MultiQC Report

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

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

Showing ⁸/₈ rows and ⁵/₅ columns.

Sample Name	Condition	BioReplicate	Fraction Group	Fraction	Label
Sample 1	Control	1
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep1_EG-1			1	1	1
Sample 2	Control	2
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep2_EG-2			2	1	1
Sample 3	Control	3
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep3_EG-3			3	1	1
Sample 4	Control	4
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep4_EG-4			4	1	1
Sample 5	Sulforaphane	5
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep1_EG-5			5	1	1
Sample 6	Sulforaphane	6
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep2_EG-6			6	1	1
Sample 7	Sulforaphane	7
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep3_EG-7			7	1	1
Sample 8	Sulforaphane	8
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep4_EG-8			8	1	1

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: experimental_design_table

Sort	Column	Description	ID
\|\|	Condition	Condition	`Condition`
\|\|	BioReplicate	BioReplicate	`BioReplicate`
\|\|	Fraction Group	Fraction Group	`FractionGroup`
\|\|	Fraction	Fraction Identifier	`Fraction`
\|\|	Label	Label	`Label`

Results Overview

Summary Table

This table shows the quantms pipeline summary statistics.

Showing ¹/₁ rows and ⁵/₅ columns.

#MS2 Spectra	#Identified MS2 Spectra	%Identified MS2 Spectra	#Peptides Identified	#Proteins Identified	#Proteins Quantified
348010	158202	45.46%	16827	2399	2315

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: identification_summary_table_table

Sort	Column	Description	ID
\|\|	#Identified MS2 Spectra	Total number of MS/MS spectra identified	`Identified_MS2_Spectra`
\|\|	%Identified MS2 Spectra	Percentage of Identified MS/MS Spectra	`Identified_MS2_Spectra-1`
\|\|	#Peptides Identified	#Peptides Identified	`Peptides_Identified`
\|\|	#Proteins Identified	#Proteins Identified	`Proteins_Identified`
\|\|	#Proteins Quantified	#Proteins Quantified	`Proteins_Quantified`

HeatMap

This heatmap provides an overview of the performance of quantms.

This plot shows the pipeline performance overview.

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.

Showing ⁸/₈ rows and ⁶/₆ columns.

Sample Name	Condition	Fraction	#Peptide IDs	#Unambiguous Peptide IDs	#Protein (group) IDs
Sample 1	Control		8164	7951	1935
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep1_EG-1		1	8164	7951	1935
Sample 2	Control		10005	9723	2010
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep2_EG-2		1	10005	9723	2010
Sample 3	Control		10310	10056	2019
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep3_EG-3		1	10310	10056	2019
Sample 4	Control		10577	10287	2023
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep4_EG-4		1	10577	10287	2023
Sample 5	Sulforaphane		9572	9338	1960
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep1_EG-5		1	9572	9338	1960
Sample 6	Sulforaphane		10609	10355	2014
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep2_EG-6		1	10609	10355	2014
Sample 7	Sulforaphane		10607	10331	2019
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep3_EG-7		1	10607	10331	2019
Sample 8	Sulforaphane		10929	10668	2019
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep4_EG-8		1	10929	10668	2019

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: pipeline_result_statistics_table

Sort	Column	Description	ID
\|\|	Condition	Condition	`Condition`
\|\|	Fraction	Fraction Identifier	`Fraction`
\|\|	#Peptide IDs	The number of identified PSMs in the pipeline	`Peptide_Num`
\|\|	#Unambiguous Peptide IDs	The number of unique peptides in the pipeline. Those that match only one protein in the provided database	`Unique_Peptide_Num`
\|\|	#Modified Peptide IDs	Number of modified identified peptides in the pipeline	`Modified_Peptide_Num`
\|\|	#Protein (group) IDs	The number of identified protein(group)s in the pipeline	`Protein_Num`

File Names vs. Acquisition Times

This table provides the mapping between file names and their corresponding acquisition times.

Showing ⁸/₈ rows.

