A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.
Report
generated on 2026-03-14, 08:03 UTC
based on data in:
/home/runner/work/pmultiqc/pmultiqc/data_temp
pmultiqc
pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, DIA-NN, FragPipe, and nf-core/mhcquant.https://github.com/bigbio/pmultiqc
Experimental Design and Metadata
DIA-NN Metadata
This table presents the DIA-NN software version used for the analysis.
DIA-NN metadata, extracted from the DIA-NN log file (report.log.txt), shows the
version of DIA-NN used for data-independent acquisition analysis.
| No. | Parameter | Value |
|---|---|---|
| 1 | DIA-NN Version | 2.1.0 |
Experimental Design
This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.
You can see details about it in
https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html
| Sample Name | MSstats Condition: CT | MSstats Condition: CN | MSstats Condition: QY | MSstats BioReplicate | Fraction Group | Fraction | Label |
|---|---|---|---|---|---|---|---|
| Sample 1 | Mixture | UPS1 | 0.1 fmol | 1 | |||
| 1 | 1 | 1 | |||||
| 2 | 1 | 1 | |||||
| Sample 2 | Mixture | UPS1 | 0.25 fmol | 2 | |||
| 3 | 1 | 1 | |||||
| 4 | 1 | 1 |
Results Overview
Summary Table
This table shows the summary statistics of the submitted data.
This table shows the summary statistics of the submitted data.
| #Peptides Quantified | #Proteins Quantified |
|---|---|
| 5764 | 1527 |
HeatMap
This heatmap provides an overview of the performance of the quantms DIA (DIA-NN) results.
This plot shows the pipeline performance overview. Some metrics are calculated.
*Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants;
1 = no contaminants
*Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold,
i.e. reaching 2^21 of 2^23 gives score 0.25.
*Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative
Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at
tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?),
in-source fragmentation, missed cleavages and buffer irregularities can cause a shift
(see Bittremieux 2017, DOI: 10.1002/mas.21544)
*Heatmap score[RT Alignment]: Compute 1 minus the mean absolute difference between 'RT' and 'Predicted.RT',
and take the maximum of this value and 0. 1: |RT - Predicted.RT| = 0
*Heatmap score [ID rate over RT]: Judge column occupancy over retention time.
Ideally, the LC gradient is chosen such that the number of identifications
(here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles.
Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient
optimization.Scored using 'Uniform' scoring function. i.e. constant receives good score, extreme shapes are bad
*Heatmap score [Norm Factor]: Computes the mean absolute deviation (MAD) of 'Normalisation.Factor' from its mean.
0 = high variability in normalization factors; 1 = perfectly consistent normalization factors
*Heatmap score [Peak Width]: Average peak width (RT.Stop - RT.Start). 1 = peak width equals 0;
0 = peak width equals 1 or greater
Created with MultiQC
Pipeline Result Statistics
This plot shows the final pipeline results.
Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline,
and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained
from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
In the FragPipe results summary, the data were obtained from psm.tsv.
| Sample Name | MSstats Condition: CT | MSstats Condition: CN | MSstats Condition: QY | Fraction | #Peptide IDs | #Unambiguous Peptide IDs | #Modified Peptide IDs | #Protein (group) IDs |
|---|---|---|---|---|---|---|---|---|
| Sample 1 | Mixture | UPS1 | 0.1 fmol | 5575 | 5521 | 879 | 1503 | |
| 1 | 5278 | 5227 | 832 | 1473 | ||||
| 1 | 5442 | 5389 | 860 | 1485 | ||||
| Sample 2 | Mixture | UPS1 | 0.25 fmol | 5705 | 5649 | 899 | 1518 | |
| 1 | 5552 | 5496 | 878 | 1501 | ||||
| 1 | 5541 | 5485 | 873 | 1501 |
File Names vs. Acquisition Times
This table provides the mapping between file names and their corresponding acquisition times.
| File Name | Acquisition Datetime |
|---|---|
| RD139_Narrow_UPS1_0_1fmol_inj1 | 2018-09-01 20:06:01 |
| RD139_Narrow_UPS1_0_25fmol_inj1 | 2018-09-01 22:09:01 |
| RD139_Narrow_UPS1_0_1fmol_inj2 | 2018-09-02 11:02:22 |
| RD139_Narrow_UPS1_0_25fmol_inj2 | 2018-09-02 13:05:24 |
Identification Summary
Number of Peptides identified Per Protein
This plot shows the number of peptides per protein in quantms pipeline final result
This statistic is extracted from the out_msstats file. Proteins supported by more peptide
identifications can constitute more confident results.
