DIA-NN 1.9

[vdemichev]

DIA-NN 1.9 release summary

DIA-NN 1.9 is the biggest improvement of DIA-NN so far. Below is the summary of key features, please see the documentation for details.

Peptidoforms
Data-dependent acquisition (DDA) has so far maintained one key advantage over data-independent acquisition (DIA): confidence in peptidoform assignment. That is, with DDA one can be reasonably confident that a peptide is matched to the spectrum of the correct peptidoform (i.e. without amino acid substitutions or other modifications), and that the set of reported modifications (phosphorylation, etc) is correct. Now we achieve this also with DIA, while maintaining all the advantages of DIA and largely preserving its deep proteome coverage. We expect a range of applications, from pQTL analysis in population proteomics to metaproteomics. A preprint describing the new peptidoform-scoring module in DIA-NN is to follow.

Phosphoproteomics
We use the new peptidoform scoring module to significantly improve phosphoproteomics workflows. Moreover, DIA-NN now reports site-specific localisation confidence along with site-level quantities, in a convenient format, greatly simplifying its use for phosphoproteomics.

Multiplexing
DIA-NN 1.9 features a second-generation plexDIA (multiplexing) module, with a significantly enhanced ability to gain channel-specific confidence in peptide and protein identifications. Further, processing of multiplexed DIA data is greatly simplified by convenient output, including channel-specific protein group quantities obtained with QuantUMS.

timsTOF proteomics
DIA-NN 1.9 implements Slice-PASEF as well as features preliminary support for midia-PASEF and Synchro-PASEF.

Quantification
DIA-NN 1.9 features a second-generation QuantUMS module, wherein quantities are optimised with machine learning and statistically-justified accuracy estimates are available for individual quantities.

Visualisation
This has been the most often requested feature since the conception of DIA-NN. Now supported via either Skyline integration or via a dedicated DIA-NN Viewer.

General performance
Better identification numbers and stricter control of false discoveries, along with extensive options to tailor the identification and quantification confidence control to a specific experiment.

Speed and code quality
DIA-NN has been overhauled to match the modern coding practices using C++20, with a focus on efficient memory use and better multithreading. DIA-NN 1.9 features code optimisations which yield roughly 1.3x-2x speed gains for library-free search. Large predicted libraries (tens of millions of precursors) are now often 10x+ quicker to generate.

Timeline. This is a Windows release of DIA-NN 1.9, Linux support is to follow shortly. Further, we have a number of features and performance improvements under active development and will likely release a series of updates implementing these in the near future. We will also be grateful for any feedback on DIA-NN 1.9 as well as feature requests, which we will do our best to implement.

Future roadmap

DIA-NN is under active development, towards (i) enabling new technologies as well as (ii) achieving better performance for existing workflows. In the latter case, we have the following planned or under development:

• While DIA-NN performs remarkably well in library-free setting already, there is a room for even better performance. Specifically, DIA-NN will in the future implement experiment-specific transfer learning, similar to the concept recently introduced in AlphaDIA.

• DIA-NN already implements a low RAM usage mode, which restricts the amount of system memory it needs for its search. Currently, the biggest factor in RAM usage by DIA-NN in the lib-free mode is the storage of the predicted library in memory, especially when using multiplexing. DIA-NN will in the future implement a different format for internal library storage, with fold-change lower memory requirements.

• The ultra-fast mode in DIA-NN is great for preliminary analyses (up to 5x faster), although it does sacrifice identification performance, as it implements a spectrum centric-like search strategy, which is inherently less sensitive. We have a different fast search mode in works, which will have minimum performance trade offs.

• We have a number of algorithms in works, which will fully explore the potential (in terms of both identification and quantification performance) of Slice-PASEF, midia- and Synchro-PASEF, Scanning SWATH and Orbitrap Astral. While the current algorithms perform remarkably well already, showing the potential of these technologies, we work on specific improvements that will further boost the performance.

• DIA-NN will in the future incorporate a module for detailed QC analysis of DIA runs.

• Together with our collaborators, we are developing some exciting new workflows combining different tools.

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This discussion was created from the release DIA-NN 1.9.

[Soulaimane-Aboulouard]

Hi Vadim,

Would it be possible to add functionality to manage the minimum number of peptides identified per protein ? directly on the interface or by a line command ? (For example, we would like the software to be able to keep proteins having at least 2 peptides, including 1 unique.)

In addition, in the "report.pg_matrix" results file, it would be interesting to add a column indicating the number of peptides identified per protein, in order to allow filtering based on this criterion.

Best,
Soulaimane

[Soulaimane-Aboulouard]

Hi Vadim,

As for adding this number to "pg_matrix", I'm fine with that.

As for the minimum number of peptides, I would like this to be based on the total number of peptides identified per protein group, without specificity to any particular run.

Please confirm if this is clear and feasible.

(Ideally, it would also be great to have a new option allowing the inclusion of an additional parameter when querying the runs on the Library/database with the DIANN software.. This parameter would be equivalent to the "Min razor + unique peptide" option in MaxQuant, where you can specify the minimum number of peptides per protein per run. For example, if we set the "Min razor + unique peptide" option to 2, a protein with 5 peptides identified in Run A is retained in the pg_matrix file with a quantification/intensity value, but the same protein with 1 peptide identified in Run B is not represented in the pg_matrix and gets a NaN value.)

Sincerely,
Soulaimane

[longchung90]

Hi Soulaimane,

It's so great that you ask the question on the number of psm(s). This is quite a crucial part in downstream analysis I think as DEqMS package requires requires this parameter to normalise.

Can I please ask if we can extract the psm table from DIANN?

Thank you

[vdemichev]

Hi Soulaimane,

Yes, we can add total number of peptides in pg_matrix. I will not promise this makes into 1.9.2, but this is in the todo list and will be there soon.

Best,
Vadim

[vdemichev]

Hi Long,

What do you mean by a PSM table? That is, anything in addition to the pr_matrix and the main report produced by DIA-NN?

Best,
Vadim

[longchung90]

Hi vdemichev,

Thank you for your reply. I think it is similar to the number of peptides used to identify the protein. I am using DEqMS to normalise the MS data and it does require the number of peptides. Hence, in this case with DIA-NN, it would be great to have the number of peptides reported as well in the future version.

[pieeye75]

This is great, but as we saw with the previous beta version, numerous vendors are flagging virus/trojan issues with the file. Our IT will not let us install (fair enough too). I get this report when I run the exe through VirusTotal;

Can you comment?

[vdemichev]

As I mentioned, we did a lot more in depth checks than the 2-second scan virustotal does. We obviously cannot control what third-party antivirus tools' heuristic 'AI' algorithms report. Their business model is to provide 'free scanning' options, show that they detect some threats and this way entice the user to install & pay. So low false positive rate is not exactly their priority. Now, I am not an expert in AI algorithms of antivirus software, but I can speculate that deep packaging of stuff in DIA-NN distribution can contribute to those AI algorithms getting suspicious.

