DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Jul 15 2024 15:40:36
Current date and time: Mon Aug  5 13:43:36 2024
CPU: GenuineIntel 13th Gen Intel(R) Core(TM) i7-13700F
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 
Logical CPU cores: 24
diann.exe --f C:\Users\akiyama\Desktop\Temporary Raw file\240612blood vessel003206.raw  --f C:\Users\akiyama\Desktop\Temporary Raw file\240612periosteum003206.raw  --lib  --threads 16 --verbose 1 --out C:\DIA-NN\1.9.1\20260805.tsv --qvalue 0.01 --matrices --predictor --reannotate --fasta C:\Users\akiyama\Desktop\uniprotkb_reviewed_true_AND_taxonomy_id_2024_08_05.fasta --fasta-search --min-fr-mz 200 --max-fr-mz 1800 --met-excision --min-pep-len 7 --max-pep-len 30 --min-pr-mz 398 --max-pr-mz 802 --min-pr-charge 1 --max-pr-charge 4 --cut K*,R* --missed-cleavages 2 --unimod4 --var-mods 5 --double-search --reanalyse --relaxed-prot-inf --pg-level 1 

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
Deep learning will be used to generate a new in silico spectral library from peptides provided
Library precursors will be reannotated using the FASTA database
DIA-NN will carry out FASTA digest for in silico lib generation
Min fragment m/z set to 200
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 398
Max precursor m/z set to 802
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 5
Neural networks will be used for peak selection
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
Implicit protein grouping: protein names; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
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
WARNING: double-pass neural network mode is experimental and may result in lower IDs

2 files will be processed
[0:00] Loading FASTA C:\Users\akiyama\Desktop\uniprotkb_reviewed_true_AND_taxonomy_id_2024_08_05.fasta
[0:00] Processing FASTA
[0:01] Assembling elution groups
[0:02] 719387 precursors generated
[0:02] Gene names missing for some isoforms
[0:02] Library contains 6045 proteins, and 5902 genes
[0:02] [0:02] [1:34] [1:52] [1:53] [1:53] Saving the library to report-lib.predicted.speclib
[1:54] Initialising library
[1:55] Loading spectral library report-lib.predicted.speclib
[1:56] Library annotated with sequence database(s): C:\Users\akiyama\Desktop\uniprotkb_reviewed_true_AND_taxonomy_id_2024_08_05.fasta
[1:56] Spectral library loaded: 6045 protein isoforms, 6947 protein groups and 719387 precursors in 464613 elution groups.
[1:56] Loading protein annotations from FASTA C:\Users\akiyama\Desktop\uniprotkb_reviewed_true_AND_taxonomy_id_2024_08_05.fasta
[1:56] Annotating library proteins with information from the FASTA database
[1:56] Gene names missing for some isoforms
[1:56] Library contains 6045 proteins, and 5902 genes
[1:56] [1:57] [3:33] [3:51] [3:51] [3:51] Saving the library to report-lib.predicted.speclib
[3:53] Initialising library

First pass: generating a spectral library from DIA data

[3:53] File #1/2
[3:53] Loading run C:\Users\akiyama\Desktop\Temporary Raw file\240612blood vessel003206.raw
[4:00] 719387 library precursors are potentially detectable
[4:00] Processing...
[4:08] RT window set to 4.96336
[4:08] Peak width: 2.612
[4:08] Scan window radius set to 5
[4:08] Recommended MS1 mass accuracy setting: 8.0409 ppm
[4:17] Optimised mass accuracy: 8.54352 ppm
[4:24] Removing low confidence identifications
[4:24] Removing interfering precursors
[4:24] Training neural networks: 19501 targets, 11538 decoys
[4:25] Number of IDs at 0.01 FDR: 10117
[4:33] Removing low confidence identifications
[4:33] Removing interfering precursors
[4:33] Training neural networks: 19700 targets, 11598 decoys
[4:34] Number of IDs at 0.01 FDR: 10210
[4:34] Calculating protein q-values
[4:34] Number of proteins identified at 1% FDR: 1407 (precursor-level), 1228 (protein-level) (inference performed using proteotypic peptides only)
[4:34] Quantification
[4:34] Quantification information saved to C:\Users\akiyama\Desktop\Temporary Raw file\240612blood vessel003206.raw.quant

[4:34] File #2/2
[4:34] Loading run C:\Users\akiyama\Desktop\Temporary Raw file\240612periosteum003206.raw
[4:41] 719387 library precursors are potentially detectable
[4:41] Processing...
[4:50] RT window set to 4.79655
[4:50] Recommended MS1 mass accuracy setting: 7.19928 ppm
[4:58] Removing low confidence identifications
[4:58] Removing interfering precursors
[4:58] Training neural networks: 16842 targets, 10071 decoys
[4:59] Number of IDs at 0.01 FDR: 9246
[5:06] Removing low confidence identifications
[5:06] Removing interfering precursors
[5:06] Training neural networks: 16943 targets, 10088 decoys
[5:07] Number of IDs at 0.01 FDR: 9064
[5:07] Calculating protein q-values
[5:07] Number of proteins identified at 1% FDR: 1284 (precursor-level), 1143 (protein-level) (inference performed using proteotypic peptides only)
[5:07] Quantification
[5:07] Quantification information saved to C:\Users\akiyama\Desktop\Temporary Raw file\240612periosteum003206.raw.quant

