (semi-digested), 2 (fully
digested, default), 8 C-term unspecific , 9 N-term unspecific
allowed_missed_cleavage = 2 # maximum value is 5; for enzyme
search
#
# Up to 9 variable modifications are supported
# format: <mass> <residues> <0=variable/else binary>
<max_mods_per_peptide> <term_distance> <n/c-term> <required> <neutral_loss>
# e.g. 79.966331 STY 0 3 -1 0 0 97.976896
#
variable_mod01 = 15.9949 M 0 3 -1 0 0 0.0
variable_mod02 = 0.0 X 0 3 -1 0 0 0.0
variable_mod03 = 0.0 X 0 3 -1 0 0 0.0
variable_mod04 = 0.0 X 0 3 -1 0 0 0.0
variable_mod05 = 0.0 X 0 3 -1 0 0 0.0
variable_mod06 = 0.0 X 0 3 -1 0 0 0.0
variable_mod07 = 0.0 X 0 3 -1 0 0 0.0
variable_mod08 = 0.0 X 0 3 -1 0 0 0.0
variable_mod09 = 0.0 X 0 3 -1 0 0 0.0
max_variable_mods_in_peptide = 5
require_variable_mod = 0
#
# fragment ions
#
# ion trap ms/ms: 1.0005 tolerance, 0.4 offset (mono masses),
theoretical_fragment_ions = 1
# high res ms/ms: 0.02 tolerance, 0.0 offset (mono masses),
theoretical_fragment_ions = 0, spectrum_batch_size = 15000
#
fragment_bin_tol = 1.0005 # binning to use on fragment ions
fragment_bin_offset = 0.0 # offset position to start the
binning (0.0 to 1.0)
theoretical_fragment_ions = 0 # 0=use flanking peaks, 1=M peak only
use_A_ions = 0
use_B_ions = 1
use_C_ions = 0
use_X_ions = 0
use_Y_ions = 1
use_Z_ions = 0
use_Z1_ions = 0
use_NL_ions = 1 # 0=no, 1=yes to consider NH3/H2O
neutral loss peaks
#
# output
#
output_sqtfile = 0 # 0=no, 1=yes write sqt file
output_txtfile = 0 # 0=no, 1=yes write tab-delimited
txt file
output_pepxmlfile = 1 # 0=no, 1=yes write pepXML file
output_mzidentmlfile = 0 # 0=no, 1=yes write mzIdentML file
output_percolatorfile = 0 # 0=no, 1=yes write Percolator pin
file
print_expect_score = 1 # 0=no, 1=yes to replace Sp with
expect in out & sqt
num_output_lines = 5 # num peptide results to show
sample_enzyme_number = 1 # Sample enzyme which is possibly
different than the one applied to the search.
# Used to calculate NTT & NMC in
pepXML output (default=1 for trypsin).