ProteinGroups Count
Number of protein groups per raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), DIA-NN report files, or FragPipe psm.tsv.
Peptide ID Count
Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), DIA-NN report files, or FragPipe psm.tsv.
Modifications
Compute an occurrence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified (but without contaminants).
Post-translational modifications contained within the identified peptide sequence.
1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
3. DPFIANGER
* 33% of 'Oxidation (M)'
* 33% of '2 Oxidation (M)'
* 33% of 'Unmodified'
The plot will show percentages, i.e. is normalized by the total number of peptide sequences (where different charge state counts as a separate peptide) per Raw file. The sum of frequencies may exceed 100% per Raw file, since a peptide can have multiple modifications.
E.g. given three peptides in a single Raw file1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
3. DPFIANGER
, the following frequencies arise:
* 33% of 'Acetyl (Protein N-term)'* 33% of 'Oxidation (M)'
* 33% of '2 Oxidation (M)'
* 33% of 'Unmodified'
Thus, 33% of sequences are unmodified, implying 66% are modified at least once. If a modification, e.g. Oxidation(M), occurs multiple times in a single peptide it's listed as a separate modification (e.g. '2 Oxidation (M)' for double oxidation of a single peptide).
FragPipe: Extracting information from the 'Assigned Modifications' column in psm.tsv. [Assigned Modifications] variable modifications (listed by mass in Da) with modified residue and location within the peptide.Peptide Length Distribution
Peptide length distribution per Run.
Peptide length distribution.
FragPipe: psm.tsv ('Peptide Length': number of residues in the peptide sequence).
MaxQuant: evidence.txt ('Length': the length of the sequence stored in the column 'Sequence').
DIA-NN: report.tsv (the length of the 'Stripped.Sequence').
quantms: *.mzTab (the length of sequence).
FragPipe: psm.tsv ('Peptide Length': number of residues in the peptide sequence).
MaxQuant: evidence.txt ('Length': the length of the sequence stored in the column 'Sequence').
DIA-NN: report.tsv (the length of the 'Stripped.Sequence').
quantms: *.mzTab (the length of sequence).
Created with MultiQC
Quantification Analysis
Peptides Quantification Table
This plot shows the quantification information of peptides in the final result (mainly the mzTab file).
The quantification information of peptides is obtained from the MSstats input file.
The table shows the quantitative level and distribution of peptides in different study variables,
run and peptiforms. The distribution show all the intensity values in a bar plot above and below
the average intensity for all the fractions, runs and peptiforms.
* BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to
1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab
peptide table for DDA datasets.
* Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
* Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions,
and then mean intensity in technical replicates/biological replicates separately.