File Name	Acquisition Datetime
20220830_JL-4884_Forster_Ecoli_DMSO_rep1_EG-1	2022-09-01 03:56:50
20220830_JL-4884_Forster_Ecoli_Suf_rep1_EG-5	2022-09-01 06:42:05
20220830_JL-4884_Forster_Ecoli_DMSO_rep2_EG-2	2022-09-01 14:57:51
20220830_JL-4884_Forster_Ecoli_Suf_rep2_EG-6	2022-09-01 17:43:05
20220830_JL-4884_Forster_Ecoli_DMSO_rep3_EG-3	2022-09-02 01:58:50
20220830_JL-4884_Forster_Ecoli_Suf_rep3_EG-7	2022-09-02 04:44:04
20220830_JL-4884_Forster_Ecoli_DMSO_rep4_EG-4	2022-09-02 12:59:47
20220830_JL-4884_Forster_Ecoli_Suf_rep4_EG-8	2022-09-02 15:45:02

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: filename_datetime_table

Sort	Visible	Group	Column	Description	ID	Scale
\|\|			Acquisition Datetime	Acquisition Datetime	`acquisition_datetime`

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.

Created with MultiQC

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.

Created with MultiQC

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.

Created with MultiQC

Missed Cleavages

Missed Cleavages by Run (or Sample).

Under optimal digestion conditions (high enzyme grade etc.), only few missed cleavages (MC) are expected. In general, increased MC counts also increase the number of peptide signals, thus cluttering the available space and potentially provoking overlapping peptide signals, biasing peptide quantification. Thus, low MC counts should be favored. Interestingly, it has been shown recently that incorporation of peptides with missed cleavages does not negatively influence protein quantification (see [Chiva, C., Ortega, M., and Sabido, E. Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation. J. Proteome Res. 2014, 13, 3979-86](https://doi.org/10.1021/pr500294d) ). However this is true only if all samples show the same degree of digestion. High missed cleavage values can indicate for example, either a) failed digestion, b) a high (post-digestion) protein contamination, or c) a sample with high amounts of unspecifically degraded peptides which are not digested by trypsin. If MC>=1 is high (>20%) you should re-analyse with increased missed cleavages parameters and compare the number of peptides. Usually high MC correlates with bad identification rates, since many spectra cannot be matched to the forward database.
FragPipe: [Number of Missed Cleavages] number of potential enzymatic cleavage sites within the identified sequence.

Created with MultiQC

MS/MS Identified

MS/MS identification rate by Run (or Sample).

MS/MS identification rate by Run (or Sample) (quantms data from mzTab and mzML files; MaxQuant data from summary.txt)

Created with MultiQC

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).

Created with MultiQC

Search Engine Scores

Summary of cross-correlation scores

This statistic is extracted from idXML files. xcorr: cross-correlation scores, the search score of Comet. The value used for plotting is xcorr.

Created with MultiQC

Summary of Search Engine PEP

This statistic is extracted from idXML files.

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.

Showing ⁵⁰/₅₀ rows and ⁶/₆ columns.

