DIA-NN 1.8.2 beta 8 (Data-Independent Acquisition by Neural Networks) Compiled on Dec 1 2022 14:47:06 Current date and time: Thu Aug 7 08:59:01 2025 Logical CPU cores: 32 /fragpipe_bin/fragpipe-23.1/fragpipe-23.1/tools/diann/1.8.2_beta_8/linux/diann-1.8.1.8 --lib library.tsv --threads 31 --verbose 1 --out dia-quant-output/report.tsv --qvalue 0.01 --matrix-qvalue 0.01 --matrices --no-prot-inf --smart-profiling --no-quant-files --peak-center --no-ifs-removal --report-lib-info --cfg /home/projects/proteomics/workingdir01/filelist_diann.txt-- Thread number set to 31 Output will be filtered at 0.01 FDR Precursor/protein x sample matrices will be filtered at 0.01 precursor & protein-level FDR 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 WARNING: unrecognised option [--] 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. 1 files will be processed [0:00] Loading spectral library library.tsv [0:01] Spectral library loaded: 8559 protein isoforms, 8559 protein groups and 89305 precursors in 72702 elution groups. [0:01] Initialising library [0:01] Saving the library to library.tsv.speclib [0:01] File #1/1 [0:01] Loading run /home/projects/proteomics/sample1/20240911_AST0_NEO4_IAH_collab_SAG_U2OS_Frac_Control_Sol_01_uncalibrated.mzML WARNING: more than 1000 different isolation windows - is this intended? [1:34] 50015 library precursors are potentially detectable [1:34] Processing... [1:34] RT window set to 0.697154 [1:34] Peak width: 3.056 [1:34] Scan window radius set to 6 [1:34] Recommended MS1 mass accuracy setting: 2.78215 ppm [1:37] Optimised mass accuracy: 13.791 ppm [1:41] Removing low confidence identifications [1:41] Removing interfering precursors [1:43] Training neural networks: 49526 targets, 44934 decoys [1:45] Number of IDs at 0.01 FDR: 49483 [1:45] Calculating protein q-values [1:45] Number of protein isoforms identified at 1% FDR: 7631 (precursor-level), 7631 (protein-level) (inference performed using proteotypic peptides only) [1:45] Quantification [1:47] Cross-run analysis [1:47] Reading quantification information: 1 files [1:47] Quantifying peptides [1:47] Quantifying proteins [1:47] Calculating q-values for protein and gene groups [1:47] Calculating global q-values for protein and gene groups [1:47] Writing report [1:48] Report saved to dia-quant-output/report.tsv. [1:48] Saving precursor levels matrix [1:48] Precursor levels matrix (1% precursor and protein group FDR) saved to dia-quant-output/report.pr_matrix.tsv. [1:48] Saving protein group levels matrix [1:48] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to dia-quant-output/report.pg_matrix.tsv. [1:48] Saving gene group levels matrix [1:48] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to dia-quant-output/report.gg_matrix.tsv. [1:48] Saving unique genes levels matrix [1:48] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to dia-quant-output/report.unique_genes_matrix.tsv. [1:48] Stats report saved to dia-quant-output/report.stats.tsv Finished