| PeptideID | Protein Name | Peptide Sequence | Best Search Score | Average Intensity | CT=Mixture;CN=UPS1;QY=0.1 fmol | CT=Mixture;CN=UPS1;QY=0.25 fmol |
|---|---|---|---|---|---|---|
| 1 | TOLA_ECOLI | AAAEADDIFGELSSGK | 1.0000 | 6.6835 | 6.6592 | 6.7066 |
| 2 | G3P2_ECOLI | AAAENIIPHTTGAAK | 0.9998 | 6.4493 | 6.4172 | 6.4792 |
| 3 | RLMH_ECOLI | AAAEQSWSLSALTLPHPLVR | 1.0000 | 7.0070 | 6.9520 | 7.0558 |
| 4 | PDXJ_ECOLI | AAAEVGAPFIEIHTGCYADAK | 0.9991 | 7.2257 | 7.2257 | 0.0000 |
| 5 | RL10_ECOLI | AAAFEGELIPASQIDR | 1.0000 | 8.8699 | 8.8317 | 8.9051 |
| 6 | YFGM_ECOLI | AAAQLQQGLADTSDENLK | 1.0000 | 7.5474 | 7.5511 | 7.5438 |
| 7 | YFGM_ECOLI | AAAQLQQGLADTSDENLKAVINLR | 1.0000 | 7.3320 | 7.1963 | 7.4353 |
| 8 | RHLE_ECOLI | AAATGEALSLVCVDEHK | 0.9996 | 6.6056 | 6.5782 | 6.6313 |
| 9 | SYP_ECOLI | AAATQEMTLVDTPNAK | 1.0000 | 7.1783 | 7.1115 | 7.2361 |
| 10 | EUTL_ECOLI | AACNAFTDAVLEIAR | 1.0000 | 6.4793 | 6.4494 | 6.5073 |
| 11 | ACRB_ECOLI | AADGQMVPFSAFSSSR | 1.0000 | 6.6129 | 6.4585 | 6.7266 |
| 12 | YIDA_ECOLI | AADGSTVAQTALSYDDYR | 0.9969 | 6.6673 | 6.7271 | 6.6340 |
| 13 | ADHE_ECOLI | AADIVLQAAIAAGAPK | 0.9998 | 8.3786 | 8.3517 | 8.4039 |
| 14 | NARG_ECOLI | AADLVDALGQENNPEWK | 1.0000 | 6.7575 | 6.6942 | 6.8128 |
| 15 | OXYR_ECOLI | AADSCHVSQPTLSGQIR | 1.0000 | 7.0698 | 7.0386 | 7.0990 |
| 16 | TALA_ECOLI | AAEELEKEGINCNLTLLFSFAQAR | 1.0000 | 7.0472 | 6.9877 | 7.0994 |
| 17 | HEMY_ECOLI | AAELAGNDTIPVEITR | 1.0000 | 7.1291 | 7.1029 | 7.1539 |
| 18 | SYL_ECOLI | AAENNPELAAFIDECR | 1.0000 | 7.5449 | 7.5040 | 7.5823 |
| 19 | TALB_ECOLI | AAEQLEKEGINCNLTLLFSFAQAR | 1.0000 | 7.7540 | 7.6774 | 7.8192 |
| 20 | MBHM_ECOLI | AAESALNIDVPVNAQYIR | 1.0000 | 6.7822 | 6.7678 | 6.7961 |
| 21 | DNAG_ECOLI | AAESGVSRPVPQLKR | 0.9998 | 5.6470 | 5.5268 | 5.7409 |
| 22 | HDFR_ECOLI | AAESLYLTQSAVSFR | 1.0000 | 6.6850 | 6.6320 | 6.7323 |
| 23 | RNE_ECOLI | AAESRPAPFLIHQESNVIVR | 1.0000 | 7.4124 | 7.3874 | 7.4360 |
| 24 | HFLK_ECOLI | AAFDDAIAARENEQQYIR | 1.0000 | 6.5679 | 6.4316 | 6.6715 |
| 25 | AROF_ECOLI | AAFPLSLQQEAQIADSR | 1.0000 | 6.3405 | 6.1849 | 6.4548 |
| 26 | AROF_ECOLI | AAFPLSLQQEAQIADSRK | 0.9994 | 7.0612 | 7.0755 | 7.0463 |
| 27 | BOLA_ECOLI | AAFQPVFLEVVDESYR | 1.0000 | 7.1931 | 7.1704 | 7.2146 |