PeptideID	Protein Name	Peptide Sequence	Best Search Score	Average Intensity	Control	Sulforaphane
1	sp\|P00959\|SYM_ECOLI	AAAAPVTGPLADDPIQETITFDDFAK	1.0000	7.9976	8.0211	7.9728
2	sp\|P60061\|ADIC_ECOLI	AAADDGLFPPIFAR	1.0000	8.5132	8.5918	8.2935
3	sp\|P25738\|MSYB_ECOLI	AAADEWDER	1.0000	8.7532	8.7054	8.7859
4	sp\|P0AEG4\|DSBA_ECOLI	AAADVQLR	1.0000	8.7235	8.7148	8.7321
5	sp\|P19934\|TOLA_ECOLI	AAAEADDIFGELSSGK	1.0000	8.2250	8.2250	0.0000
6	sp\|P0A8I8\|RLMH_ECOLI	AAAEQSWSLSALTLPHPLVR	1.0000	7.5814	0.0000	7.5814
7	sp\|P0A8T7\|RPOC_ECOLI	AAAESSIQVK	1.0000	9.3244	9.3066	9.3415
8	sp\|P0A7J3\|RL10_ECOLI	AAAFEGELIPASQIDR	1.0000	10.1585	10.1702	10.1464
9	sp\|P0A9Q7\|ADHE_ECOLI	AAALAAADAR	1.0000	10.0167	10.0666	9.9603
10	sp\|P67087\|RSMI_ECOLI	AAALAAEIHGVK	0.9996	7.8254	7.4218	8.0309
11	sp\|P76576\|YFGM_ECOLI	AAAQLQQGLADTSDENLK	1.0000	8.8898	8.8613	8.9165
12	sp\|P0AET8\|HDHA_ECOLI	AAASHLVR	1.0000	8.6520	8.7646	7.7009
13	sp\|P16659\|SYP_ECOLI	AAATQEMTLVDTPNAK	1.0000	9.1399	9.1652	9.1037
14	sp\|P0ABQ0\|COABC_ECOLI	AAATQHNLEVLASR	1.0000	8.2816	8.1817	8.3628
15	sp\|P0ADC3\|LOLC_ECOLI	AAATQPAEALR	1.0000	7.8243	7.8243	0.0000
16	sp\|P10121\|FTSY_ECOLI	AAAVEQLQVWGQR	1.0000	8.5321	8.5389	8.5252
17	sp\|P45955\|CPOB_ECOLI	AADAMFK	1.0000	8.1929	8.2467	7.8666
18	sp\|P15639\|PUR9_ECOLI	AADEGLEVK	1.0000	8.4273	8.4428	8.4111
19	sp\|P37908\|YFJD_ECOLI	AADEIYFVPEGTPLSTQLVK	1.0000	7.3786	7.5148	7.1790
20	sp\|P37641\|YHJC_ECOLI	AADFFALPK	1.0000	7.1548	7.1433	7.1660
21	sp\|P27248\|GCST_ECOLI	AADFWR	1.0000	8.6849	8.6008	8.7215
22	sp\|P31224\|ACRB_ECOLI	AADGQMVPFSAFSSSR	1.0000	7.9769	0.0000	7.9769
23	sp\|P0A8Y5\|YIDA_ECOLI	AADGSTVAQTALSYDDYR	1.0000	8.3123	8.2858	8.3373
24	sp\|P50456\|MLC_ECOLI	AADILFPVISDSIR	1.0000	7.4345	7.4345	0.0000
