I agree with this. This is a great new feature in the latest version
(7.1.0). In earlier versions we had to colaculate nSIN for every file
separately in that case we needed to take the average of SIN score
separately for biological replicates and then compare among the groups.

Its a good feature.

Best

On Fri, Aug 16, 2024 at 12:37 AM 'David Shteynberg' via spctools-discuss <
[email protected]> wrote:

> Hello Shagun,
>
> I think there is nothing specific in the TPP that makes it more or less
> suited for a particular instrument, manufacturer or experimental setup,
> however, it is important to give the tools good parameters to expect good
> results, assuming good data.
>
> As far as new features in the TPP 7.0.0 (and beyond,)  StPeter label-free
> quantitation that computes spectral indices has new features in TPP 7 that
> allow it to separate the protein quantities in one ProteinProphet resulting
> protXML file, "by run" or "by experiment" which helps the user compare
> protein quantities across runs or across experiments.  We hope this is
> useful.
>
> Cheers!
>
> -David
>
> On Thu, Aug 15, 2024 at 10:34 AM Shagun Gupta <[email protected]> wrote:
>
>> Hi David
>>
>> Thank you for your prompt response. I’ll try out your suggestions today
>> and will update you on the results or if I encounter any issues.
>>
>> Quick question—are there any specific features in the latest version of
>> TPP that make it particularly well-suited for DDA-LFQ analysis on Bruker
>> instruments?
>>
>> Best
>> Shagun
>>
>> On Wednesday, August 14, 2024 at 11:47:31 AM UTC-4 David Shteynberg wrote:
>>
>>> Hello Shagun,
>>>
>>> I was able to run comet and the TPP on these files from the latest TPP
>>> version 7.1.0.  After adding two independent deBruijn randomized decoys to
>>> the database using the Petunia Decoy Database tool, my pipeline found
>>> approximately 80 thousand to almost 90 thousand PSMs at 1% spectrum error
>>> rate, per mzML file.  These map to about 68 thousand unique peptides at 1%
>>> peptide error rate, after iProphet combining all the files.  ProteinProphet
>>> mapped all of these to just over 7000 proteins at 1% protein error rate (or
>>> lower.)
>>>
>>> The biggest issue I found in your comet params file was the was
>>> n-terminal acetylation was specified using MSFragger notation ‘[^’ for the
>>> amino acid (should be ’n’ instead for comet.)  Unfortunately this confused
>>> the pipeline and exposed downstream assumptions that broke the analysis.
>>>
>>> I am attaching the comet params file I used to process this data.
>>>
>>> Cheers!
>>> -David
>>>
>>> P.S.  Please update your TPP to the latest 7.1.0 to get the latest
>>> features and bug-fixes!
>>>
>>> --
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Dr. Sudarshan Kumar
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