Since this still creates a concern, today or tomorrow I will add a regular '.zip' folder, without installer / uninstaller (if you check individual files, the uninstaller produces most hits on virustotal), so anyone will be able to just extract the contents to a preferred location on their PC, and these will not be producing any virustotal hits.

[vdemichev]

We are using a tool called InstallForge to generate Setup files (which package all the DIA-NN files and allow one-click installation, as opposed to unpacking a .zip file). It's apparently known to create install files that get flagged by VirusTotal https://installforge.net/forums/viewtopic.php?p=1882. At the same time, it's a popular tool with a large number of users https://installforge.net/forums/viewforum.php?f=5. Given that no one ever reported a concrete confirmed identification of anything malicious there (only AI-based suspicions, apparently), I am inclined to think everything is fine with that tool. But for the peace of mind, as mentioned above, I will add DIA-NN just as a .zip folder.

[vdemichev]

Ok, I have added DIA-NN.1.9.binaries.zip to the release (i.e. just unpacked DIA-NN files without using InstallForge). Virustotal does not report anything for it.

[mziehm]

Many thanks this is great!

[pieeye75]

Thank you for this, much appreciated. I never suspected it had a virus, but our IT department was adamant. They have to assume the one hit is true. Clearly that would have been a "no win" for all concerned.

[animesh]

Awesome @vdemichev 💯 Any update on Linux version/rpm maybe?

[animesh]

Thanks @vdemichev for looking into this 🙏 will launch it from the directory and check again 🤞 BTW how far are we from Linux release 🤓

[vdemichev]

In the process :))

[vdemichev]

"will launch it from the directory " - will take a lot of time with this experiment. But if you could try to repeat the same approach on something which is quick to analyse (any empirical lib + two files), I would be grateful.

[animesh]

Tried two files via command line from withing the install exe dir, logs
mouse-report.log.txt

mouse-report.log.txt

and correlation is perfect 👍🏽

uploading those tsv files

Machine 1 https://drive.google.com/file/d/1oBuMR3VExK-9tb0jWq8q9BxHRUjoOLPM/view?usp=sharing ,
Machine 2 https://drive.google.com/file/d/1o_pzTbG1D-09CZIgcegGcxwupugKhQQl/view?usp=sharing respectively, are there still some differences with ?

[vdemichev]

Thank you!
I will need to think what caused the first inconsistency.
Also, we will probably switch to .parquet format for DIA-based spectral libraries (i.e. still support .tsv as input, but new libs will be generated as .parquet), and it will then eliminate any kind of issues like this in the future, as it is a precise binary format.

[kometkamerat]

Hi! Seems like the program refuses to run or install on Windows Server 2019. Version 1.8.1 runs without problems.

Error message from installer:

Unpacking the biaries and starting the executables just results in nothing. No program starts and no error message in event log.

K.

[vdemichev]

If you unpack the .binaries.zip, navigate to the respective folder in command prompt, and launch diann.exe from the command prompt, what does it print?

[kometkamerat]

Interresting, so running diann.exe or DIA-NN.exe from Powershell works perfectly, just not double-clicking from the File Explorer... Creating a desktop shortcut does not work either. But I'll direct my users to run it from powershell for now.

[vdemichev]

Then a .bat file launching DIA-NN.exe should also work I guess... Strange indeed

[xiaosiruwei]

There is no diann.cpp file in the source code of versions 1.8.1 and 1.9. Is it not open-source？

[vdemichev]

No, not open source

[vdemichev]

A quick update about the Linux version, I have managed to build DIA-NN under Linux with all the dependencies now, i.e. all the technical difficulties have been solved. So what's left is testing, plus I would like to incorporate some new functionality (have received several great suggestions) - and then will release 1.9.1 for both Windows and Linux.

[Aaron-Amunix]

Great, thanks a lot for all your hard work on DIA-NN. I tested DIA-NN 1.9.1 on my unique substrate library data and it works quite well. We currently are using DIA-NN 1.8 in a Linux environment and am excited to try 1.9.1 soon! Thanks again for making such an awesome tool and keeping on building. Hopefully I'll see you again at HUPO this year!

[vmataf]

Hi Vadim,
I run a dataset with DIANN 1.8.1 and DIANN 1.9.1 and I compared results.
Number of ProteinGroups identified were similar : 8261 for DIANN1.8.1 and 8134 for DIANN1.9.1
Number of Peptides identified were similar: 94767 for DIANN1.8.1 and 105210 for DIANN 1.9.1
however the coefficient of variation was different: medium CV 0.12 for DIANN1.8.1 while 0.35 for DIANN1.9.1 with bimodal shape
CV.pdf
This reflects on statistically significant proteins that were low for DIANN1.9.1.
I used the default setting for DIANN 1.9.1
-(direct DIA fasta digest for library free enabled)
-no peptidoform
-MBR , QUantUMS high precision and RT dependent normalization enambled.
Do you have any suggestion why DIANN1.9.1 quantitation has higher variability?

[vdemichev]

Hi,

Can you please share the (i) log of 1.9.1 and (ii) pg_matrix output of both 1.9.1 and 1.8.1?

(direct DIA fasta digest for library free enabled)

DIA-NN 1.9.1 prints a warning here that 'on the fly digest + analysis' must not be used. If that was the case, this likely explains weird CVs.

Best,
Vadim

[vmataf]

Hi, here the files you requested. Thanks a lot Vittoria

…

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________________________________ From: Vadim Demichev ***@***.***> Sent: Tuesday, October 1, 2024 11:38:39 AM To: vdemichev/DiaNN Cc: Vittoria Matafora; Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Hi, Can you please share the (i) log of 1.9.1 and (ii) pg_matrix output of both 1.9.1 and 1.8.1? (direct DIA fasta digest for library free enabled) DIA-NN 1.9.1 prints a warning here that 'on the fly digest + analysis' must not be used. If that was the case, this likely explains weird CVs. Best, Vadim — Reply to this email directly, view it on GitHub<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fvdemichev%2FDiaNN%2Fdiscussions%2F1034%23discussioncomment-10806986&e=c326a8c7&h=456313af&f=y&p=y>, or unsubscribe<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FBLX3TQIAZ3QQIV2D5PIPB7DZZJUR7AVCNFSM6AAAAABJBC5L66VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBQGY4TQNQ&e=c326a8c7&h=39178112&f=y&p=y>. You are receiving this because you commented.Message ID: ***@***.***>