| 28 | CLPB_ECOLI | AAGATTANITQAIEQMR | 0.9998 | 7.7595 | 7.7095 | 7.8044 |
| 29 | YBIS_ECOLI | AAGEPLPAVVPAGPDNPMGLYALYIGR | 1.0000 | 6.6506 | 6.5366 | 6.7408 |
| 30 | SDHA_ECOLI | AAGLHLQESIAEQGALR | 1.0000 | 6.0782 | 6.0140 | 6.1341 |
| 31 | TALA_ECOLI | AAGLSQYEHLIDDAIAWGK | 0.9977 | 6.8333 | 6.7832 | 6.8563 |
| 32 | TALA_ECOLI | AAGLSQYEHLIDDAIAWGKK | 0.9996 | 7.0255 | 6.9752 | 7.0706 |
| 33 | ADHE_ECOLI | AAGVETEVFFEVEADPTLSIVR | 1.0000 | 6.8999 | 6.9376 | 6.8586 |
| 34 | ADHE_ECOLI | AAGVETEVFFEVEADPTLSIVRK | 0.9994 | 7.4480 | 7.3769 | 7.5091 |
| 35 | ENO_ECOLI | AAGYELGKDITLAMDCAASEFYK | 1.0000 | 7.8599 | 7.8309 | 7.8870 |
| 36 | YEBE_ECOLI | AAHQDEPQFGAQSTPLDER | 1.0000 | 5.8561 | 5.9019 | 5.8049 |
| 37 | RIBB_ECOLI | AAIADGAKPSDLNRPGHVFPLR | 1.0000 | 7.2435 | 7.2118 | 7.2730 |
| 38 | DEOC_ECOLI | AAIAYGADEVDVVFPYR | 1.0000 | 7.5780 | 7.5213 | 7.6282 |
| 39 | IDH_ECOLI | AAIEYAIANDRDSVTLVHK | 1.0000 | 7.8265 | 7.7961 | 7.8549 |
| 40 | SYFA_ECOLI | AAISQASDVAALDNVR | 1.0000 | 7.2636 | 7.2590 | 7.2683 |
| 41 | SYFA_ECOLI | AAISQASDVAALDNVRVEYLGK | 1.0000 | 7.6989 | 7.6390 | 7.7515 |
| 42 | MUKF_ECOLI | AAISSCELLLSETSGTLR | 0.9991 | 6.1443 | 6.1618 | 6.1261 |
| 43 | MSCM_ECOLI | AAKPAQPEVVEALQSALNALEER | 1.0000 | 6.0440 | 5.9216 | 6.1394 |
| 44 | AMPN_ECOLI | AALEQLKGLENLSGDLYEK | 1.0000 | 7.5275 | 7.4481 | 7.5946 |
| 45 | DBHA_ECOLI | AALESTLAAITESLK | 1.0000 | 7.9418 | 7.9523 | 7.9311 |
| 46 | HEM3_ECOLI | AALPPEISLPAVGQGAVGIECR | 0.9989 | 6.6256 | 6.5181 | 6.7118 |
| 47 | SDHA_ECOLI | AALQISQSGQTCALLSK | 1.0000 | 6.1558 | 6.0982 | 6.2067 |
| 48 | THIP_ECOLI | AAMLALLQMVCCLGLVLLSQR | 0.9938 | 5.8533 | 5.8234 | 5.8812 |
| 49 | DHE4_ECOLI | AANAGGVATSGLEMAQNAAR | 1.0000 | 6.8074 | 6.7294 | 6.8735 |
| 50 | GRCA_ECOLI | AANDDLLNSFWLLDSEK | 0.9998 | 6.5632 | 6.5535 | 6.5726 |
Protein Quantification Table
This plot shows the quantification information of proteins in the final result (mainly the mzTab file).
The quantification information of proteins is obtained from the msstats input file.
The table shows the quantitative level and distribution of proteins in different study variables and run.
* Peptides_Number: The number of peptides for each protein.
* Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
* Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.
