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, and DIA-NN.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 ¹¹/₁₁ rows and ⁸/₈ columns.

Sample Name	MSstats Condition: SP	MSstats Condition: CT	MSstats Condition: QY	MSstats Condition: CV	MSstats BioReplicate	Fraction Group	Fraction	Label
Sample 1	Saccharomyces cerevisiae	Mixture	10%	in-house	1
↳ a05058						1	1	1
↳ a05059						1	2	1
↳ a05060						1	3	1
↳ a05061						1	4	1
↳ a05062						1	5	1
↳ a05063						1	6	1
↳ a05064						1	7	1
↳ a05065						1	8	1
↳ a05066						1	9	1
↳ a05067						1	10	1
↳ a05068						1	11	1
Sample 2	Saccharomyces cerevisiae	Mixture	10%	in-house	2
↳ a05058						1	1	2
↳ a05059						1	2	2
↳ a05060						1	3	2
↳ a05061						1	4	2
↳ a05062						1	5	2
↳ a05063						1	6	2
↳ a05064						1	7	2
↳ a05065						1	8	2
↳ a05066						1	9	2
↳ a05067						1	10	2
↳ a05068						1	11	2
Sample 3	Saccharomyces cerevisiae	Mixture	10%	in-house	3
↳ a05058						1	1	3
↳ a05059						1	2	3
↳ a05060						1	3	3
↳ a05061						1	4	3
↳ a05062						1	5	3
↳ a05063						1	6	3
↳ a05064						1	7	3
↳ a05065						1	8	3
↳ a05066						1	9	3
↳ a05067						1	10	3
↳ a05068						1	11	3
Sample 4	Saccharomyces cerevisiae	Mixture	5%	in-house	4
↳ a05058						1	1	4
↳ a05059						1	2	4
↳ a05060						1	3	4
↳ a05061						1	4	4
↳ a05062						1	5	4
↳ a05063						1	6	4
↳ a05064						1	7	4
↳ a05065						1	8	4
↳ a05066						1	9	4
↳ a05067						1	10	4
↳ a05068						1	11	4
Sample 5	Saccharomyces cerevisiae	Mixture	5%	in-house	5
↳ a05058						1	1	5
↳ a05059						1	2	5
↳ a05060						1	3	5
↳ a05061						1	4	5
↳ a05062						1	5	5
↳ a05063						1	6	5
↳ a05064						1	7	5
↳ a05065						1	8	5
↳ a05066						1	9	5
↳ a05067						1	10	5
↳ a05068						1	11	5
Sample 6	Saccharomyces cerevisiae	Mixture	5%	in-house	6
↳ a05058						1	1	6
↳ a05059						1	2	6
↳ a05060						1	3	6
↳ a05061						1	4	6
↳ a05062						1	5	6
↳ a05063						1	6	6
↳ a05064						1	7	6
↳ a05065						1	8	6
↳ a05066						1	9	6
↳ a05067						1	10	6
↳ a05068						1	11	6
Sample 7	Saccharomyces cerevisiae	Mixture	5%	in-house	7
↳ a05058						1	1	7
↳ a05059						1	2	7
↳ a05060						1	3	7
↳ a05061						1	4	7
↳ a05062						1	5	7
↳ a05063						1	6	7
↳ a05064						1	7	7
↳ a05065						1	8	7
↳ a05066						1	9	7
↳ a05067						1	10	7
↳ a05068						1	11	7
Sample 8	Saccharomyces cerevisiae	Mixture	3.3%	in-house	8
↳ a05058						1	1	8
↳ a05059						1	2	8
↳ a05060						1	3	8
↳ a05061						1	4	8
↳ a05062						1	5	8
↳ a05063						1	6	8
↳ a05064						1	7	8
↳ a05065						1	8	8
↳ a05066						1	9	8
↳ a05067						1	10	8
↳ a05068						1	11	8
Sample 9	Saccharomyces cerevisiae	Mixture	3.3%	in-house	9
↳ a05058						1	1	9
↳ a05059						1	2	9
↳ a05060						1	3	9
↳ a05061						1	4	9
↳ a05062						1	5	9
↳ a05063						1	6	9
↳ a05064						1	7	9
↳ a05065						1	8	9
↳ a05066						1	9	9
↳ a05067						1	10	9
↳ a05068						1	11	9
Sample 10	Saccharomyces cerevisiae	Mixture	3.3%	in-house	10
↳ a05058						1	1	10
↳ a05059						1	2	10
↳ a05060						1	3	10
↳ a05061						1	4	10
↳ a05062						1	5	10
↳ a05063						1	6	10
↳ a05064						1	7	10
↳ a05065						1	8	10
↳ a05066						1	9	10
↳ a05067						1	10	10
↳ a05068						1	11	10
Sample 11	Saccharomyces cerevisiae	Mixture	3.3%	in-house	11
↳ a05058						1	1	11
↳ a05059						1	2	11
↳ a05060						1	3	11
↳ a05061						1	4	11
↳ a05062						1	5	11
↳ a05063						1	6	11
↳ a05064						1	7	11
↳ a05065						1	8	11
↳ a05066						1	9	11
↳ a05067						1	10	11
↳ a05068						1	11	11