-- This message has been checked by Libraesva ESG and is found to be clean. Report as bad/spam<https://esva.ifom.eu/action/4XHtDK0NrqzLpsl/report-as-bad> Blocklist sender<https://esva.ifom.eu/action/4XHtDK0NrqzLpsl/blocklist> DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks) Compiled on Jul 15 2024 15:40:36 Current date and time: Thu Sep 19 09:21:43 2024 CPU: GenuineIntel Intel(R) Xeon(R) w5-2465X SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 Logical CPU cores: 32 diann.exe --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_1.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_2.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_3.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL1.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL2.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL3.mzML --lib --threads 16 --verbose 1 --out C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.tsv --qvalue 0.01 --matrices --out-lib C:\DIA-NN\1.9.1\report-lib.parquet --gen-spec-lib --predictor --fasta C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\CP_Human_2020.fasta --fasta-search --min-fr-mz 100 --max-fr-mz 1800 --met-excision --min-pep-len 7 --max-pep-len 30 --min-pr-mz 300 --max-pr-mz 1800 --min-pr-charge 1 --max-pr-charge 4 --cut K*,R* --missed-cleavages 2 --unimod4 --var-mods 2 --reanalyse --relaxed-prot-inf --rt-profiling Thread number set to 16 Output will be filtered at 0.01 FDR Precursor/protein x samples expression level matrices will be saved along with the main report A spectral library will be generated Deep learning will be used to generate a new in silico spectral library from peptides provided DIA-NN will carry out FASTA digest for in silico lib generation Min fragment m/z set to 100 Max fragment m/z set to 1800 N-terminal methionine excision enabled Min peptide length set to 7 Max peptide length set to 30 Min precursor m/z set to 300 Max precursor m/z set to 1800 Min precursor charge set to 1 Max precursor charge set to 4 In silico digest will involve cuts at K*,R* Maximum number of missed cleavages set to 2 Cysteine carbamidomethylation enabled as a fixed modification Maximum number of variable modifications set to 2 A spectral library will be created from the DIA runs and used to reanalyse them; .quant files will only be saved to disk during the first step Heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers, GO/pathway and system-scale analyses The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs DIA-NN will optimise the mass accuracy automatically using the first run in the experiment. This is useful primarily for quick initial analyses, when it is not yet known which mass accuracy setting works best for a particular acquisition scheme. WARNING: it is strongly recommended to first generate an in silico-predicted library in a separate pipeline step and then use it to process the raw data, now without activating FASTA digest 6 files will be processed [0:00] Loading FASTA C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\CP_Human_2020.fasta [0:11] Processing FASTA [0:22] Assembling elution groups [0:34] 8237541 precursors generated [0:34] Gene names missing for some isoforms [0:34] Library contains 74674 proteins, and 20461 genes [0:39] [0:49] [10:17] [11:08] [11:16] [11:17] Saving the library to C:\DIA-NN\1.9.1\report-lib.predicted.speclib [11:37] Initialising library [11:51] Loading spectral library C:\DIA-NN\1.9.1\report-lib.predicted.speclib [12:05] Library annotated with sequence database(s): C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\CP_Human_2020.fasta [12:06] Spectral library loaded: 96655 protein isoforms, 170605 protein groups and 8237541 precursors in 2557925 elution groups. [12:06] Loading protein annotations from FASTA C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\CP_Human_2020.fasta [12:07] Annotating library proteins with information from the FASTA database [12:08] Gene names missing for some isoforms [12:08] Library contains 74674 proteins, and 20461 genes [12:14] [12:23] [21:50] [22:41] [22:49] [22:50] Saving the library to C:\DIA-NN\1.9.1\report-lib.predicted.speclib [23:10] Initialising library First pass: generating a spectral library from DIA data [23:22] File #1/6 [23:22] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_1.mzML [23:36] 4993915 library precursors are potentially detectable [23:40] Processing... [84:02] RT window set to 5.83553 [84:02] Peak width: 4.652 [84:02] Scan window radius set to 10 [84:03] Recommended MS1 mass accuracy setting: 2.77668 ppm [89:16] Optimised mass accuracy: 12.49 ppm [96:16] Removing low confidence identifications [96:17] Removing interfering precursors [96:21] Training neural networks: 155635 targets, 95068 decoys [96:27] Number of IDs at 0.01 FDR: 79571 [96:28] Calculating protein q-values [96:29] Number of genes identified at 1% FDR: 7831 (precursor-level), 6961 (protein-level) (inference performed using proteotypic peptides only) [96:29] Quantification [96:30] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_1.mzML.quant [96:30] File #2/6 [96:30] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_2.mzML [96:44] 4993915 library precursors are potentially detectable [96:47] Processing... [139:31] RT window set to 6.59068 [139:31] Recommended MS1 mass accuracy setting: 3.60766 ppm [146:02] Removing low confidence identifications [146:03] Removing interfering precursors [146:07] Training neural networks: 152544 targets, 93510 decoys [146:13] Number of IDs at 0.01 FDR: 80180 [146:14] Calculating protein q-values [146:15] Number of genes identified at 1% FDR: 7874 (precursor-level), 7107 (protein-level) (inference performed using proteotypic peptides only) [146:15] Quantification [146:16] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_2.mzML.quant [146:16] File #3/6 [146:16] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_3.mzML [146:29] 4993915 library precursors are potentially detectable [146:33] Processing... [188:19] RT window set to 6.8232 [188:20] Recommended MS1 mass accuracy setting: 3.05057 ppm [195:03] Removing low confidence identifications [195:04] Removing interfering precursors [195:09] Training neural networks: 150264 targets, 92456 decoys [195:15] Number of IDs at 0.01 FDR: 77722 [195:15] Calculating protein q-values [195:16] Number of genes identified at 1% FDR: 7730 (precursor-level), 7021 (protein-level) (inference performed using proteotypic peptides only) [195:16] Quantification [195:17] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_3.mzML.quant [195:17] File #4/6 [195:17] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL1.mzML [195:31] 4993915 library precursors are potentially detectable [195:35] Processing... [239:19] RT window set to 6.92527 [239:19] Recommended MS1 mass accuracy setting: 3.60953 ppm [246:26] Removing low confidence identifications [246:27] Removing interfering precursors [246:32] Training neural networks: 154108 targets, 93554 decoys [246:38] Number of IDs at 0.01 FDR: 79752 [246:38] Calculating protein q-values [246:39] Number of genes identified at 1% FDR: 7910 (precursor-level), 7136 (protein-level) (inference performed using proteotypic peptides only) [246:39] Quantification [246:41] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL1.mzML.quant [246:41] File #5/6 [246:41] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL2.mzML [246:54] 4993915 library precursors are potentially detectable [246:58] Processing... [249:11] RT window set to 7.41131 [249:11] Recommended MS1 mass accuracy setting: 4.1982 ppm [256:31] Removing low confidence identifications [256:32] Removing interfering precursors [256:36] Training neural networks: 154873 targets, 95344 decoys [256:42] Number of IDs at 0.01 FDR: 80588 [256:43] Calculating protein q-values [256:44] Number of genes identified at 1% FDR: 7871 (precursor-level), 7195 (protein-level) (inference performed using proteotypic peptides only) [256:44] Quantification [256:45] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL2.mzML.quant [256:45] File #6/6 [256:45] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL3.mzML [256:59] 4993915 library precursors are potentially detectable [257:02] Processing... [300:43] RT window set to 6.96936 [300:43] Recommended MS1 mass accuracy setting: 2.89874 ppm [307:40] Removing low confidence identifications [307:41] Removing interfering precursors [307:45] Training neural networks: 152132 targets, 93364 decoys [307:51] Number of IDs at 0.01 FDR: 78914 [307:52] Calculating protein q-values [307:53] Number of genes identified at 1% FDR: 7880 (precursor-level), 7158 (protein-level) (inference performed using proteotypic peptides only) [307:53] Quantification [307:54] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL3.mzML.quant [307:54] Cross-run analysis [307:54] Reading quantification information: 6 files [307:59] Quantifying peptides [308:19] Assembling protein groups [308:24] Quantifying proteins [308:25] Calculating q-values for protein and gene groups [308:26] Calculating global q-values for protein and gene groups [308:27] Protein groups with global q-value <= 0.01: 8155 [308:29] Compressed report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process [308:29] Writing report [308:43] Report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report-first-pass.tsv. [308:43] Saving precursor levels matrix [308:43] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report-first-pass.pr_matrix.tsv. [308:43] Manifest saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report-first-pass.manifest.txt [308:43] Stats report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report-first-pass.stats.tsv [308:43] Generating spectral library: [308:45] 106249 target and 1062 decoy precursors saved [308:45] Spectral library saved to C:\DIA-NN\1.9.1\report-lib.parquet [308:47] Loading spectral library C:\DIA-NN\1.9.1\report-lib.parquet [308:48] Spectral library loaded: 14463 protein isoforms, 9717 protein groups and 107311 precursors in 95912 elution groups. [308:48] Loading protein annotations from FASTA C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\CP_Human_2020.fasta [308:50] Annotating library proteins with information from the FASTA database [308:50] Gene names missing for some isoforms [308:50] Library contains 9431 proteins, and 9101 genes [308:50] Initialising library [308:50] Saving the library to C:\DIA-NN\1.9.1\report-lib.parquet.skyline.speclib Second pass: using the newly created spectral library to reanalyse the data [308:50] File #1/6 [308:50] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_1.mzML [308:59] 106249 library precursors are potentially detectable [308:59] Processing... [309:02] RT window set to 2.91985 [309:02] Recommended MS1 mass accuracy setting: 19.5851 ppm [309:08] Removing low confidence identifications [309:08] Removing interfering precursors [309:09] Training neural networks: 90180 targets, 48039 decoys [309:12] Number of IDs at 0.01 FDR: 87756 [309:12] Calculating protein q-values [309:12] Number of genes identified at 1% FDR: 8011 (precursor-level), 7406 (protein-level) (inference performed using proteotypic peptides only) [309:12] Quantification [309:12] File #2/6 [309:12] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_2.mzML [309:21] 106249 library precursors are potentially detectable [309:21] Processing... [309:24] RT window set to 2.90095 [309:24] Recommended MS1 mass accuracy setting: 20.1233 ppm [309:30] Removing low confidence identifications [309:30] Removing interfering precursors [309:31] Training neural networks: 89838 targets, 47928 decoys [309:34] Number of IDs at 0.01 FDR: 88139 [309:34] Calculating protein q-values [309:34] Number of genes identified at 1% FDR: 8036 (precursor-level), 7466 (protein-level) (inference performed using proteotypic peptides only) [309:34] Quantification [309:34] File #3/6 [309:34] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shBACE2_3.mzML [309:42] 106249 library precursors are potentially detectable [309:42] Processing... [309:45] RT window set to 2.87995 [309:45] Recommended MS1 mass accuracy setting: 15.001 ppm [309:51] Removing low confidence identifications [309:51] Removing interfering precursors [309:52] Training neural networks: 87776 targets, 46949 decoys [309:54] Number of IDs at 0.01 FDR: 84479 [309:55] Calculating protein q-values [309:55] Number of genes identified at 1% FDR: 7881 (precursor-level), 7310 (protein-level) (inference performed using proteotypic peptides only) [309:55] Quantification [309:55] File #4/6 [309:55] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL1.mzML [310:03] 106249 library precursors are potentially detectable [310:03] Processing... [310:06] RT window set to 2.93309 [310:07] Recommended MS1 mass accuracy setting: 16.9847 ppm [310:13] Removing low confidence identifications [310:13] Removing interfering precursors [310:13] Training neural networks: 89923 targets, 47917 decoys [310:16] Number of IDs at 0.01 FDR: 86978 [310:16] Calculating protein q-values [310:16] Number of genes identified at 1% FDR: 8008 (precursor-level), 7479 (protein-level) (inference performed using proteotypic peptides only) [310:16] Quantification [310:17] File #5/6 [310:17] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL2.mzML [310:25] 106249 library precursors are potentially detectable [310:25] Processing... [310:27] RT window set to 2.87418 [310:27] Recommended MS1 mass accuracy setting: 11.5094 ppm [310:33] Removing low confidence identifications [310:33] Removing interfering precursors [310:34] Training neural networks: 89643 targets, 47783 decoys [310:36] Number of IDs at 0.01 FDR: 87911 [310:36] Calculating protein q-values [310:36] Number of genes identified at 1% FDR: 8091 (precursor-level), 7473 (protein-level) (inference performed using proteotypic peptides only) [310:36] Quantification [310:37] File #6/6 [310:37] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\VM_shCTL3.mzML [310:45] 106249 library precursors are potentially detectable [310:45] Processing... [310:48] RT window set to 2.89349 [310:48] Recommended MS1 mass accuracy setting: 27.2802 ppm [310:54] Removing low confidence identifications [310:55] Removing interfering precursors [310:55] Training neural networks: 89429 targets, 47721 decoys [310:58] Number of IDs at 0.01 FDR: 86707 [310:58] Calculating protein q-values [310:58] Number of genes identified at 1% FDR: 8065 (precursor-level), 7465 (protein-level) (inference performed using proteotypic peptides only) [310:58] Quantification [310:58] Cross-run analysis [310:58] Reading quantification information: 6 files [311:00] Quantifying peptides [311:29] Quantification parameters: 0.471582, 0.00143234, 0.000502131, 0.0125609, 0.0136892, 0.0132414, 0.0791401, 0.111344, 0.145912, 0.0145982, 0.0447503, 0.0257992, 0.303294, 0.0513164, 0.0559372, 0.0117526 [311:38] Quantifying proteins [311:39] Calculating q-values for protein and gene groups [311:39] Calculating global q-values for protein and gene groups [311:39] Protein groups with global q-value <= 0.01: 8094 [311:40] Compressed report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process [311:40] Writing report [311:56] Report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.tsv. [311:56] Saving precursor levels matrix [311:57] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.pr_matrix.tsv. [311:57] Saving protein group levels matrix [311:57] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.pg_matrix.tsv. [311:57] Saving gene group levels matrix [311:57] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.gg_matrix.tsv. [311:57] Saving unique genes levels matrix [311:57] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.unique_genes_matrix.tsv. [311:57] Manifest saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.manifest.txt [311:57] Stats report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_mzmlonlypeakpiking\report.stats.tsv