| ProteinID | Protein Name | Number of Peptides | Average Intensity | CT=Mixture;CN=UPS1;QY=0.1 fmol | CT=Mixture;CN=UPS1;QY=0.25 fmol |
|---|---|---|---|---|---|
| 1 | 3PASE_ECOLI | 2 | 7.0750 | 6.8899 | 7.1117 |
| 2 | 5DNU_ECOLI | 1 | 6.1992 | 6.0985 | 6.2809 |
| 3 | 6PGD_ECOLI | 11 | 8.3687 | 8.3457 | 8.3906 |
| 4 | 6PGL_ECOLI | 2 | 7.6224 | 7.5912 | 7.6458 |
| 5 | AAS_ECOLI | 3 | 6.9049 | 6.9101 | 6.9008 |
| 6 | AAT_ECOLI | 2 | 7.9860 | 7.9633 | 8.0076 |
| 7 | ACCA_ECOLI | 11 | 8.2718 | 8.2244 | 8.3131 |
| 8 | ACCC_ECOLI | 12 | 8.4622 | 8.4333 | 8.4884 |
| 9 | ACCD_ECOLI | 4 | 7.8705 | 7.8532 | 7.8872 |
| 10 | ACEA_ECOLI | 9 | 7.7377 | 7.6981 | 7.7749 |
| 11 | ACFD_ECOLI | 8 | 7.6064 | 7.5641 | 7.6451 |
| 12 | ACKA_ECOLI | 11 | 9.1230 | 9.0903 | 9.1531 |
| 13 | ACNA_ECOLI | 3 | 6.8059 | 6.7185 | 6.8786 |
| 14 | ACNB_ECOLI | 12 | 8.2757 | 8.2333 | 8.3237 |
| 15 | ACP_ECOLI | 1 | 7.6456 | 7.6151 | 7.6741 |
| 16 | ACRA_ECOLI | 6 | 8.1709 | 8.1180 | 8.2180 |
| 17 | ACRB_ECOLI | 6 | 8.5427 | 8.5082 | 8.5746 |
| 18 | ACSA_ECOLI | 2 | 6.4190 | 6.3684 | 6.4437 |
| 19 | ACUI_ECOLI | 4 | 7.8596 | 7.7893 | 7.9198 |
| 20 | ACYP_ECOLI | 1 | 6.3432 | 6.3462 | 6.3416 |
| 21 | ADD_ECOLI | 5 | 7.8573 | 7.8486 | 7.8659 |
| 22 | ADEC_ECOLI | 1 | 5.4947 | 5.4551 | 5.5311 |
| 23 | ADHE_ECOLI | 33 | 9.5637 | 9.5168 | 9.6061 |
| 24 | ADHP_ECOLI | 2 | 6.6090 | 6.6221 | 6.5936 |
| 25 | ADIA_ECOLI | 3 | 7.1023 | 7.0648 | 7.1368 |
| 26 | ADPP_ECOLI | 2 | 7.0454 | 7.0274 | 7.0626 |
| 27 | AGP_ECOLI | 3 | 6.6550 | 6.6051 | 6.6997 |
| 28 | AHPC_ECOLI | 9 | 8.9990 | 8.9973 | 9.0063 |
| 29 | AHPF_ECOLI | 10 | 8.3698 | 8.3316 | 8.4056 |
| 30 | AK1H_ECOLI | 7 | 7.3833 | 7.3373 | 7.4331 |
| 31 | AK2H_ECOLI | 4 | 7.1295 | 7.0868 | 7.1684 |
| 32 | AK3_ECOLI | 5 | 7.4376 | 7.2251 | 7.4546 |
| 33 | ALAA_ECOLI | 2 | 6.8758 | 6.8189 | 6.9261 |
| 34 | ALAC_ECOLI | 5 | 8.0337 | 8.0003 | 8.0648 |
| 35 | ALDB_ECOLI | 1 | 6.4293 | 6.4020 | 6.4549 |
| 36 | ALF1_ECOLI | 3 | 7.0837 | 6.8281 | 7.0958 |
| 37 | ALF_ECOLI | 12 | 9.4130 | 9.3660 | 9.4545 |
| 38 | ALKH_ECOLI | 4 | 7.7455 | 7.6167 | 7.8122 |
| 39 | ALLE_ECOLI | 1 | 6.2408 | 6.1327 | 6.3273 |
| 40 | ALR1_ECOLI | 3 | 7.3584 | 7.3292 | 7.3857 |
| 41 | AMIA_ECOLI | 1 | 5.9310 | 5.9235 | 5.9384 |
| 42 | AMIB_ECOLI | 2 | 6.4302 | 6.3811 | 6.5041 |
| 43 | AMIC_ECOLI | 4 | 6.8883 | 6.8306 | 6.9475 |
| 44 | AMN_ECOLI | 6 | 7.3450 | 7.3281 | 7.3574 |
| 45 | AMPA_ECOLI | 8 | 7.9139 | 7.8890 | 7.9313 |
| 46 | AMPC_ECOLI | 2 | 7.1657 | 7.1088 | 7.2159 |
| 47 | AMPH_ECOLI | 2 | 6.5991 | 6.5581 | 6.6366 |
| 48 | AMPN_ECOLI | 7 | 8.1049 | 8.0640 | 8.1423 |
| 49 | AMPP_ECOLI | 2 | 7.1472 | 7.0959 | 7.1932 |
| 50 | AMY2_ECOLI | 1 | 6.8699 | 6.8140 | 6.9193 |
Intensity Distribution
log2(Precursor.Quantity) for each Run (or Sample).