25	sp\|P0A9Q7\|ADHE_ECOLI	AADIVLQAAIAAGAPK	1.0000	8.7000	8.5429	8.7891
26	sp\|P09152\|NARG_ECOLI	AADLVDALGQENNPEWK	1.0000	8.3929	8.4471	8.3082
27	sp\|P0A7J7\|RL11_ECOLI	AADMTGADIEAMTR	1.0000	10.0873	10.0677	10.1060
28	sp\|P0A7J7\|RL11_ECOLI	AADMTGADIEAMTRSIEGTAR	1.0000	7.3863	0.0000	7.3863
29	sp\|P0A6Y8\|DNAK_ECOLI	AADNKSLGQFNLDGINPAPR	1.0000	7.7067	7.7105	7.6952
30	sp\|P50465\|END8_ECOLI	AADNLEAAIK	1.0000	7.8332	7.8042	7.9101
31	sp\|P0ADG4\|SUHB_ECOLI	AAEAVIIDTIR	1.0000	8.8448	8.8393	8.8521
32	sp\|P35340\|AHPF_ECOLI	AAEELNKR	0.9998	6.7511	6.7511	0.0000
33	sp\|Q59385\|COPA_ECOLI	AAEFGVLVR	1.0000	8.1346	8.1230	8.1459
34	sp\|P0ACB7\|HEMY_ECOLI	AAELAGNDTIPVEITR	1.0000	8.4091	8.3753	8.4697
35	sp\|P05847\|TTDA_ECOLI	AAELELR	0.9998	8.2380	0.0000	8.2380
36	sp\|P28249\|ASMA_ECOLI	AAENFDNVTR	1.0000	7.8913	7.8576	7.9792
37	sp\|P30864\|YAFC_ECOLI	AAEQLGQANSAVSR	1.0000	7.5983	7.6151	7.5236
38	sp\|P0ADC1\|LPTE_ECOLI	AAEQLIR	1.0000	8.3972	0.0000	8.3972
39	sp\|P07658\|FDHF_ECOLI	AAEQYVIDEYNK	1.0000	9.0044	8.9995	9.0093
40	sp\|P0ACE0\|MBHM_ECOLI	AAESALNIDVPVNAQYIR	1.0000	8.7230	8.6921	8.7519
41	sp\|P0A8R9\|HDFR_ECOLI	AAESLYLTQSAVSFR	1.0000	7.9714	7.9212	8.0165
42	sp\|P07650\|TYPH_ECOLI	AAEVFGR	1.0000	8.2554	8.4527	7.8834
43	sp\|P0ABC7\|HFLK_ECOLI	AAFDDAIAAR	1.0000	9.0045	8.9733	9.0428
44	sp\|P0ABA4\|ATPD_ECOLI	AAFDFAVEHQSVER	1.0000	8.7102	8.5878	8.8055
45	sp\|P37651\|GUN_ECOLI	AAFDNILDWTQNNLAQGSLK	1.0000	7.6431	7.5985	7.7024
46	sp\|P65870\|QUED_ECOLI	AAFKPTYER	1.0000	7.9919	7.9099	8.0277
47	sp\|P30958\|MFD_ECOLI	AAFLAVDNHK	1.0000	7.9040	7.8773	7.9412
48	sp\|P0AD59\|IVY_ECOLI	AAFNQMVQGHK	1.0000	8.3450	8.2679	8.4002
49	sp\|P39451\|ADHP_ECOLI	AAFNSAVDAVR	1.0000	8.6120	8.5942	8.6455
50	sp\|P05041\|PABB_ECOLI	AAFPGGSITGAPK	1.0000	8.0928	8.0151	8.1586