[vdemichev]

Likely this:

WARNING: it is strongly recommended to first generate an in silico-predicted library in a separate pipeline step and then use it to process the raw data, now without activating FASTA digest

[vmataf]

Hi Vadim, I run the analysis as you suggested,

…

-first I run in silico predicted library from fasta, with fast digest for library free enabled (without loading .raw files) - then I run the analysis with .raw files, loading in silico predicted spectral library and with fast digest for library free not enabled. I attached log file. Again CV showed the same shape. Might you help me again? Thanks Vittoria

________________________________ From: Vittoria Matafora Sent: Tuesday, October 1, 2024 11:51 AM To: vdemichev/DiaNN; vdemichev/DiaNN Cc: Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Hi, here the files you requested. Thanks a lot Vittoria

-- Vittoria Matafora IFOM | Staff Scientist | Proteomics Unit Ph.: +39 02 57430 3302 | ***@***.******@***.***> www.ifom.eu<https://www.ifom.eu> | Linkedin<https://www.linkedin.com/school/ifom/> | Instagram<https://www.instagram.com/ifom_milan/?hl=it> | Facebook<https://www.facebook.com/ifom.eu> | Twitter<https://twitter.com/ifomresearch> | YouTube<https://www.youtube.com/channel/UC7J0QYrvQH87ZhWKJshQVgw> IFOM ETS - The AIRC Institute of Molecular Oncology | Via Adamello 16, 20139 Milan, Italy [cid:logoIfom_2d2161e0-3259-466c-8931-26f8c2a5979f.png]

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________________________________ From: Vadim Demichev ***@***.***> Sent: Tuesday, October 1, 2024 11:38:39 AM To: vdemichev/DiaNN Cc: Vittoria Matafora; Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Hi, Can you please share the (i) log of 1.9.1 and (ii) pg_matrix output of both 1.9.1 and 1.8.1? (direct DIA fasta digest for library free enabled) DIA-NN 1.9.1 prints a warning here that 'on the fly digest + analysis' must not be used. If that was the case, this likely explains weird CVs. Best, Vadim — Reply to this email directly, view it on GitHub<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fvdemichev%2FDiaNN%2Fdiscussions%2F1034%23discussioncomment-10806986&e=c326a8c7&h=456313af&f=y&p=y>, or unsubscribe<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FBLX3TQIAZ3QQIV2D5PIPB7DZZJUR7AVCNFSM6AAAAABJBC5L66VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBQGY4TQNQ&e=c326a8c7&h=39178112&f=y&p=y>. You are receiving this because you commented.Message ID: ***@***.***>