#
# mzXML parameters
#
scan_range = 0 0 # start and end scan range to
search; either entry can be set independently
precursor_charge = 0 0 # precursor charge range to analyze;
does not override any existing charge; 0 as 1st entry ignores parameter
override_charge = 0 # 0=no, 1=override precursor charge
states, 2=ignore precursor charges outside precursor_charge range, 3=see
online
ms_level = 2 # MS level to analyze, valid are
levels 2 (default) or 3
activation_method = ALL # activation method; used if
activation method set; allowed ALL, CID, ECD, ETD, ETD+SA, PQD, HCD, IRMPD,
SID
#
# misc parameters
#
digest_mass_range = 500.0 6000.0 # MH+ peptide mass range to analyze
peptide_length_range = 7 30 # minimum and maximum peptide length
to analyze (default 1 63; max length 63)
num_results = 50 # number of search hits to store
internally
max_duplicate_proteins = 20 # maximum number of additional
duplicate protein names to report for each peptide ID; -1 reports all
duplicates
max_fragment_charge = 3 # set maximum fragment charge state
to analyze (allowed max 5)
max_precursor_charge = 6 # set maximum precursor charge state
to analyze (allowed max 9)
nucleotide_reading_frame = 0 # 0=proteinDB, 1-6, 7=forward three,
8=reverse three, 9=all six
clip_nterm_methionine = 1 # 0=leave sequences as-is; 1=also
consider sequence w/o N-term methionine
spectrum_batch_size = 30000 # max. # of spectra to search at a
time; 0 to search the entire scan range in one loop
decoy_prefix = DECOY_ # decoy entries are denoted by this
string which is pre-pended to each protein accession
equal_I_and_L = 1 # 0=treat I and L as different;
1=treat I and L as same
output_suffix = # add a suffix to output base names
i.e. suffix "-C" generates base-C.pep.xml from base.mzXML input
mass_offsets = # one or more mass offsets to search
(values substracted from deconvoluted precursor mass)
precursor_NL_ions = # one or more precursor neutral loss
masses, will be added to xcorr analysis
#
# spectral processing
#
minimum_peaks = 10 # required minimum number of peaks
in spectrum to search (default 10)
minimum_intensity = 0 # minimum intensity value to read in
remove_precursor_peak = 0 # 0=no, 1=yes, 2=all charge reduced
precursor peaks (for ETD), 3=phosphate neutral loss peaks
remove_precursor_tolerance = 1.5 # +- Da tolerance for precursor
removal
clear_mz_range = 0.0 0.0 # for iTRAQ/TMT type data; will
clear out all peaks in the specified m/z range
#
# additional modifications
#
add_Cterm_peptide = 0.0
add_Nterm_peptide = 0.0
add_Cterm_protein = 0.0
add_Nterm_protein = 0.0
add_G_glycine = 0.0000 # added to G - avg. 57.0513, mono.
57.02146
add_A_alanine = 0.0000 # added to A - avg. 71.0779, mono.
71.03711
add_S_serine = 0.0000 # added to S - avg. 87.0773, mono.
87.03203
add_P_proline = 0.0000 # added to P - avg. 97.1152, mono.
97.05276
add_V_valine = 0.0000 # added to V - avg. 99.1311, mono.
99.06841
add_T_threonine = 0.0000 # added to T - avg. 101.1038, mono.
101.04768
add_C_cysteine = 57.021464 # added to C - avg. 103.1429, mono.
103.00918
add_L_leucine = 0.0000 # added to L - avg. 113.1576, mono.
113.08406
add_I_isoleucine = 0.0000 # added to I - avg. 113.1576, mono.
113.08406
add_N_asparagine = 0.0000 # added to N - avg. 114.1026, mono.
114.04293
add_D_aspartic_acid = 0.0000 # added to D - avg. 115.0874, mono.
115.02694
add_Q_glutamine = 0.0000 # added to Q - avg. 128.1292, mono.
128.05858
add_K_lysine = 0.0000 # added to K - avg. 128.1723, mono.
128.09496
add_E_glutamic_acid = 0.0000 # added to E - avg. 129.1140, mono.
129.04259
add_M_methionine = 0.0000 # added to M - avg. 131.1961, mono.
131.04048
add_H_histidine = 0.0000 # added to H - avg. 137.1393, mono.
137.05891
add_F_phenylalanine = 0.0000 # added to F - avg. 147.1739, mono.
147.06841
add_U_selenocysteine = 0.0000 # added to U - avg. 150.0379, mono.
150.95363
add_R_arginine = 0.0000 # added to R - avg. 156.1857, mono.
156.10111
add_Y_tyrosine = 0.0000 # added to Y - avg. 163.0633, mono.
163.06333
add_W_tryptophan = 0.0000 # added to W - avg. 186.0793, mono.
186.07931
add_O_pyrrolysine = 0.0000 # added to O - avg. 237.2982, mono
237.14773
add_B_user_amino_acid = 0.0000 # added to B - avg. 0.0000, mono.
0.00000
add_J_user_amino_acid = 0.0000 # added to J - avg. 0.0000, mono.