Expand table

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: peptides_quantification_table_table

Sort	Column	Description	ID
\|\|	Protein Name	Name/Identifier(s) of the protein (group)	`ProteinName`
\|\|	Peptide Sequence	Peptide Sequence	`PeptideSequence`
\|\|	Best Search Score	Best Search Score	`BestSearchScore`
\|\|	Average Intensity	Average intensity across all conditions	`Average_Intensity`
\|\|	Control	Control	`Control`
\|\|	Sulforaphane	Sulforaphane	`Sulforaphane`

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.

Showing ⁵⁰/₅₀ rows and ⁵/₅ columns.

ProteinID	Protein Name	Number of Peptides	Average Intensity	Control	Sulforaphane
1	CON_ENSEMBL:ENSBTAP00000024462;CON_ENSEMBL:ENSBTAP00000024466	1	7.6849	7.6849	0.0000
2	CON_O76013	1	7.7206	7.7206	0.0000
3	CON_P00761	7	11.0119	11.0292	10.9924
4	CON_P01966	2	7.9845	7.8709	7.8047
5	CON_P02070	2	8.3506	8.0953	7.9986
6	CON_P02533	3	8.7814	8.7873	8.7700
7	CON_P02538	2	8.3141	8.1165	8.5289
8	CON_P02662	4	8.9994	9.1077	6.9143
9	CON_P02663	5	9.1589	9.1768	7.8948
10	CON_P02666	3	9.3573	9.3573	0.4771
11	CON_P02668	1	8.2426	8.2426	0.0000
12	CON_P02754	6	9.1969	9.1969	0.7782
13	CON_P02769	7	8.7809	8.7545	8.8023
14	CON_P04259	1	7.9492	7.8619	8.0218
15	CON_P04264	20	10.5669	10.6571	10.4526
16	CON_P08779	9	9.0533	8.8943	9.1962
17	CON_P13645	18	10.2074	10.3714	9.8415
18	CON_P13647	4	9.0163	9.1061	8.8417
19	CON_P19001	1	7.2940	7.3931	7.1653
20	CON_P19013	1	8.7977	8.7910	8.8108
21	CON_P20930	2	7.9920	7.5009	7.8228
22	CON_P35527	25	10.2992	10.3461	10.2449
23	CON_P35900	1	7.4052	7.4052	0.0000
24	CON_P35908	1	7.5284	7.6612	7.1944
25	CON_P35908v2	1	8.1333	8.2900	7.6752
26	CON_Q04695	3	8.2725	7.6639	8.4765
27	CON_Q1RMK2	2	7.7254	7.6998	7.3804
28	CON_Q3SZ57	1	8.1115	8.0031	8.3323
29	CON_Q3ZBD7	1	8.8338	8.8156	8.8513
30	CON_Q5D862	5	8.0818	8.1552	7.7680
31	CON_Q6ISB0;CON_Q9NSB2	4	8.2765	8.2765	0.6021
32	CON_Q6KB66-1	2	7.6695	7.5363	7.0914
33	CON_Q7RTT2;CON_Q8N1N4-2	8	8.7128	8.6971	7.2632
34	CON_Q7Z794	1	7.1855	7.1855	0.0000
35	CON_Q86YZ3	8	8.2955	8.3041	8.2202
36	CON_Q8VED5	1	7.6312	7.6312	0.0000
37	CON_Q99456	1	7.2723	0.0000	7.2723
38	sp\|A5A613\|YCIY_ECOLI	1	8.1821	8.1821	0.0000
39	sp\|P00350\|6PGD_ECOLI	22	9.7935	9.7558	9.8176
40	sp\|P00363\|FRDA_ECOLI	26	10.5125	10.5281	10.4738
41	sp\|P00370\|DHE4_ECOLI	16	9.4577	9.2352	9.4379
42	sp\|P00393\|NDH_ECOLI	9	9.1095	8.8448	9.1941
43	sp\|P00448\|SODM_ECOLI	6	8.4133	8.3561	8.3618
44	sp\|P00452\|RIR1_ECOLI	2	8.1650	8.1795	8.1224
45	sp\|P00490\|PHSM_ECOLI	24	9.7498	9.6380	9.8160
46	sp\|P00509\|AAT_ECOLI	23	10.2899	10.2367	10.3192
47	sp\|P00547\|KHSE_ECOLI	3	8.3783	8.0735	8.4210
48	sp\|P00550\|PTM3C_ECOLI	8	8.9958	8.7744	8.9916
49	sp\|P00561\|AK1H_ECOLI	27	9.4750	9.3168	9.5524
50	sp\|P00562\|AK2H_ECOLI	20	9.4718	9.2382	9.4877

Expand table

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: protein_quant_result_table

Sort	Column	Description	ID
\|\|	Protein Name	Name/Identifier(s) of the protein (group)	`ProteinName`
\|\|	Number of Peptides	Number of peptides per proteins	`Peptides_Number`
\|\|	Average Intensity	Average intensity across all conditions	`Average_Intensity`
\|\|	Control	Control	`Control`
\|\|	Sulforaphane	Sulforaphane	`Sulforaphane`

Peptide Intensity Distribution

Peptide intensity per Run.

quantms: Calculate the average of peptide_abundance_study_variable[1-n] values for each peptide from the peptide table in the 'mzTab', and then apply a log2 transformation.
FragPipe: Use the 'Intensity' column from psm.tsv and apply a log2 transformation.

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.

Showing ⁸/₈ rows and ³/₃ columns.