-- This message has been checked by Libraesva ESG and is found to be clean. Report as bad/spam<https://esva.ifom.eu/action/4XHtDK0NrqzLpsl/report-as-bad> Blocklist sender<https://esva.ifom.eu/action/4XHtDK0NrqzLpsl/blocklist> DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks) Compiled on Jul 15 2024 15:40:36 Current date and time: Wed Oct 2 10:57:36 2024 CPU: GenuineIntel Intel(R) Xeon(R) w5-2465X SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 Logical CPU cores: 32 diann.exe --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_1.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_2.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_3.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL1.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL2.mzML --f C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL3.mzML --lib C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\library.predicted.speclib --threads 16 --verbose 1 --out C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.tsv --qvalue 0.01 --matrices --out-lib C:\DIA-NN\1.9.1\report-lib.parquet --gen-spec-lib --fasta C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Spectronaut\CP_Human_2020.fasta --met-excision --cut K*,R* --missed-cleavages 1 --unimod4 --var-mods 1 --reanalyse --relaxed-prot-inf --rt-profiling Thread number set to 16 Output will be filtered at 0.01 FDR Precursor/protein x samples expression level matrices will be saved along with the main report A spectral library will be generated N-terminal methionine excision enabled In silico digest will involve cuts at K*,R* Maximum number of missed cleavages set to 1 Cysteine carbamidomethylation enabled as a fixed modification Maximum number of variable modifications set to 1 A spectral library will be created from the DIA runs and used to reanalyse them; .quant files will only be saved to disk during the first step Heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers, GO/pathway and system-scale analyses The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs DIA-NN will optimise the mass accuracy automatically using the first run in the experiment. This is useful primarily for quick initial analyses, when it is not yet known which mass accuracy setting works best for a particular acquisition scheme. 6 files will be processed [0:00] Loading spectral library C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\library.predicted.speclib [0:08] Library annotated with sequence database(s): C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Spectronaut\CP_Human_2020.fasta [0:09] Spectral library loaded: 96610 protein isoforms, 163296 protein groups and 5139869 precursors in 1600597 elution groups. [0:09] Loading protein annotations from FASTA C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Spectronaut\CP_Human_2020.fasta [0:11] Annotating library proteins with information from the FASTA database [0:11] Gene names missing for some isoforms [0:11] Library contains 74630 proteins, and 20457 genes [0:12] Initialising library First pass: generating a spectral library from DIA data [0:20] File #1/6 [0:20] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_1.mzML [0:31] 3102888 library precursors are potentially detectable [0:33] Processing... [30:56] RT window set to 6.3501 [30:56] Peak width: 4.72 [30:56] Scan window radius set to 10 [30:56] Recommended MS1 mass accuracy setting: 2.8958 ppm [35:06] Optimised mass accuracy: 11.8336 ppm [40:01] Removing low confidence identifications [40:02] Removing interfering precursors [40:05] Training neural networks: 146742 targets, 88371 decoys [40:10] Number of IDs at 0.01 FDR: 75328 [40:11] Calculating protein q-values [40:11] Number of genes identified at 1% FDR: 7766 (precursor-level), 6980 (protein-level) (inference performed using proteotypic peptides only) [40:11] Quantification [40:12] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_1.mzML.quant [40:12] File #2/6 [40:12] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_2.mzML [40:24] 3102888 library precursors are potentially detectable [40:26] Processing... [66:24] RT window set to 5.74768 [66:24] Recommended MS1 mass accuracy setting: 3.52225 ppm [70:10] Removing low confidence identifications [70:11] Removing interfering precursors [70:14] Training neural networks: 143422 targets, 86354 decoys [70:19] Number of IDs at 0.01 FDR: 74446 [70:20] Calculating protein q-values [70:20] Number of genes identified at 1% FDR: 7797 (precursor-level), 7084 (protein-level) (inference performed using proteotypic peptides only) [70:20] Quantification [70:21] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_2.mzML.quant [70:21] File #3/6 [70:21] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_3.mzML [70:33] 3102888 library precursors are potentially detectable [70:35] Processing... [81:14] RT window set to 7.70136 [81:14] Recommended MS1 mass accuracy setting: 3.63744 ppm [85:55] Removing low confidence identifications [85:56] Removing interfering precursors [85:59] Training neural networks: 144097 targets, 86844 decoys [86:04] Number of IDs at 0.01 FDR: 72964 [86:04] Calculating protein q-values [86:05] Number of genes identified at 1% FDR: 7673 (precursor-level), 7045 (protein-level) (inference performed using proteotypic peptides only) [86:05] Quantification [86:06] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_3.mzML.quant [86:06] File #4/6 [86:06] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL1.mzML [86:18] 3102888 library precursors are potentially detectable [86:20] Processing... [117:42] RT window set to 7.27329 [117:43] Recommended MS1 mass accuracy setting: 3.73591 ppm [122:25] Removing low confidence identifications [122:26] Removing interfering precursors [122:29] Training neural networks: 144714 targets, 87145 decoys [122:34] Number of IDs at 0.01 FDR: 74422 [122:35] Calculating protein q-values [122:35] Number of genes identified at 1% FDR: 7838 (precursor-level), 7156 (protein-level) (inference performed using proteotypic peptides only) [122:35] Quantification [122:36] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL1.mzML.quant [122:36] File #5/6 [122:36] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL2.mzML [122:48] 3102888 library precursors are potentially detectable [122:50] Processing... [124:20] RT window set to 6.13904 [124:20] Recommended MS1 mass accuracy setting: 4.85066 ppm [128:20] Removing low confidence identifications [128:20] Removing interfering precursors [128:23] Training neural networks: 144616 targets, 87497 decoys [128:29] Number of IDs at 0.01 FDR: 75702 [128:29] Calculating protein q-values [128:30] Number of genes identified at 1% FDR: 7867 (precursor-level), 7167 (protein-level) (inference performed using proteotypic peptides only) [128:30] Quantification [128:31] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL2.mzML.quant [128:31] File #6/6 [128:31] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL3.mzML [128:43] 3102888 library precursors are potentially detectable [128:45] Processing... [154:53] RT window set to 7.04954 [154:53] Recommended MS1 mass accuracy setting: 3.05858 ppm [159:25] Removing low confidence identifications [159:26] Removing interfering precursors [159:29] Training neural networks: 144597 targets, 86837 decoys [159:34] Number of IDs at 0.01 FDR: 73303 [159:34] Calculating protein q-values [159:35] Number of genes identified at 1% FDR: 7801 (precursor-level), 7103 (protein-level) (inference performed using proteotypic peptides only) [159:35] Quantification [159:36] Quantification information saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL3.mzML.quant [159:36] Cross-run analysis [159:36] Reading quantification information: 6 files [159:40] Quantifying peptides [159:59] Assembling protein groups [160:03] Quantifying proteins [160:04] Calculating q-values for protein and gene groups [160:05] Calculating global q-values for protein and gene groups [160:05] Protein groups with global q-value <= 0.01: 8132 [160:06] Compressed report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process [160:06] Writing report [160:20] Report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report-first-pass.tsv. [160:20] Saving precursor levels matrix [160:20] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report-first-pass.pr_matrix.tsv. [160:20] Manifest saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report-first-pass.manifest.txt [160:20] Stats report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report-first-pass.stats.tsv [160:20] Generating spectral library: [160:22] 98593 target and 985 decoy precursors saved [160:22] Spectral library saved to C:\DIA-NN\1.9.1\report-lib.parquet [160:23] Loading spectral library C:\DIA-NN\1.9.1\report-lib.parquet [160:24] Spectral library loaded: 14390 protein isoforms, 9562 protein groups and 99578 precursors in 89609 elution groups. [160:24] Loading protein annotations from FASTA C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Spectronaut\CP_Human_2020.fasta [160:25] Annotating library proteins with information from the FASTA database [160:25] Gene names missing for some isoforms [160:25] Library contains 9326 proteins, and 9029 genes [160:25] Initialising library [160:26] Saving the library to C:\DIA-NN\1.9.1\report-lib.parquet.skyline.speclib Second pass: using the newly created spectral library to reanalyse the data [160:26] File #1/6 [160:26] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_1.mzML [160:34] 98593 library precursors are potentially detectable [160:34] Processing... [160:37] RT window set to 2.91788 [160:37] Recommended MS1 mass accuracy setting: 16.0703 ppm [160:43] Removing low confidence identifications [160:43] Removing interfering precursors [160:44] Training neural networks: 84455 targets, 44819 decoys [160:46] Number of IDs at 0.01 FDR: 81606 [160:46] Calculating protein q-values [160:46] Number of genes identified at 1% FDR: 7972 (precursor-level), 7379 (protein-level) (inference performed using proteotypic peptides only) [160:46] Quantification [160:47] File #2/6 [160:47] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_2.mzML [160:55] 98593 library precursors are potentially detectable [160:55] Processing... [160:58] RT window set to 2.86069 [160:58] Recommended MS1 mass accuracy setting: 12.3658 ppm [161:04] Removing low confidence identifications [161:04] Removing interfering precursors [161:04] Training neural networks: 84270 targets, 44730 decoys [161:07] Number of IDs at 0.01 FDR: 81917 [161:07] Calculating protein q-values [161:07] Number of genes identified at 1% FDR: 7989 (precursor-level), 7425 (protein-level) (inference performed using proteotypic peptides only) [161:07] Quantification [161:07] File #3/6 [161:07] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shBACE2_3.mzML [161:15] 98593 library precursors are potentially detectable [161:15] Processing... [161:18] RT window set to 2.83845 [161:18] Recommended MS1 mass accuracy setting: 5.11251 ppm [161:24] Removing low confidence identifications [161:24] Removing interfering precursors [161:25] Training neural networks: 82535 targets, 43893 decoys [161:27] Number of IDs at 0.01 FDR: 78946 [161:27] Calculating protein q-values [161:27] Number of genes identified at 1% FDR: 7864 (precursor-level), 7334 (protein-level) (inference performed using proteotypic peptides only) [161:27] Quantification [161:28] File #4/6 [161:28] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL1.mzML [161:36] 98593 library precursors are potentially detectable [161:36] Processing... [161:39] RT window set to 2.90401 [161:39] Recommended MS1 mass accuracy setting: 12.9078 ppm [161:45] Removing low confidence identifications [161:45] Removing interfering precursors [161:46] Training neural networks: 84195 targets, 44613 decoys [161:48] Number of IDs at 0.01 FDR: 81138 [161:48] Calculating protein q-values [161:48] Number of genes identified at 1% FDR: 7958 (precursor-level), 7432 (protein-level) (inference performed using proteotypic peptides only) [161:48] Quantification [161:49] File #5/6 [161:49] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL2.mzML [161:57] 98593 library precursors are potentially detectable [161:57] Processing... [162:00] RT window set to 2.86317 [162:00] Recommended MS1 mass accuracy setting: 9.90091 ppm [162:05] Removing low confidence identifications [162:05] Removing interfering precursors [162:06] Training neural networks: 84121 targets, 44689 decoys [162:08] Number of IDs at 0.01 FDR: 82010 [162:08] Calculating protein q-values [162:08] Number of genes identified at 1% FDR: 8019 (precursor-level), 7427 (protein-level) (inference performed using proteotypic peptides only) [162:08] Quantification [162:09] File #6/6 [162:09] Loading run C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\VM_shCTL3.mzML [162:17] 98593 library precursors are potentially detectable [162:17] Processing... [162:22] RT window set to 2.85827 [162:22] Recommended MS1 mass accuracy setting: 10.6505 ppm [162:28] Removing low confidence identifications [162:28] Removing interfering precursors [162:28] Training neural networks: 83727 targets, 44418 decoys [162:31] Number of IDs at 0.01 FDR: 80559 [162:31] Calculating protein q-values [162:31] Number of genes identified at 1% FDR: 8002 (precursor-level), 7416 (protein-level) (inference performed using proteotypic peptides only) [162:31] Quantification [162:31] Cross-run analysis [162:31] Reading quantification information: 6 files [162:32] Quantifying peptides [163:00] Quantification parameters: 0.471744, 0.00148669, 0.000522689, 0.0138588, 0.0141014, 0.013498, 0.114488, 0.131805, 0.156202, 0.0145654, 0.0444517, 0.024958, 0.28076, 0.0497001, 0.0512904, 0.01376 [163:09] Quantifying proteins [163:09] Calculating q-values for protein and gene groups [163:09] Calculating global q-values for protein and gene groups [163:09] Protein groups with global q-value <= 0.01: 8125 [163:11] Compressed report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process [163:11] Writing report [163:26] Report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.tsv. [163:26] Saving precursor levels matrix [163:26] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.pr_matrix.tsv. [163:26] Saving protein group levels matrix [163:26] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.pg_matrix.tsv. [163:26] Saving gene group levels matrix [163:26] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.gg_matrix.tsv. [163:26] Saving unique genes levels matrix [163:26] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.unique_genes_matrix.tsv. [163:26] Manifest saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.manifest.txt [163:26] Stats report saved to C:\Users\worms\Desktop\PersonalFolder\Vittoria\shBACE2_new\Diann1_9_vadim\report.stats.tsv

