Hi Ankit, Actually I see now it does find peptides from the X!tandem search, it first tells you:
Found 676 Decoys, and 834 Non-Decoys That is a low number of peptide hits for what I gather from your filenames is a liver sample. That might be why your models are failing because they need some minimum per charge to pass. I recommend checking if the X!tandem search output before PeptideProphet finds a similar number of peptides as your comet search. In my experience, you should get similar results with msconvert vs sciex converter. You can rule this out by converting with the other one and comparing. You will get a slight improvement using msconvert with the qtofpeakpicker flag compared to sciex or normal msconvert. The conversion will only influence PeptideProphet indirectly by changing your number of hits because although it tries to look for your raw data I don't think it actually needs to unless you want the TPP GUI to associate the two for viewing spectra in your browser. In the end, if your comet search + PeptideProphet works you might just go with that. You do get more by combining searches but for most projects I'm left with more changes than I can interpret anyway. Good luck and best regards, Jesse Jesse G. Meyer, Ph.D. Postdoctoral Fellow Coon Lab Department of Chemistry Department of Biomolecular Chemistry National Center for Quantitative Biology of Complex Systems University of Wisconsin - Madison [email protected] On Thu, Feb 14, 2019 at 2:00 PM Ankit Balhara <[email protected]> wrote: > Hi Jesse, > > Thanks for helping. I also tried the same data on comet also, it is > showing no errors. And I am using same fasta and mass tolerances for comet > search also, and its peptideprophet results are showing no errors. > As I have mentioned that I am using msconvert directly but not AB Sciex > converter for data conversion, could this be the reason of it. Also, I have > attached the tandem parameters file, could you please have a look into that > also. > > Thanks > Ankit > > On 15-Feb-2019 00:43, "Jesse Meyer" <[email protected]> wrote: > >> Hi Ankit, >> >> I've seen this before when my search doesn't produce any hits - usually >> the mixture model fails for only charge 1+, 6+, 7+ because I set the >> instrument to not fragment those charge states, but your model failed all >> of them. Either there are no peptides in your samples, or your search >> settings were very wrong and that prevented you from finding any hits. >> >> I suggest checking your raw data's chromatogram for the presence of >> peptide peaks and also rich MS/MS spectra, and if you have those, then >> double check that you used the correct search settings (e.g. correct fasta, >> correct precursor/fragment tolerances, etc). >> >> >> Best, >> >> Jesse G. Meyer, Ph.D. >> Postdoctoral Fellow >> Coon Lab >> Department of Chemistry >> Department of Biomolecular Chemistry >> National Center for Quantitative Biology of Complex Systems >> University of Wisconsin - Madison >> [email protected] >> >> >> On Thu, Feb 14, 2019 at 12:27 PM Ankit Balhara <[email protected]> >> wrote: >> >>> 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. >>> >>> -- >>> You received this message because you are subscribed to the Google >>> Groups "spctools-discuss" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an email to [email protected]. >>> To post to this group, send email to [email protected]. >>> Visit this group at https://groups.google.com/group/spctools-discuss. >>> For more options, visit https://groups.google.com/d/optout. >>> >> -- >> You received this message because you are subscribed to the Google Groups >> "spctools-discuss" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To post to this group, send email to [email protected]. >> Visit this group at https://groups.google.com/group/spctools-discuss. >> For more options, visit https://groups.google.com/d/optout. >> > -- > You received this message because you are subscribed to the Google Groups > "spctools-discuss" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at https://groups.google.com/group/spctools-discuss. > For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "spctools-discuss" group. 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