0.00000
add_X_user_amino_acid = 0.0000 # added to X - avg. 0.0000, mono.
0.00000
add_Z_user_amino_acid = 0.0000 # added to Z - avg. 0.0000, mono.
0.00000
#
# COMET_ENZYME_INFO _must_ be at the end of this parameters file
#
[COMET_ENZYME_INFO]
0. Cut_everywhere 0 - -
1. Trypsin 1 KR P
2. Trypsin/P 1 KR -
3. Lys_C 1 K P
4. Lys_N 0 K -
5. Arg_C 1 R P
6. Asp_N 0 D -
7. CNBr 1 M -
8. Glu_C 1 DE P
9. PepsinA 1 FL P
10. Chymotrypsin 1 FWYL P
11. No_cut 1 @ @
суббота, 15 марта 2025 г. в 00:08:09 UTC+2, Jimmy Eng:
> Valeriia,
>
> That PXD003594 experiment has 76 raw files associated with. Were there a
> subset of raw files that you analyzed here or does your analysis include
> all 76 runs? Just for a quick test, I downloaded 4 raw files from that
> experiment and searched it with Comet against the UniProt human database.
> Here's a very basic summary:
>
> - b1369p080_sample_01_a.raw: high res MS/MS, almost no IDs (less
> than 100 positive PSMs)
> - b1369p601_DMSO_G1_B2_S10.RAW: ion trap MS/MS, ~6000 PSMs at 1%
> error rate
> - b1369p601_GDC_G2_B3_S23.RAW: ion trap MS/MS, ~6000 PSMs at 1%
> error rate
> - b1369p65_PP4_R.RAW: ion trap MS/MS, ~500 PSMs at 1% error
> rate
>
> So there are runs with thousands of good PSM IDs in them. Note that in
> the 4 raw files that I sampled, there was a mix of high-res and low-res
> MS/MS spectra so hopefully you adjusted the fragment ion settings
> appropriately for each raw file using my suggested parameter settings shown
> below. If the fragment ion settings aren't the issue, feel free to
> follow-up including attaching the contents of your comet.params file.
>
> high-res:
> fragment_bin_tol = 0.02
> fragment_bin_offset = 0.0
> theoretical_fragment_ions = 0
>
> low-res:
> fragment_bin_tol = 1.0005
> fragment_bin_offset = 0.4
> theoretical_fragment_ions = 1
>
> Jimmy
>
> On Thu, Mar 13, 2025 at 5:02 PM Valeriia Vasylieva <[email protected]>
> wrote:
>
>> Hi.
>> I run Comet and Peptideprophet on two public datasets with TDC with
>> UniProt. I calculated the q-value in Python based on fval distribution and
>> filtered data with a threshold 1%. Like that:
>> #df - PeptideProphet outputdf = df.sort_values(by='fval',
>> ascending=False).reset_index(drop=True)
>> # Calculate cumulative counts of targets and decoys df['cum_targets'] =
>> (df['database'] == 'T').cumsum() df['cum_decoys'] = (df['database'] ==
>> 'D').cumsum() # Calculate FDR df['FDR'] = df['cum_decoys'] /
>> df['cum_targets'] # cumulative minimum from bottom to top df['q-value'] =
>> df['FDR'][::-1].cummin()[::-1]
>>
>> Lower you see the proportion of PSMs annotated as targets and decoys
>> which passed the value threshold or not. One of the datasets (PXD03594)
>> has a very low number of identifications. It also has a wide distribution
>> of decoys (on the graph the raw files are plotted together). Could anyone
>> suggest what could have happened here? I used default parameters, just
>> changed peptide length from 7 to 30 aa, and peptide mass range
>> 500.0-6000.0, and also enabled Methioning clipping.
>> Thanks!
>>
>> [image: Capture9.PNG][image: Capture8.PNG]
>>
>>
>> [image: Capture6.PNG][image: Capture7.PNG]
>>
>>
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