[DIA-NN: main report] log2(Precursor.Quantity) for each Run (or Sample).
Created with MultiQC
Standard Deviation of Intensity
Standard deviation of intensity by sample (experimental conditions).
[DIA-NN: report.tsv] Sample grouping is derived from the SDRF when available;
otherwise, it is parsed from "Run" names.
First, identify the experimental conditions from the "Run" name.
Then, group the data by experimental condition and Modified.Sequence, and calculate
the standard deviation of log2(Precursor.Quantity).
Created with MultiQC
MS1 Analysis
Total Ion Chromatograms
MS1 quality control information extracted from the spectrum files.
This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples.
The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point.
Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal
throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column.
Created with MultiQC
MS1 Base Peak Chromatograms
MS1 base peak chromatograms extracted from the spectrum files.
The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention time point.
Created with MultiQC
MS1 Peaks
MS1 Peaks from the spectrum files
This plot shows the number of peaks detected in MS1 scans over the course of each sample run.
Created with MultiQC
General stats for MS1 information
General stats for MS1 information extracted from the spectrum files.
This table presents general statistics for MS1 information extracted from mass spectrometry data files.
| Sample Name | Acquisition Date Time | log10(Total Current) | log10(Scan Current) |
|---|---|---|---|
| Sample 1 | - | 12.5541 | 12.4060 |
2018-09-01 20:06:01 | 12.2519 | 12.0890 | |
2018-09-02 11:02:22 | 12.2542 | 12.1204 | |
| Sample 2 | - | 12.5913 | 12.4506 |
2018-09-01 22:09:01 | 12.2904 | 12.1417 | |
2018-09-02 13:05:24 | 12.2901 | 12.1573 |
MS1 Area Distribution
log2(Ms1.Area) for each Run.
[DIA-NN: report.tsv] log2(Ms1.Area) for each Run. Ms1.Area: non-normalised MS1 peak area.
Created with MultiQC
MS1 TIC Proxy
This plot monitors sample loading consistency by tracking the log2-transformed median of summed peak intensities for MS1 spectra,
ordered by acquisition time.
For each run, the summed_peak_intensities from all ms_level: 1 spectra are extracted.
The median value is calculated and log2-transformed to provide a robust representative of the Total Ion Current (TIC) per run.
Created with MultiQC
MS2 and Spectral Stats
Number of Peaks per MS/MS spectrum
Histogram of number of peaks per MS/MS spectrum.
Too few peaks may indicate poor fragmentation; many peaks could indicate noisy spectra.
Peak Intensity Distribution
Histogram of ion intensity vs. frequency for all MS2 spectra.
High number of low intensity noise peaks expected; disproportionate high signal peaks may indicate issues.
Distribution of Precursor Charges
This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
[DIA-NN: main report] distribution of the precursor ion charges for a given whole experiment.
Precursor.Charge: the charge of the precursor.
Charge-state
The distribution of the charge-state of the precursor ion.
[DIA-NN: main report] The distribution of the charge-state of the precursor ion (Precursor.Charge).
MS2 Precursor Intensity Trend
This plot monitors instrument sensitivity by tracking the median intensity of all MS2 precursor ions per run.
This plot tracks instrument sensitivity by extracting intensities from all ms_level: 2 precursor ions.
For each run, the median intensity is calculated as a robust representative value and plotted chronologically by acquisition datetime.
Created with MultiQC
Mass Error Trends
Delta Mass
This plot is based on the "Ms1.Apex.Mz.Delta" column from the DIA-NN main report.
[DIA-NN: main report]
Ms1.Apex.Mz.Delta: difference between observed precursor m/z and the theoretical value.
Created with MultiQC
RT Quality Control
IDs over RT
Distribution of retention time, derived from the main report.
[DIA-NN: main report] Distribution of retention time (RT) for each run.
This plot allows to judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization. See [Moruz 2014, DOI: 10.1002/pmic.201400036](https://pubmed.ncbi.nlm.nih.gov/24700534/) for details.