[vmataf]

Hi, I have just realized that DIANN does not support FIAMS with mulyiple CVs

…

-FAIMS with multiple CVs is supported after splitting the runs, All my samples have multiple CVs, I did not split the runs ...could this affect the coefficient of variation? Thanks

________________________________ From: Vittoria Matafora Sent: Wednesday, October 2, 2024 3:38:12 PM To: vdemichev/DiaNN; vdemichev/DiaNN Cc: Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Hi Vadim, I run the analysis as you suggested,

-first I run in silico predicted library from fasta, with fast digest for library free enabled (without loading .raw files) - then I run the analysis with .raw files, loading in silico predicted spectral library and with fast digest for library free not enabled. I attached log file. Again CV showed the same shape. Might you help me again? Thanks Vittoria

________________________________ From: Vittoria Matafora Sent: Tuesday, October 1, 2024 11:51 AM To: vdemichev/DiaNN; vdemichev/DiaNN Cc: Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Hi, here the files you requested. Thanks a lot Vittoria

-- Vittoria Matafora IFOM | Staff Scientist | Proteomics Unit Ph.: +39 02 57430 3302 | ***@***.******@***.***> www.ifom.eu<https://www.ifom.eu> | Linkedin<https://www.linkedin.com/school/ifom/> | Instagram<https://www.instagram.com/ifom_milan/?hl=it> | Facebook<https://www.facebook.com/ifom.eu> | Twitter<https://twitter.com/ifomresearch> | YouTube<https://www.youtube.com/channel/UC7J0QYrvQH87ZhWKJshQVgw> IFOM ETS - The AIRC Institute of Molecular Oncology | Via Adamello 16, 20139 Milan, Italy [cid:logoIfom_2d2161e0-3259-466c-8931-26f8c2a5979f.png]

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________________________________ From: Vadim Demichev ***@***.***> Sent: Tuesday, October 1, 2024 11:38:39 AM To: vdemichev/DiaNN Cc: Vittoria Matafora; Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Hi, Can you please share the (i) log of 1.9.1 and (ii) pg_matrix output of both 1.9.1 and 1.8.1? (direct DIA fasta digest for library free enabled) DIA-NN 1.9.1 prints a warning here that 'on the fly digest + analysis' must not be used. If that was the case, this likely explains weird CVs. Best, Vadim — Reply to this email directly, view it on GitHub<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fvdemichev%2FDiaNN%2Fdiscussions%2F1034%23discussioncomment-10806986&e=c326a8c7&h=456313af&f=y&p=y>, or unsubscribe<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FBLX3TQIAZ3QQIV2D5PIPB7DZZJUR7AVCNFSM6AAAAABJBC5L66VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBQGY4TQNQ&e=c326a8c7&h=39178112&f=y&p=y>. You are receiving this because you commented.Message ID: ***@***.***>

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[vdemichev]

Yes, this would explain then I guess...

[vmataf]

Hi Vadim, I splitted the FAIMS CV for all the .raw files , I run all the CV as separate experiments in DIANN 1.9.1, I increased the number of proteingroups identified, however I got the same coefficient of variation CV shape. I do not understand why, in version 1.8.1 I do not have same problem.

…

-- Vittoria Matafora IFOM | Staff Scientist | Proteomics Unit Ph.: +39 02 57430 3302 | ***@***.******@***.***> www.ifom.eu<https://www.ifom.eu> | Linkedin<https://www.linkedin.com/school/ifom/> | Instagram<https://www.instagram.com/ifom_milan/?hl=it> | Facebook<https://www.facebook.com/ifom.eu> | Twitter<https://twitter.com/ifomresearch> | YouTube<https://www.youtube.com/channel/UC7J0QYrvQH87ZhWKJshQVgw> IFOM ETS - The AIRC Institute of Molecular Oncology | Via Adamello 16, 20139 Milan, Italy [cid:logoIfom_2d2161e0-3259-466c-8931-26f8c2a5979f.png]

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________________________________ From: Vadim Demichev ***@***.***> Sent: Wednesday, October 2, 2024 5:26:49 PM To: vdemichev/DiaNN Cc: Vittoria Matafora; Comment Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) Yes, this would explain then I guess... — Reply to this email directly, view it on GitHub<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fvdemichev%2FDiaNN%2Fdiscussions%2F1034%23discussioncomment-10822327&e=c326a8c7&h=a9d4aa84&f=y&p=y>, or unsubscribe<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FBLX3TQKCXJR7VHP3DRDC4F3ZZQGDTAVCNFSM6AAAAABJBC5L66VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBSGIZTENY&e=c326a8c7&h=6d80bf48&f=y&p=y>. You are receiving this because you commented.Message ID: ***@***.***>

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[vdemichev]

I run all the CV as separate experiments in DIANN 1.9.1

So are coefficients of variation fine within each such experiment?