Sample Name	Acquisition Date Time	log10(Total Current)	log10(Scan Current)
Sample 1	-	13.3931	11.9798
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep1_EG-1	2022-09-01 03:56:50	13.3931	11.9798
Sample 2	-	13.3286	11.9780
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep2_EG-2	2022-09-01 14:57:51	13.3286	11.9780
Sample 3	-	13.3058	11.9485
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep3_EG-3	2022-09-02 01:58:50	13.3058	11.9485
Sample 4	-	13.2950	11.9244
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep4_EG-4	2022-09-02 12:59:47	13.2950	11.9244
Sample 5	-	13.3883	12.0491
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep1_EG-5	2022-09-01 06:42:05	13.3883	12.0491
Sample 6	-	13.3700	12.0362
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep2_EG-6	2022-09-01 17:43:05	13.3700	12.0362
Sample 7	-	13.3627	11.9869
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep3_EG-7	2022-09-02 04:44:04	13.3627	11.9869
Sample 8	-	13.3728	12.0193
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep4_EG-8	2022-09-02 15:45:02	13.3728	12.0193

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: ms_general_stats_table

Sort	Column	Description	ID
\|\|	Acquisition Date Time	Acquisition Date Time	`AcquisitionDateTime`
\|\|	log10(Total Current)	log10(Total Current)	`log10_TotalCurrent`
\|\|	log10(Scan Current)	log10(Scan Current)	`log10_ScanCurrent`

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.

Created with MultiQC

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.

Created with MultiQC

Pipeline Spectrum Tracking

This plot shows the tracking of the number of spectra along the quantms pipeline

This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified and quantified is obtained from the PSM table in the mzTab file. You can also remove decoys with the `remove_decoy` parameter.: * MS1_Num: The number of MS1 spectra extracted from mzMLs * MS2_Num: The number of MS2 spectra extracted from mzMLs * MSGF: The Number of spectra identified by MSGF search engine * Comet: The Number of spectra identified by Comet search engine * Sage: The Number of spectra identified by Sage search engine * PSMs from quant. peptides: extracted from PSM table in mzTab file * Peptides quantified: extracted from PSM table in mzTab file

Showing ⁸/₈ rows and ⁵/₅ columns.

Sample Name	#MS1 Spectra	#MS2 Spectra	Comet	#PSMs from quant. peptides	#Peptides quantified
Sample 1	8923	41807	29922	19158	8164
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep1_EG-1	8923	41807	29922	19158	8164
Sample 2	8189	43504	30987	19339	10005
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep2_EG-2	8189	43504	30987	19339	10005
Sample 3	7391	45633	32068	19885	10310
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep3_EG-3	7391	45633	32068	19885	10310
Sample 4	8156	43336	30646	19199	10577
↳ 20220830_JL-4884_Forster_Ecoli_DMSO_rep4_EG-4	8156	43336	30646	19199	10577
Sample 5	8411	43055	31581	20493	9572
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep1_EG-5	8411	43055	31581	20493	9572
Sample 6	8174	43587	31432	20238	10609
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep2_EG-6	8174	43587	31432	20238	10609
Sample 7	7938	44173	31734	20234	10607
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep3_EG-7	7938	44173	31734	20234	10607
Sample 8	8384	42915	30901	19656	10929
↳ 20220830_JL-4884_Forster_Ecoli_Suf_rep4_EG-8	8384	42915	30901	19656	10929

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: pipeline_spectrum_tracking_table

Sort	Column	Description	ID
\|\|	#MS1 Spectra	Number of MS1 spectra	`MS1_Num`
\|\|	#MS2 Spectra	Number of MS2 spectra	`MS2_Num`
\|\|	Comet	Number of spectra identified by Comet search engine	`Comet`
\|\|	#PSMs from quant. peptides	Number of reliable PSMs from peptides IDs used in quantification	`num_quant_psms`
\|\|	#Peptides quantified	Number of quantified peptides that passed final protein and peptide FDR thresholds.	`num_quant_peps`

Distribution of Precursor Charges

Bar chart of precursor ion charge distribution.

Use to identify potential ionization problems or unexpected distributions.

Created with MultiQC

Charge-state

The distribution of precursor ion charge states (based on mzTab data).

