Thank you Dr David! That is really great help. 

I have a couple  of follow-up queries:

1) If I use iProphet on these results, does that now use Decoy based FDR 
estimates or the PeptideProphet statisitcal model only estimates.

2) If I combine multiple PeptideProphet outputs (from fractions of same 
digest) in iProphet , how does that affect individual PSMs? For example, if 
a PSM if found in multiple fractions, does it get converted to single PSM 
in the iProphet output (I just want to make the output compatible to 
MSStatsTMT - see here https://groups.google.com/g/msstats/c/aINhWMKt2Co) 
While MSStatsTMT has converters for other SW like Maxquant and PD, I kind 
of like TPP, so trying to establish a proper workflow for my fractionated 
TMT data.

Thanks again,
Debojyoti

On Sunday, May 12, 2024 at 10:54:18 PM UTC+5:30 David Shteynberg wrote:

> Dear Debojyoti,
>
> Welcome to the world of TPP!
>
> If you have a database of targets you can use the following tool in TPP to 
> generate random (repeat preserving deBruijn) decoys for your targets:
>
> [image: image.png]
>
> With the default options this tool will create two independent sets decoys 
> for each of your targets, prefixed by DECOY0 and DECOY1.
>
> After you search the data you can analyze it with PeptideProphet in many 
> different ways.  I would suggest you try with the following options to 
> start:
>
> [image: image.png]
>
> This will enable PeptideProphet to use DECOY0 hits as model-decoys and 
> DECOY1 hits as validation-decoys.
>
> With these setting the table on the models page will contain model-based 
> error estimations based on the model trained with DECOY0 ("known" decoys).
>
> As part of the run with these settings DECOY1 will be used to validate the 
> PeptideProphet model using the "unknown" decoys.  This will be displayed on 
> the models page "Models Charts" tab near the bottom, for example:
> [image: image.png]
>
> The chart on the right shows both the "DECOY" (DECOY1 "unknown") ROC curve 
> and the "PREDICTED" (DECOY0 "known" model-based) ROC curve.  The error 
> estimates comparing the model-based error to the unknown/validation 
> decoy-based error are on the chart on the left.  If you want evaluate a 
> model using a different decoys settings you can run the ProphetModels.pl 
> decoy validation tool on the following page:
> [image: image.png]
>
> On this page set the decoy proteins to the PeptideProphet "model unknown" 
> and the excluded decoys to the PeptideProphet "model known' ones (if any) 
> as follows:
> [image: image.png]
>
> Hopefully this helps you process your dataset.  Let us know if you have 
> additional questions.
>
> Cheers!
> -David
>
>
>
> On Sun, May 12, 2024 at 2:38 AM Debojyoti Pal <[email protected]> wrote:
>
>> Hello everyone
>>
>> Proteomics newbie here. I am trying to use TPP and peptide prophet but 
>> really unable to understand the options and outputs. Seeking help of the 
>> experienced members.[image: peptideprophet.PNG]
>>
>> What are the options that I need to activate to estimate FDR via the 
>> target-decoy mode? I am currently generation decoy through comet and 
>> actuvation "use decoy hits to pin down" option and "known protein names 
>> begin with" option and "use non parametric model option". What do the 
>> option "report decoy hits with computed probability do? And the other 
>> options too? 
>>
>> [image: results.PNG]
>>
>> How is this decoy based FDR calculated?? I am not asking the principle 
>> behind it but I can't see a table for the same? And what is the FDR after 
>> discard?
>>
>> [image: table.PNG]
>>
>> The data in this table is not for decoy based FDR, right? This is the 
>> peptide prophet stattistical model based FDR, correct?
>>
>> I would be highly obliged if anyone could help me out. 
>>
>> Thanks
>> Debojyoti 
>> PhD Student
>>
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