[vmataf]

The coefficients of variation within each such experiment look always wired, separate and/or together look the same

…

-- Vittoria Matafora IFOM | Staff Scientist | Proteomics Unit Ph.: +39 02 57430 3302 | ***@***.******@***.***> www.ifom.eu<https://www.ifom.eu> | Linkedin<https://www.linkedin.com/school/ifom/> | Instagram<https://www.instagram.com/ifom_milan/?hl=it> | Facebook<https://www.facebook.com/ifom.eu> | Twitter<https://twitter.com/ifomresearch> | YouTube<https://www.youtube.com/channel/UC7J0QYrvQH87ZhWKJshQVgw> IFOM ETS - The AIRC Institute of Molecular Oncology | Via Adamello 16, 20139 Milan, Italy [cid:logoIfom_2d2161e0-3259-466c-8931-26f8c2a5979f.png]

________________________________ Confidentiality notice. This message, together with its annexes, contains information to be deemed strictly confidential and is destined only to the addressee(s) identified above who only may use, copy and, under his/their responsibility, further disseminate it. if anyone received this message by mistake or reads it without entitlement is forewarned that keeping, copying, disseminating or distributing this message to persons other than the addressee(s) is strictly forbidden and is asked to transmit it immediately to the sender and to erase the original message received. Thank you Save a tree - Do not print this email unless absolutely necessary From: Vadim Demichev ***@***.***> Reply-To: vdemichev/DiaNN ***@***.***> Date: Thursday, October 3, 2024 at 12:47 PM To: vdemichev/DiaNN ***@***.***> Cc: Vittoria Matafora ***@***.***>, Comment ***@***.***> Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) I run all the CV as separate experiments in DIANN 1.9.1 So are coefficients of variation fine within each such experiment? — Reply to this email directly, view it on GitHub<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fvdemichev%2FDiaNN%2Fdiscussions%2F1034%23discussioncomment-10830681&e=c326a8c7&h=8509831a&f=y&p=y>, or unsubscribe<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FBLX3TQLQH4VWRPGAV34SSUTZZUOFPAVCNFSM6AAAAABJBC5L66VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBTGA3DQMI&e=c326a8c7&h=77db6e78&f=y&p=y>. You are receiving this because you commented.Message ID: ***@***.***>

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[vmataf]

Hi Vadim, I fixed the problem of coefficient of variation, now it is fine. I run all the CV as separate experiments in DIANN 1.9.1. Next step is to combine the CV channels. You suggested to • combine the reports using R, select the 'best' channel for each precursor ion, discard all other channels for this precursor On which criteria do you select the 'best' channel for each precursor ion? Moreover • perform protein quantification using MaxLFQ with the diann R package or the "iq" R package (the latter is currently much faster) How to do that? Thanks a lot Vittoria

…

-- Vittoria Matafora IFOM | Staff Scientist | Proteomics Unit Ph.: +39 02 57430 3302 | ***@***.******@***.***> www.ifom.eu<https://www.ifom.eu> | Linkedin<https://www.linkedin.com/school/ifom/> | Instagram<https://www.instagram.com/ifom_milan/?hl=it> | Facebook<https://www.facebook.com/ifom.eu> | Twitter<https://twitter.com/ifomresearch> | YouTube<https://www.youtube.com/channel/UC7J0QYrvQH87ZhWKJshQVgw> IFOM ETS - The AIRC Institute of Molecular Oncology | Via Adamello 16, 20139 Milan, Italy [cid:logoIfom_2d2161e0-3259-466c-8931-26f8c2a5979f.png]

________________________________ Confidentiality notice. This message, together with its annexes, contains information to be deemed strictly confidential and is destined only to the addressee(s) identified above who only may use, copy and, under his/their responsibility, further disseminate it. if anyone received this message by mistake or reads it without entitlement is forewarned that keeping, copying, disseminating or distributing this message to persons other than the addressee(s) is strictly forbidden and is asked to transmit it immediately to the sender and to erase the original message received. Thank you Save a tree - Do not print this email unless absolutely necessary From: Vadim Demichev ***@***.***> Reply-To: vdemichev/DiaNN ***@***.***> Date: Thursday, October 3, 2024 at 12:47 PM To: vdemichev/DiaNN ***@***.***> Cc: Vittoria Matafora ***@***.***>, Comment ***@***.***> Subject: Re: [vdemichev/DiaNN] DIA-NN 1.9 (Discussion #1034) I run all the CV as separate experiments in DIANN 1.9.1 So are coefficients of variation fine within each such experiment? — Reply to this email directly, view it on GitHub<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fvdemichev%2FDiaNN%2Fdiscussions%2F1034%23discussioncomment-10830681&e=c326a8c7&h=8509831a&f=y&p=y>, or unsubscribe<https://urlsand.esvalabs.com/?u=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FBLX3TQLQH4VWRPGAV34SSUTZZUOFPAVCNFSM6AAAAABJBC5L66VHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBTGA3DQMI&e=c326a8c7&h=77db6e78&f=y&p=y>. You are receiving this because you commented.Message ID: ***@***.***>

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[vdemichev]

Hi Vittoria,

On which criteria do you select the 'best' channel for each precursor ion?

E.g. the lowest global q-value for the precursor across the experiments with different CVs. Or highest data completeness.

How to do that?

Please see the respective docs for the iq R package, it's fairly simple but requires a bit of coding in R (like ~20-30 lines maybe).

Best,
Vadim

[dorvashmr]

Hi Vadim,

I was wondering if you could help me know what are the changes made from the 1.9.0 to 1.9.1 versions? Particularly if there are any FDR- or quantity calculation-related changes.

Cheers,
Reza

[dorvashmr]

Hi again Vadim,

My current observation with these two versions is:
The same dataset (mixed-species) was searched with the same settings using both versions (lib-free, FDR=1%, MBR+, Double-pass, QuantUMS high acc, Global normaization).

With 1.9.0 -> protein groups = ~5000 ; experimentally calculated precursor FDR = ~0.5-0.7% ; experimentally calculated protein FDR = ~2.5-3.5%
With 1.9.1 -> protein groups = ~11000 ; experimentally calculated precursor FDR = ~2-3% ; experimentally calculated protein FDR = ~20%

I was wondering if you could help me find what is causing this observation.

Cheers,
Reza

[vdemichev]

Hi Reza,

I assume the in silico library prediction from FASTA and raw data analysis is combined in one step?

Best,
Vadim

[dorvashmr]

Hi Vadim,

With both versions, the FASTA was loaded with the raw (wiff.dia) files and the "FASTA digest ..." and "Deep learnin ..." options were selected. The search in both versions was double-pass, though.

Cheers,
Reza

[vdemichev]

Hi Reza,

In this case 1.9.1 behaves incorrectly (please note that it also prints a warning that settings are incorrect). 1.9.2 should work correctly (but still print a warning, so please create an in silico library in a separate pipeline step).

Best,
Vadim

[karlssoc]

DIA-NN has 130(!) available command line parameters. Maybe a collection of configuration files for different types of analyses could help users get started. For example:

• TimsTOF libfree peptide
• Astral phosphopeptides
• Sciex Scanning swath
• ...

[vdemichev]

Hi,

It's 130, but for most experiments you need to use none :)
All the common settings are configured through the GUI, and this takes just a minute or two, we describe the steps to configure here: https://github.com/vdemichev/DiaNN?tab=readme-ov-file#changing-default-settings.

Best,
Vadim