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Nesvilab / PD-Nodes

The implementation of MSFragger and Philosopher (PeptideProphet) as Proteome Discoverer nodes
http://www.nesvilab.org/PD-Nodes/
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Open_Search_Failure using High Performance Desktop with Proteome Discover version 2.3 #11

Closed BenSamy2020 closed 3 years ago

BenSamy2020 commented 3 years ago

Greetings,

Currently I am using a high performance desktop (please refer to image for desktop spec) to performance open search. I am experiencing error messages on proteome discover version 2.3 while performing these searches. I am using the most up to data version of MSFragger. Please refer to attachment for error messages and MSFragger search setting (image split into two). Please do advise me on how to successfully perform these searches.

Lastly, i would like to thank you i advance for your assistance.

Computer_Spec Open_Search_Error Open_Search_Setting_1 Open_Search_Setting_2

huiyinc commented 3 years ago

Hi~

It looks like the error occurs when you run MSFragger.

@fcyu Could you please take a look of the error message and let us know what might be the reason? Thanks.

Huiyin

fcyu commented 3 years ago

Hi @huiyinc and @BenSamy2020,

I have no idea.

Best,

Fengchao

BenSamy2020 commented 3 years ago

Greetings All,

When I drop the max peptide length to 35 the runs goes successfully. Maybe it might be memory issue..?

Regards Samy

anesvi commented 3 years ago

I am curious how many PTMs you specified? And how much memory you have on your computer. But yes, memory issue is a possible explanation

From: BenSamy2020 notifications@github.com Sent: Thursday, August 20, 2020 7:07 PM To: Nesvilab/PD-Nodes PD-Nodes@noreply.github.com Cc: Subscribed subscribed@noreply.github.com Subject: Re: [Nesvilab/PD-Nodes] Open_Search_Failure using High Performance Desktop with Proteome Discover version 2.3 (#11)

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Greetings All,

When I drop the max peptide length to 35 the runs goes successfully. Maybe it might be memory issue..?

Regards Samy

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BenSamy2020 commented 3 years ago

Greetings All,

I have attached here my MSFragger Open-Search setting and computer spec below as an image. I also did a comparison between Open and Close search using MSfragger on PD 2.3 (Ven Diagram below).

Lastly, I would like thank all of you for developing such an amazing tool where OpenSearch could be done on Proteome Discover using MSFragger!

Regards, Samy

Open_Search_VS_Close_Search MSFragger_Setting_Part-1 MSFragger_Setting_Part-2 High_Performace_Desktop_Specs

ghost commented 3 years ago

Hi Samy,

Thanks very much for the sharing!

Huiyin

BenSamy2020 notifications@github.com 於 2020年8月21日 上午8:41 寫道：



Greetings All,

I have attached here my MSFragger Open-Search setting and computer spec below as an image. I also did a comparison between Open and Close search using MSfragger on PD 2.3 (Ven Diagram below).

Lastly, I would like thank all of you for developing such an amazing tool where OpenSearch could be do on Proteome Discover using MSFragger!

Regards, Samy

[image: Open_Search_VS_Close_Search] https://user-images.githubusercontent.com/69944967/90839354-ac18da00-e39a-11ea-824e-7dbbdd218414.png [image: MSFragger_Setting_Part-1] https://user-images.githubusercontent.com/69944967/90839355-ad4a0700-e39a-11ea-9c45-62fff5c3a58a.JPG [image: MSFragger_Setting_Part-2] https://user-images.githubusercontent.com/69944967/90839358-ae7b3400-e39a-11ea-80d3-5585d6375aae.JPG [image: High_Performace_Desktop_Specs] https://user-images.githubusercontent.com/69944967/90839359-af13ca80-e39a-11ea-924b-1a3d44088c29.JPG

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BenSamy2020 commented 3 years ago

Hi Huiyin,

By an chance based on my MSFragger setting shown above for the PD node, can I improve my assignment percentage (PSM : MS/MS)? (please do suggest alternative setting that I can utilize) Currently, for open search I am attaining ~2.3% MS/MS assigned to PSM.

Lastly, thank you for your assistance.

Regards, Samy

huiyinc commented 3 years ago

Hi Samy,

I think this is a good question. There are still some issues that need to be addressed for the open search in PD. For example, not all mass shifts (i.e., the mass difference between observed and theoretical peptides) are well interpreted which I think is one possible reason that the assignment rate (# of PSM / # of MS2) is relatively low. We are working on the issues and hope we can provide a better solution soon. I am sorry that I don't have any parameter suggestions for you now. But if you would like to process data using open search, using FragPipe is a good solution.

Thanks!

Huiyin

BenSamy2020 notifications@github.com 於 2020年8月24日 週一 下午2:36寫道：

Hi Huiyin,

By an chance based on my MSFragger setting shown above for the PD node, can I improve my assignment percentage (PSM : MS/MS)? (please do suggest alternative setting that I can utilize) Currently, for open search I am attaining ~2.3% MS/MS assigned to PSM.

Lastly, thank you for your assistance.

Regards, Samy

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/Nesvilab/PD-Nodes/issues/11#issuecomment-678934130, or unsubscribe https://github.com/notifications/unsubscribe-auth/ALAWWA5CQKA4NM7PUXU4PUDSCIC5LANCNFSM4QFW336A .

-- Hui-Yin Chang, 張彙音 Assistant Professor, 助理教授 Department of Biomedical Sciences and Engineering, 生醫科學與工程學系 National Central University (Taiwan), 國立中央大學 (台灣)

BenSamy2020 commented 3 years ago

Greetings Huiyin,

Firstly, I would like to thank you for your prompt reply. I will give FragPipe a go (never used before though). Will see if the assignment percentage increases compared to close search using FragPipe.

Regards, Samy

huiyinc commented 3 years ago

Hi Samy,

No problem and thanks for your understanding.

Huiyin

BenSamy2020 notifications@github.com 於 2020年8月24日 週一 下午3:50寫道：

Greetings Huiyin,

Firstly, I would like to thank you for your prompt reply. I will give FragPipe a go (never used before though). Will see if the assignment percentage increases compared to close search using FragPipe.

Regards, Samy

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/Nesvilab/PD-Nodes/issues/11#issuecomment-678968304, or unsubscribe https://github.com/notifications/unsubscribe-auth/ALAWWA2W3TJGQZGRG6DCCITSCILU5ANCNFSM4QFW336A .

-- Hui-Yin Chang, 張彙音 Assistant Professor, 助理教授 Department of Biomedical Sciences and Engineering, 生醫科學與工程學系 National Central University (Taiwan), 國立中央大學 (台灣)