[5:07] Cross-run analysis
[5:07] Reading quantification information: 2 files
[5:07] Quantifying peptides
[5:08] Assembling protein groups
[5:08] Quantifying proteins
[5:08] Calculating q-values for protein and gene groups
[5:08] Calculating global q-values for protein and gene groups
[5:08] Protein groups with global q-value <= 0.01: 1352
[5:08] Compressed report saved to C:\DIA-NN\1.9.1\20260805-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[5:08] Writing report
[5:09] Report saved to C:\DIA-NN\1.9.1\20260805-first-pass.tsv.
[5:09] Saving precursor levels matrix
[5:09] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\DIA-NN\1.9.1\20260805-first-pass.pr_matrix.tsv.
[5:09] Manifest saved to C:\DIA-NN\1.9.1\20260805-first-pass.manifest.txt
[5:09] Stats report saved to C:\DIA-NN\1.9.1\20260805-first-pass.stats.tsv
[5:09] Generating spectral library:
[5:09] 9306 target and 46 decoy precursors saved
WARNING: 2413 precursors without any fragments annotated were skipped
[5:09] Spectral library saved to report-lib.parquet

[5:09] Loading spectral library report-lib.parquet
[5:09] Spectral library loaded: 1341 protein isoforms, 1314 protein groups and 9352 precursors in 8658 elution groups.
[5:09] Loading protein annotations from FASTA C:\Users\akiyama\Desktop\uniprotkb_reviewed_true_AND_taxonomy_id_2024_08_05.fasta
[5:09] Annotating library proteins with information from the FASTA database
[5:09] Gene names missing for some isoforms
[5:09] Library contains 1341 proteins, and 1312 genes
[5:09] Initialising library
[5:09] Saving the library to report-lib.parquet.skyline.speclib


Second pass: using the newly created spectral library to reanalyse the data

[5:09] File #1/2
[5:09] Loading run C:\Users\akiyama\Desktop\Temporary Raw file\240612blood vessel003206.raw
[5:15] 9306 library precursors are potentially detectable
[5:15] Processing...
[5:16] RT window set to 2.29957
[5:16] Recommended MS1 mass accuracy setting: 7.04428 ppm
[5:16] Removing low confidence identifications
[5:16] Removing interfering precursors
[5:16] Training neural networks: 8225 targets, 1795 decoys
[5:16] Number of IDs at 0.01 FDR: 8610
[5:16] Removing low confidence identifications
[5:16] Removing interfering precursors
[5:16] Training neural networks: 8225 targets, 1793 decoys
[5:17] Number of IDs at 0.01 FDR: 8628
[5:17] Calculating protein q-values
[5:17] Number of proteins identified at 1% FDR: 1200 (precursor-level), 1123 (protein-level) (inference performed using proteotypic peptides only)
[5:17] Quantification

[5:17] File #2/2
[5:17] Loading run C:\Users\akiyama\Desktop\Temporary Raw file\240612periosteum003206.raw
[5:23] 9306 library precursors are potentially detectable
[5:23] Processing...
[5:23] RT window set to 2.14403
[5:23] Recommended MS1 mass accuracy setting: 7.90081 ppm
[5:24] Removing low confidence identifications
[5:24] Removing interfering precursors
[5:24] Training neural networks: 7641 targets, 1368 decoys
[5:24] Number of IDs at 0.01 FDR: 8027
[5:24] Removing low confidence identifications
[5:24] Removing interfering precursors
[5:24] Training neural networks: 7643 targets, 1372 decoys
[5:24] Number of IDs at 0.01 FDR: 8030
[5:24] Calculating protein q-values
[5:24] Number of proteins identified at 1% FDR: 1137 (precursor-level), 1055 (protein-level) (inference performed using proteotypic peptides only)
[5:24] Quantification

[5:24] Cross-run analysis
[5:24] Reading quantification information: 2 files
[5:24] Quantifying peptides
WARNING: QuantUMS requires 6 or more runs for the optimisation of its hyperparameters to perform best.
[5:27] Quantification parameters: 0.196384, 0.00787536, 0.00298193, 0.0119537, 0.0125626, 0.0121914, 0.0779277, 0.0138154, 0.0137811, 0.0137228, 0.0389836, 0.0223217, 0.257863, 0.0532416, 0.0697785, 0.0112865
[5:28] Quantifying proteins
[5:28] Calculating q-values for protein and gene groups
[5:28] Calculating global q-values for protein and gene groups
[5:28] Protein groups with global q-value <= 0.01: 1233
[5:28] Compressed report saved to C:\DIA-NN\1.9.1\20260805.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[5:28] Writing report
[5:28] Report saved to C:\DIA-NN\1.9.1\20260805.tsv.
[5:28] Saving precursor levels matrix
[5:28] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\DIA-NN\1.9.1\20260805.pr_matrix.tsv.
[5:28] Saving protein group levels matrix
[5:28] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to C:\DIA-NN\1.9.1\20260805.pg_matrix.tsv.
[5:28] Saving gene group levels matrix
[5:28] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to C:\DIA-NN\1.9.1\20260805.gg_matrix.tsv.
[5:28] Saving unique genes levels matrix
[5:28] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to C:\DIA-NN\1.9.1\20260805.unique_genes_matrix.tsv.
[5:28] Manifest saved to C:\DIA-NN\1.9.1\20260805.manifest.txt
[5:28] Stats report saved to C:\DIA-NN\1.9.1\20260805.stats.tsv