Expand table

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
\|\|	MSstats Condition: SP		`MSstats_Condition_SP`
\|\|	MSstats Condition: CT		`MSstats_Condition_CT`
\|\|	MSstats Condition: QY		`MSstats_Condition_QY`
\|\|	MSstats Condition: CV		`MSstats_Condition_CV`
\|\|	MSstats BioReplicate		`MSstats_BioReplicate`
\|\|	Fraction Group		`Fraction_Group`
\|\|	Fraction	Fraction Identifier	`Fraction`
\|\|	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
433836	154772	35.68%	90555	9655	9655

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 quantms pipeline final result.

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.

Showing ¹¹/₁₁ rows and ⁹/₉ columns.

Sample Name	MSstats Condition: SP	MSstats Condition: CT	MSstats Condition: QY	MSstats Condition: CV	Fraction	#Peptide IDs	#Unambiguous Peptide IDs	#Modified Peptide IDs	#Protein (group) IDs
Sample 1	Saccharomyces cerevisiae	Mixture	10%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 2	Saccharomyces cerevisiae	Mixture	10%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 3	Saccharomyces cerevisiae	Mixture	10%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 4	Saccharomyces cerevisiae	Mixture	5%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 5	Saccharomyces cerevisiae	Mixture	5%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 6	Saccharomyces cerevisiae	Mixture	5%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 7	Saccharomyces cerevisiae	Mixture	5%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 8	Saccharomyces cerevisiae	Mixture	3.3%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 9	Saccharomyces cerevisiae	Mixture	3.3%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 10	Saccharomyces cerevisiae	Mixture	3.3%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10
Sample 11	Saccharomyces cerevisiae	Mixture	3.3%	in-house		98697	91611	98697	12872
↳ a05058					1	12495	11321	12495	5631
↳ a05059					2	13568	12325	13568	6045
↳ a05060					3	13003	11783	13003	5875
↳ a05061					4	13433	12260	13433	5902
↳ a05062					5	14216	12956	14216	6159
↳ a05063					6	18009	16369	18009	6420
↳ a05064					7	16285	14718	16285	6068
↳ a05065					8	14215	13004	14215	6077
↳ a05066					9	14554	13271	14554	6093
↳ a05067					10	13765	12537	13765	6024
↳ a05068					11	12	12	12	10

Expand table

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
\|\|	MSstats Condition: SP		`MSstats_Condition_SP`
\|\|	MSstats Condition: CT		`MSstats_Condition_CT`
\|\|	MSstats Condition: QY		`MSstats_Condition_QY`
\|\|	MSstats Condition: CV		`MSstats_Condition_CV`
\|\|	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`

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), or DIA-NN report files.

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), or DIA-NN report files.

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.

Created with MultiQC

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.

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 file
1. _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).

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

Search Engine Scores

Summary of Spectral E-values

This statistic is extracted from idXML files. SpecEvalue: Spectral E-values, the search score of MSGF. The value used for plotting is -lg(SpecEvalue).

Created with MultiQC

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

Consensus Across Search Engines

Consensus support is a measure of agreement between search engines. Every peptide sequence in the analysis has been identified by at least one search run. The consensus support defines which fraction (between 0 and 1) of the remaining search runs "supported" a peptide identification that was kept. The meaning of "support" differs slightly between algorithms: For best, worst, average and rank, each search run supports peptides that it has also identified among its top considered_hits candidates. So the "consensus support" simply gives the fraction of additional search engines that have identified a peptide. (For example, if there are three search runs, peptides identified by two of them will have a "support" of 0.5.) For the similarity-based algorithms PEPMatrix and PEPIons, the "support" for a peptide is the average similarity of the most-similar peptide from each (other) search run.

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 ⁷/₇ columns.

