Why does LFQ-MBR workflow generate DIA-NN input?

[Maithy15]

Dear Fragpipe team,

I am searching TimsToF IM enabled DDA files on the latest version of Fragpipe using the standard LFQ-MBR workflow. Everything works fine but when I looked at the log, Fragpipe seems to have created DIA-NN input file. Is this standard for LFQ workflow now? It also tries to look for TMT modificaition. Both DIA-NN and TMT integrator are switched off. Not sure if this is a problem, please find the extract from the log file.

Here is the extract from the Log file

Generating input file for DIA-NN 986028 unique peptides from 3604821 PSMs Writing DIA-NN input file Diann input file generation took 10114 milliseconds Input file at E:\Marlene_Project\Marlene_Raw_data\Mouse_samples\Kidney\fragpipe_result_kidney\BB_K_TFE_2\spectraRT.tsv 986028 unique peptides from 3604821 PSMs createFull input file generation took 9334 milliseconds Input file at E:\Marlene_Project\Marlene_Raw_data\Mouse_samples\Kidney\fragpipe_result_kidney\BB_K_TFE_2\spectraRT_full.tsv Generating DIA-NN predictions C:\softwares\fragpipe_v_19.1\tools\diann\1.8.2_beta_8\win\DiaNN.exe --lib E:\Marlene_Project\Marlene_Raw_data\Mouse_samples\Kidney\fragpipe_result_kidney\BB_K_TFE_2\spectraRT.tsv --predict --threads 63 --strip-unknown-mods --mod TMT,229.1629 --predict-n-frag 100 DIA-NN 1.8.2 beta 8 (Data-Independent Acquisition by Neural Networks) Compiled on Sep 15 2022 18:28:57 Current date and time: Sat Feb 25 07:59:25 2023 CPU: AuthenticAMD AMD Ryzen Threadripper 3990X 64-Core Processor SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 SSE4a Logical CPU cores: 64 Predicted spectra will be saved in a binary format Thread number set to 63 DIA-NN will use deep learning to predict spectra/RTs/IMs even for peptides carrying modifications which are not recognised by the deep learning predictor. In this scenario, if also generating a spectral library from the DIA data or using the MBR mode, it might or might not be better (depends on the data) to also use the --out-measured-rt option - it's recommended to test it with and without this option Modification TMT with mass delta 229.163 added to the list of recognised modifications for spectral library-based search Deep learning predictor will predict 100 fragments Cannot find a UniMod modification match for TMT: 73.0618 minimal mass discrepancy; using the original modificaiton name

Thanks Maithy

[anesvi]

DIA-NN is also called by MSBooster to predict RT and spectra for rescoring.

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From: Maithy15 @.> Sent: Friday, March 3, 2023 6:48:52 AM To: Nesvilab/FragPipe @.> Cc: Subscribed @.***> Subject: [Nesvilab/FragPipe] Why does LFQ-MBR workflow generate DIA-NN input? (Issue #1026)

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Dear Fragpipe team,

I am searching TimsToF IM enabled DDA files on the latest version of Fragpipe using the standard LFQ-MBR workflow. Everything works fine but when I looked at the log, Fragpipe seems to have created DIA-NN input file. Is this standard for LFQ workflow now? It also tries to look for TMT modificaition. Both DIA-NN and TMT integrator are switched off. Not sure if this is a problem, please find the extract from the log file.

Here is the extract from the Log file

Generating input file for DIA-NN 986028 unique peptides from 3604821 PSMs Writing DIA-NN input file Diann input file generation took 10114 milliseconds Input file at E:\Marlene_Project\Marlene_Raw_data\Mouse_samples\Kidney\fragpipe_result_kidney\BB_K_TFE_2\spectraRT.tsv 986028 unique peptides from 3604821 PSMs createFull input file generation took 9334 milliseconds Input file at E:\Marlene_Project\Marlene_Raw_data\Mouse_samples\Kidney\fragpipe_result_kidney\BB_K_TFE_2\spectraRT_full.tsv Generating DIA-NN predictions C:\softwares\fragpipe_v_19.1\tools\diann\1.8.2_beta_8\win\DiaNN.exe --lib E:\Marlene_Project\Marlene_Raw_data\Mouse_samples\Kidney\fragpipe_result_kidney\BB_K_TFE_2\spectraRT.tsv --predict --threads 63 --strip-unknown-mods --mod TMT,229.1629 --predict-n-frag 100 DIA-NN 1.8.2 beta 8 (Data-Independent Acquisition by Neural Networks) Compiled on Sep 15 2022 18:28:57 Current date and time: Sat Feb 25 07:59:25 2023 CPU: AuthenticAMD AMD Ryzen Threadripper 3990X 64-Core Processor SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 SSE4a Logical CPU cores: 64 Predicted spectra will be saved in a binary format Thread number set to 63 DIA-NN will use deep learning to predict spectra/RTs/IMs even for peptides carrying modifications which are not recognised by the deep learning predictor. In this scenario, if also generating a spectral library from the DIA data or using the MBR mode, it might or might not be better (depends on the data) to also use the --out-measured-rt option - it's recommended to test it with and without this option Modification TMT with mass delta 229.163 added to the list of recognised modifications for spectral library-based search Deep learning predictor will predict 100 fragments Cannot find a UniMod modification match for TMT: 73.0618 minimal mass discrepancy; using the original modificaiton name

Thanks Maithy

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[fcyu]

As Alexey pointed out, MSBooster used the prediction module in DIA-NN to predict the spectra and RT. They are used to calculate additional deep-learning scores for Percolator rescoring.

Best,

Fengchao

[Maithy15]

Thank you very much for the fast reply and the explanation!