Hi,
I am using peptideprophet for the statistical validation of X!Tandem
results. I am using the options: Use accurate mass binning, using ppm, Use
Hydrophobicity / RT information, Use decoy hits to pin down the negative
distribution. Decoy protein names begin with'rev_', Use Non-parametric
model (can only be used with decoy option) and Report decoy hits with a
computed probability (based on the model learned). I have acquired the data
on AB Sciex system but I used msconvert for conversion of .wiff files to
mzXML format.
But I am getting the following error message:
Found 676 Decoys, and 834 Non-Decoys
Iterations: .........10.........20.....Estimating Retention Time Model ...
please wait ... WARNING: Not enough IDs in run index c:/TPP/data/liver_DDA_1 to
generate RT Gradient Correction.WARNING: Not enough high probability IDs in run
index c:/TPP/data/liver_DDA_1 to generate RT model. RT Model has been disabled.
Run Index: c:/TPP/data/liver_DDA_1, slope=nan, intercept=nan, r_sq=nanWARNING:
Not enough IDs in run index c:/TPP/data/liver_DDA_2 to generate RT Gradient
Correction.WARNING: Not enough high probability IDs in run index
c:/TPP/data/liver_DDA_2 to generate RT model. RT Model has been disabled.
Run Index: c:/TPP/data/liver_DDA_2, slope=nan, intercept=nan, r_sq=nanWARNING:
Not enough IDs in run index c:/TPP/data/liver_DDA_3 to generate RT Gradient
Correction.WARNING: Not enough high probability IDs in run index
c:/TPP/data/liver_DDA_3 to generate RT model. RT Model has been disabled.
Run Index: c:/TPP/data/liver_DDA_3, slope=nan, intercept=nan, r_sq=nanWARNING:
Not enough high probability IDs in run index c:/TPP/data/liver_DDA_1 to
generate RT model. RT Model has been disabled.
Run Index: c:/TPP/data/liver_DDA_1, slope=nan, intercept=nan, r_sq=nanWARNING:
Not enough high probability IDs in run index c:/TPP/data/liver_DDA_2 to
generate RT model. RT Model has been disabled.
Run Index: c:/TPP/data/liver_DDA_2, slope=nan, intercept=nan, r_sq=nanWARNING:
Not enough high probability IDs in run index c:/TPP/data/liver_DDA_3 to
generate RT model. RT Model has been disabled.
Run Index: c:/TPP/data/liver_DDA_3, slope=nan, intercept=nan, r_sq=nan
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 26 iterations
command completed in 4 sec
Could anyone help me out to resolve this problem?
I am also attaching the tandem parameter file also. Also the X!Tandem
results showed that the valid models for first data = 0, while 2 and 4
models for two other data.
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<?xml version="1.0" encoding="UTF-8"?>
<bioml>
<note> DEFAULT PARAMETERS. The value of "isb_default_input_kscore.xml" is
recommended. Change to "isb_default_input_native.xml" for native X!Tandem
scoring.</note>
<note type="input" label="list path, default
parameters">C:/TPP/data/params/isb_default_input_kscore.xml</note>
<note> FILE LOCATIONS. Replace them with your input (.mzXML) file and output
file -- these are REQUIRED. Optionally a log file and a sequence output file of
all protein sequences identified in the first-pass can be specified. Use of
FULL path (not relative) paths is recommended. </note>
<note type="input" label="spectrum, path">full_mzXML_filepath</note>
<note type="input" label="output, path">full_tandem_output_path</note>
<note type="input" label="output, log path"></note>
<note type="input" label="output, sequence path"></note>
<note> TAXONOMY FILE. This is a file containing references to the sequence
databases. Point it to your own taxonomy.xml if needed.</note>
<note type="input" label="list path, taxonomy
information">C:/TPP/data/params/taxonomy.xml</note>
<note> PROTEIN SEQUENCE DATABASE. This refers to identifiers in the
taxomony.xml, not the .fasta files themselves! Make sure the database you want
is present as an entry in the taxonomy.xml referenced above. This is REQUIRED.
</note>
<note type="input" label="protein, taxon">Human</note>
<note> PRECURSOR MASS TOLERANCES. In the example below, a 0 Da to 20.0 ppm
(monoisotopic mass) window is searched for peptide candidates. Since this is
monoisotopic mass, so for non-accurate-mass instruments, for which the
precursor is often taken nearer to the isotopically averaged mass, an
asymmetric tolerance (-2.0 Da to 4.0 Da) is preferable. This somewhat imitates
a (-3.0 Da to 3.0 Da) window for averaged mass (but not exactly)</note>
<note type="input" label="spectrum, parent monoisotopic mass
error">0.0</note>
<note type="input" label="spectrum, parent monoisotopic mass error
plus">20.0</note>
<note type="input" label="spectrum, parent monoisotopic mass error
units">ppm</note>
<note>The value for this parameter may be 'Daltons' or 'ppm':
all other values are ignored</note>
<note type="input" label="spectrum, parent monoisotopic mass isotope
error">no</note>
<note>This allows peptide candidates in windows around -1 Da
and -2 Da from the acquired mass to be considered. Only applicable when the
minus/plus window above is set to less than 0.5 Da. Good for accurate-mass
instruments for which the reported precursor mass is not corrected to the
monoisotopic mass. </note>
<note> MODIFICATIONS. In the example below, there is a static (carbamidomethyl)
modification on C, and variable modifications on M (oxidation). Multiple
modifications can be separated by commas, as in "80.0@S,80.0@T". Peptide
terminal modifications can be specified with the symbol '[' for N-terminus and
']' for C-terminus, such as 42.0@[ . </note>
<note type="input" label="residue, modification mass">57.021464@C</note>
<note type="input" label="residue, potential modification
mass">15.994915@M</note>
<note type="input" label="residue, potential modification motif"></note>
<note> You can specify a variable modification only when
present in a motif. For instance, 0.998@N!{P}[ST] is a deamidation modification
on N only if it is present in an N[any but P][S or T] motif (N-glycosite).
</note>
<note type="input" label="protein, N-terminal residue modification
mass"></note>
<note type="input" label="protein, C-terminal residue modification
mass"></note>
<note> These are *static* modifications on the PROTEINS' N or
C-termini. </note>
<note> SEMI-TRYPTICS AND MISSED CLEAVAGES. In the example below, semitryptic
peptides are allowed, and up to 2 missed cleavages are allowed. </note>
<note type="input" label="protein, cleavage semi">no</note>
<note type="input" label="scoring, maximum missed cleavage
sites">2</note>
<note> REFINEMENT. Do not use unless you know what you are doing. Set "refine"
to "yes" and specify what you want to search in the refinement. For
non-confusing results, repeat the same modifications you set above for the
first-pass here.</note>
<note type="input" label="refine">no</note>
<note type="input" label="refine, maximum valid expectation
value">0.1</note>
<note type="input" label="refine, modification mass">57.012@C</note>
<note type="input" label="refine, potential modification
mass">15.994915@M</note>
<note type="input" label="refine, potential modification motif"></note>
<note type="input" label="refine, cleavage semi">yes</note>
<note type="input" label="refine, unanticipated cleavage">no</note>
<note type="input" label="refine, potential N-terminus
modifications"></note>
<note type="input" label="refine, potential C-terminus
modifications"></note>
<note type="input" label="refine, point mutations">no</note>
<note type="input" label="refine, use potential modifications for full
refinement">no</note>
</bioml>