Charge distribution by Run (or Sample). 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). The charge distribution should be similar across Raw files. Consistent charge distribution is paramount for comparable 3D-peak intensities across samples.

Precursor ion charge states are based on mzTab data.

Created with MultiQC

MS/MS Counts Per 3D-peak

An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.

For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

Created with MultiQC

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 [Da]

This chart represents the distribution of the relative frequency of experimental precursor ion mass (m/z) - theoretical precursor ion mass (m/z).

Mass deltas close to zero reflect more accurate identifications and also that the reporting of the amino acid modifications and charges have been done accurately. This plot can highlight systematic bias if not centered on zero. Other distributions can reflect modifications not being reported properly. Also it is easy to see the different between the target and the decoys identifications.

Created with MultiQC

Delta Mass [ppm]

Delta Mass [ppm] calculated from mzTab.

Delta Mass [ppm] calculated from mzTab: ((experimental m/z - theoretical m/z) / (theoretical m/z)) x 10^6.

Created with MultiQC

RT Quality Control

IDs over RT

Distribution of retention time, derived from the mzTab.

The uncalibrated retention time in minutes in the elution profile of the precursor ion.

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

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
COMET	Comet	`null`
	CometAdapter	`3.5.0-pre-HEAD-2025-12-12`
GENERATE_DECOY_DATABASE	DecoyDatabase	`3.5.0-pre-HEAD-2025-12-12`
ID_FILTER	IDFilter	`3.5.0-pre-HEAD-2025-12-12`
MZML_INDEXING	FileConverter	`3.5.0-pre-HEAD-2025-12-12`
MZML_STATISTICS	quantms-utils	`0.0.24`
PERCOLATOR	PercolatorAdapter	`3.5.0-pre-HEAD-2025-12-12`
	percolator	`3.06.0, Build Date Mar 11 2025 11:26:42`
PROTEOMICSLFQ	ProteomicsLFQ	`3.5.0-pre-HEAD-2025-12-12`
SAMPLESHEET_CHECK	quantms-utils	`0.0.24`
SDRF_PARSING	sdrf-pipelines	`0.0.33`
Workflow	Nextflow	`25.10.4`
	bigbio/quantms	`v1.7.0-ge719f43`

bigbio/quantms Workflow Summary

this information is collected when the pipeline is started.https://github.com/bigbio/quantms

Input/output options

input: data/PXD040621/PXD040621.sdrf.tsv
outdir: results/PXD040621
root_folder: /workspaces/dsp_course_proteomics_intro/data/PXD040621/mzML/

Protein database

add_decoys: true
database: data/fasta/merged_ecoli_with_contaminants.fasta

Database search

max_fr_mz: 1800
max_pr_mz: 2400
min_fr_mz: 100
min_pr_mz: 400

Modification localization

onsite_debug: 0

PSM re-scoring (Percolator)

description_correct_features: 0

Consensus ID

consensusid_considered_top_hits: 0
min_consensus_support: 0

Isobaric analyzer

isotope_correction: true
min_precursor_intensity: 1
min_precursor_purity: 0
min_reporter_intensity: 0
precursor_isotope_deviation: 10

Protein Quantification (LFQ)

lfq_intensity_threshold: 1000

Statistical post-processing

skip_post_msstats: true

Quality control

pmultiqc_idxml_skip: false

Generic options

trace_report_suffix: 2026-05-30_07-10-49

Core Nextflow options

configFiles: /workspaces/.nextflow/assets/bigbio/quantms/nextflow.config, /workspaces/dsp_course_proteomics_intro/nextflow.config
containerEngine: docker
launchDir: /workspaces/dsp_course_proteomics_intro
profile: docker
projectDir: /workspaces/.nextflow/assets/bigbio/quantms
revision: 1.7.0
runName: nostalgic_poincare
userName: root
workDir: /workspaces/dsp_course_proteomics_intro/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 v1.7.0 (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 bigbio/quantms -revision 1.7.0 -params-file PXD040621_w_contaminants-params.yaml -profile docker -resume

Tools 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.

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