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vdemichev / DiaNN

DIA-NN - a universal automated software suite for DIA proteomics data analysis.
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No PTM info produced with --monitor-mod #544

Closed danielgeiszler closed 2 years ago

danielgeiszler commented 2 years ago

Hi Vadim,

I'm running DIA-NN in FragPipe. Library was DDA phospho. I see that it says PTM scoring is enabled by default for phospho, but I was hoping to look at the PTM.Q.Value column regardless. After running, that column doesn't exist in the output.

Is this expected behavior? Is it filtered to 0.01 peptidoform FDR even if that column is absent?

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks) Compiled on Apr 14 2022 15:31:19 Current date and time: Thu Oct 27 15:05:15 2022 CPU: GenuineIntel 11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 Logical CPU cores: 16 C:\Users\Nozlu\Downloads\FragPipe-18.0\fragpipe\tools\diann\1.8.1\win\DiaNN.exe --lib library.tsv --threads 15 --verbose 1 --out diann-output.tsv --qvalue 0.01 --matrix-spec-q 0.01 --matrices --no-prot-inf --smart-profiling --no-quant-files --peak-center --no-ifs-removal --monitor-mod --cfg C:\Users\Nozlu\Desktop\DIAPhosData_raw_5\filelist_diann.txt

Thread number set to 15 Output will be filtered at 0.01 FDR Protein x sample matrices will be also filtered using run-specific protein q-value Precursor/protein x samples expression level matrices will be saved along with the main report Protein inference will not be performed When generating a spectral library, in silico predicted spectra will be retained if deemed more reliable than experimental ones .quant files will not be saved to the disk Fixed-width center of each elution peak will be used for quantification Interference removal from fragment elution curves disabled 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.

3 files will be processed [0:00] Loading spectral library library.tsv [0:01] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete) [0:01] Spectral library loaded: 5999 protein isoforms, 5999 protein groups and 53544 precursors in 44655 elution groups. [0:01] Initialising library [0:01] Saving the library to library.tsv.speclib

[0:01] File #1/3 [0:01] Loading run C:\Users\Nozlu\Desktop\DIAPhosData_raw\20220706_004_S399820_pool3_6_rep1_MCF7_Inter.raw [0:37] 49741 library precursors are potentially detectable [0:37] Processing... [0:38] RT window set to 1.82996 [0:38] Peak width: 4.016 [0:38] Scan window radius set to 8 [0:38] Recommended MS1 mass accuracy setting: 5.87349 ppm [0:39] Optimised mass accuracy: 18.7925 ppm [0:41] Removing low confidence identifications [0:41] Removing interfering precursors [0:42] Training neural networks: 33653 targets, 17344 decoys [0:44] Number of IDs at 0.01 FDR: 27662 [0:44] Calculating protein q-values [0:44] Number of protein isoforms identified at 1% FDR: 4680 (precursor-level), 4567 (protein-level) (inference performed using proteotypic peptides only) [0:44] Quantification

[0:44] File #2/3 [0:44] Loading run C:\Users\Nozlu\Desktop\DIAPhosData_raw\20220706_003_S399818_pool2_5_rep1_MCF7_Inter.raw [1:20] 49741 library precursors are potentially detectable [1:20] Processing... [1:20] RT window set to 1.78438 [1:20] Recommended MS1 mass accuracy setting: 5.29998 ppm [1:23] Removing low confidence identifications [1:23] Removing interfering precursors [1:23] Training neural networks: 30357 targets, 13339 decoys [1:25] Number of IDs at 0.01 FDR: 24713 [1:25] Calculating protein q-values [1:25] Number of protein isoforms identified at 1% FDR: 4723 (precursor-level), 4666 (protein-level) (inference performed using proteotypic peptides only) [1:25] Quantification

[1:25] File #3/3 [1:25] Loading run C:\Users\Nozlu\Desktop\DIAPhosData_raw\20220706_002_S399816_pool1_4_rep1_MCF7_Inter.raw [2:01] 49741 library precursors are potentially detectable [2:01] Processing... [2:02] RT window set to 1.88713 [2:02] Recommended MS1 mass accuracy setting: 5.70374 ppm [2:04] Removing low confidence identifications [2:04] Removing interfering precursors [2:05] Training neural networks: 20988 targets, 7902 decoys [2:06] Number of IDs at 0.01 FDR: 15549 [2:06] Calculating protein q-values [2:06] Number of protein isoforms identified at 1% FDR: 3704 (precursor-level), 3611 (protein-level) (inference performed using proteotypic peptides only) [2:06] Quantification

[2:06] Cross-run analysis [2:06] Reading quantification information: 3 files [2:06] Quantifying peptides [2:07] Quantifying proteins [2:07] Calculating q-values for protein and gene groups [2:07] Calculating global q-values for protein and gene groups [2:07] Writing report [2:10] Report saved to diann-output.tsv. [2:10] Saving precursor levels matrix [2:10] Precursor levels matrix (1% precursor and protein group FDR) saved to diann-output.pr_matrix.tsv. [2:10] Saving protein group levels matrix [2:10] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to diann-output.pg_matrix.tsv. [2:10] Saving gene group levels matrix [2:10] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to diann-output.gg_matrix.tsv. [2:10] Saving unique genes levels matrix [2:10] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to diann-output.unique_genes_matrix.tsv. [2:10] Stats report saved to diann-output.stats.tsv

Finished

danielgeiszler commented 2 years ago

Parameter was input incorrectly.