Created with MultiQC
Normalisation Factor over RT
Distribution of Normalisation.Factor with retention time, derived from the main report.
[DIA-NN: main report] Distribution of Normalisation.Factor with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes. Normalisation.Factor: normalisation factor
applied to the precursor in the specific run,
i.e. normalised quantity = normalisation factor X non-normalised quantity
Created with MultiQC
FWHM over RT
Distribution of FWHM with retention time, derived from the main report.
FWHM: estimated peak width at half-maximum.
[DIA-NN: main report] Distribution of FWHM with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes. FWHM: estimated peak width at half-maximum;
note that the accuracy of such estimates sometimes strongly depends on the DIA cycle time and
sample injection amount, i.e. they can only be used to evaluate chromatographic performance in
direct comparisons with similar settings, including the scan window; another caveat is that
FWHM does not reflect any peak tailing.
Created with MultiQC
Peak Width over RT
Distribution of peak width with retention time, derived from the main report.
Peak Width = RT.Stop - RT.Start.
[DIA-NN: main report] Distribution of peak width with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes. RT.Start and RT.Stop: peak boundaries.
Created with MultiQC
Absolute RT Error over RT
Distribution of rt error with retention time, derived from the main report.
[DIA-NN: main report] Distribution of absolute RT error (|RT - Predicted.RT|) with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes.
Predicted.RT: predicted RT based on the iRT.
Created with MultiQC
LOESS RT ~ iRT
Distribution of LOESS RT ~ iRT for each run, derived from the main report.
[DIA-NN: main report] Distribution of LOESS RT ~ iRT for each run.
RT: the retention time (RT) of the PSM in minutes.
iRT: reference RT as recorded in the spectral library.
Created with MultiQC
MS1 Retention Time Trend
This plot displays the median retention time (RT) of MS1 spectra for each file, ordered by their acquisition datetime.
This plot tracks LC stability by extracting RTs from all ms_level: 1 spectra.
For each run, the median RT is calculated as a robust representative value and plotted chronologically by acquisition datetime.
Created with MultiQC
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Group | Software | Version |
|---|---|---|
| ASSEMBLE_EMPIRICAL_LIBRARY | DIA-NN | 2.1.0 |
| DIANN_MSSTATS | quantms-utils | 0.0.25 |
| FINAL_QUANTIFICATION | DIA-NN | 2.1.0 |
| GENERATE_CFG | quantms-utils | 0.0.25 |
| INDIVIDUAL_ANALYSIS | DIA-NN | 2.1.0 |
| MSSTATS_LFQ | bioconductor-msstats | 4.14.0 |
| r-base | 4.4.2 | |
| PRELIMINARY_ANALYSIS | DIA-NN | 2.1.0 |
| SAMPLESHEET_CHECK | quantms-utils | 0.0.25 |
| SDRF_PARSING | sdrf-pipelines | 0.0.33 |
| THERMORAWFILEPARSER | ThermoRawFileParser | 1.4.5 |
| Workflow | Nextflow | 25.10.4 |
| bigbio/quantms | v1.8.0dev |
bigbio/quantms Workflow Summary
- this information is collected when the pipeline is started.https://github.com/bigbio/quantms
Input/output options
- input
- /home/yueqx/Data_Disk/proteogenomics/quantms/test_data/DIA/PXD026600.sdrf.tsv
- outdir
- /home/yueqx/Data_Disk/proteogenomics/quantms/diann_test_20260225/test_dir/diann_2_1_0/results_dia
Protein database
- database
- /home/yueqx/Data_Disk/proteogenomics/quantms/test_data/DIA/REF_EColi_K12_UPS1_combined.fasta
Database search
- allowed_missed_cleavages
- 1
- max_fr_mz
- 1500
- max_mods
- 2
- max_peptide_length
- 30
- max_pr_mz
- 950
- max_precursor_charge
- 3
- min_fr_mz
- 500
- min_peptide_length
- 15
- min_pr_mz
- 350
Modification localization
- onsite_debug
- 0
PSM re-scoring (general)
- run_fdr_cutoff
- 0.10
PSM re-scoring (Percolator)
- description_correct_features
- 0
Consensus ID
- consensusid_considered_top_hits
- 0
- min_consensus_support
- 0
Protein Quantification (LFQ)
- feature_with_id_min_score
- 0.10
- lfq_intensity_threshold
- 1000
DIA-NN
- diann_normalize
- false
Institutional config options
- custom_config_base
- ../../confs/
Generic options
- publish_dir_mode
- symlink
- trace_report_suffix
- 2026-03-01_10-24-25
Core Nextflow options
- configFiles
- /home/yueqx/Data_Disk/proteogenomics/quantms/diann_test_20260225/ypriverol_dev/quantms/nextflow.config, /home/yueqx/Data_Disk/proteogenomics/quantms/diann_test_20260225/test_dir/diann_2_1_0/../../confs/run_dia_2_1_0.config
- container
- [withLabel:diann:docker.io/library/diann:2.1.0]
- containerEngine
- docker
- launchDir
- /home/yueqx/Data_Disk/proteogenomics/quantms/diann_test_20260225/test_dir/diann_2_1_0
- profile
- docker
- projectDir
- /home/yueqx/Data_Disk/proteogenomics/quantms/diann_test_20260225/ypriverol_dev/quantms
- runName
- chaotic_maxwell
- userName
- yueqx
- workDir
- /home/yueqx/Data_Disk/proteogenomics/quantms/diann_test_20260225/test_dir/diann_2_1_0/work
bigbio/quantms Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.https://github.com/bigbio/quantms
Methods
Data was processed using bigbio/quantms v1.8.0dev (doi: 10.5281/zenodo.15573386) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.
The pipeline was executed with Nextflow v25.10.4 (Di Tommaso et al., 2017) with the following command:
nextflow run ../../ypriverol_dev/quantms/ -profile docker --custom_config_base ../../confs/ -c ../../confs/run_dia_2_1_0.configTools used in the workflow included: OpenMS (Röst et al. 2016), DIA-NN (Demichev et al. 2020), MSstats (Choi et al. 2014), Comet (Eng et al. 2013), MS-GF+ (Kim & Pevzner 2014), ThermoRawFileParser (Hulstaert et al. 2020), Percolator (Käll et al. 2007), Luciphor (Fermin et al. 2017), pMultiQC (Perez-Riverol et al. 2024) .
References
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
- Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
- da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
- Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich HC, Gutenbrunner P, Kenar E, Liang X, Nahnsen S, Nilse L, Pfeuffer J, Rosenberger G, Rurik M, Schmitt U, Veit J, Walzer M, Wojnar D, Wolski WE, Schilling O, Choudhary JS, Malmström L, Aebersold R, Reinert K, Kohlbacher O. (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nature Methods, 13(9), 741–748. doi: 10.1038/nmeth.3959
- Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. (2020). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods, 17(1), 41-44. doi: 10.1038/s41592-019-0638-x
- Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, Vitek O. (2014). MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics, 30(17), 2524–2526. doi: 10.1093/bioinformatics/btu305
- Eng JK, Jahan TA, Hoopmann MR. (2013). Comet: an open-source MS/MS sequence database search tool. Proteomics, 13(1), 22–24. doi: 10.1002/pmic.201200439
- Kim S, Pevzner PA. (2014). MS-GF+ makes progress towards a universal database search tool for proteomics. Nature Communications, 5, 5277. doi: 10.1038/ncomms6277
- Hulstaert N, Shofstahl J, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y. (2020). ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. Journal of Proteome Research, 19(1), 537-542. doi: 10.1021/acs.jproteome.9b00328
- Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. (2007). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 4(11), 923-925. doi: 10.1038/nmeth1113
- Fermin D, Walmsley SJ, Gingras AC, Choi H, Nesvizhskii AI. (2017). LuciPHOr2: site prediction of generic post-translational modifications from tandem mass spectrometry data. Bioinformatics, 33(19), 2926-2933. doi: 10.1093/bioinformatics/btx401
- Perez-Riverol Y, Moreno P, da Veiga Leprevost F, Csordas A, Bai J, Carver J, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA. (2024). pMultiQC: a comprehensive tool for quality control of proteomics data. Nature Methods, 21(1), 1-2. doi: 10.1038/s41592-023-02125-1
Notes:
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.