PeptideID	Protein Name	Peptide Sequence	Best Search Score	Average Intensity	SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house
1	sp\|P55011\|S12A2_HUMAN	AAAAAAAAAAAAAAAGAGAGAK	0.9999	5.1101	5.0860	5.1404	5.0960
2	sp\|Q99453\|PHX2B_HUMAN	AAAAAAAAAK	1.0000	5.9295	5.9276	5.9481	5.9117
3	sp\|Q96JP5\|ZFP91_HUMAN	AAAAAAAAAVSR	1.0000	4.2500	4.2237	4.2531	4.2656
4	sp\|Q5TF21\|SOGA3_HUMAN	AAAAAAAAQMHAK	1.0000	4.9208	4.8813	4.9496	4.9193
5	sp\|Q9Y4H2\|IRS2_HUMAN	AAAAAAAAVPSAGPAGPAPTSAAGR	1.0000	4.6795	4.6525	4.7113	4.6659
6	sp\|P36578\|RL4_HUMAN	AAAAAAALQAK	1.0000	5.6602	5.6780	5.6812	5.6235
7	sp\|O00178\|GTPB1_HUMAN	AAAAAAR	0.9997	4.1917	4.0798	4.2682	4.1818
8	sp\|Q6SPF0\|SAMD1_HUMAN	AAAAAATAPPSPGPAQPGPR	0.9970	4.5783	4.5565	4.5829	4.5895
9	sp\|O76031\|CLPX_HUMAN	AAAAADLANR	1.0000	4.9044	4.8651	4.9259	4.9106
10	sp\|Q8WUQ7\|CATIN_HUMAN	AAAAALSQQQSLQER	1.0000	3.5486	3.4986	3.5609	3.5710
11	sp\|A6NIH7\|U119B_HUMAN	AAAAASAAGPGGLVAGK	1.0000	4.5009	4.4867	4.5216	4.4900
12	sp\|Q9BTD8\|RBM42_HUMAN	AAAAATVVPPMVGGPPFVGPVGFGPGDR	0.9997	4.1653	4.1245	4.1983	4.1603
13	sp\|Q9P258\|RCC2_HUMAN	AAAAAWEEPSSGNGTAR	1.0000	4.8387	4.8197	4.8616	4.8291
14	sp\|Q99615\|DNJC7_HUMAN	AAAAECDVVMAATEPELLDDQEAK	0.9989	4.3740	4.3483	4.3851	4.3813
15	sp\|Q9Y2U8\|MAN1_HUMAN	AAAAGSLDR	0.9999	4.9444	4.9107	4.9686	4.9437
16	sp\|P0C0T4\|RS25B_YEAST;sp\|Q3E792\|RS25A_YEAST	AAAALAGGK	0.9999	5.8076	6.0928	5.7184	5.4974
17	sp\|Q15596\|NCOA2_HUMAN	AAAANIDEVQK	1.0000	4.1618	4.1756	4.1710	4.1415
18	sp\|Q9UPT8\|ZC3H4_HUMAN	AAAAPAATTATPPPEGAPPQPGVHNLPVPTLFGTVK	1.0000	4.4684	4.4335	4.5097	4.4498
19	sp\|Q96L91\|EP400_HUMAN	AAAAPFQTSQASASAPR	0.9997	4.2268	4.1954	4.2729	4.2001
20	sp\|P52701\|MSH6_HUMAN	AAAAPGASPSPGGDAAWSEAGPGPR	1.0000	5.3677	5.4158	5.3708	5.3244
21	sp\|P52701\|MSH6_HUMAN	AAAAPGASPSPGGDAAWSEAGPGPRPLAR	1.0000	3.5832	3.5465	3.6371	3.5511
22	sp\|P55036\|PSMD4_HUMAN	AAAASAAEAGIATTGTEDSDDALLK	1.0000	4.9622	4.9446	4.9947	4.9408
23	sp\|P09938\|RIR2_YEAST	AAADALSDLEIK	0.9997	4.8986	5.1140	4.8541	4.6881
24	sp\|O95159\|ZFPL1_HUMAN	AAADSDPNLDPLMNPHIR	1.0000	5.1045	5.0664	5.1372	5.0980
25	sp\|Q6P2E9\|EDC4_HUMAN	AAADTLQGPMQAAYR	1.0000	3.6096	3.5992	3.6228	3.6039
26	sp\|Q6P2E9\|EDC4_HUMAN	AAADTLQGPMQAAYR	1.0000	3.3063	3.2023	3.3229	3.3557
27	sp\|Q9NQP4\|PFD4_HUMAN	AAAEDVNVTFEDQQK	1.0000	4.0292	3.9906	4.0576	4.0276
28	sp\|O95551\|TYDP2_HUMAN	AAAEEGHIIPR	1.0000	5.4478	5.3897	5.4833	5.4515
29	sp\|O15357\|SHIP2_HUMAN	AAAEELLAR	1.0000	5.0724	5.0563	5.0820	5.0745
30	sp\|Q9H9Y2\|RPF1_HUMAN	AAAEELQEAAGAGDGATENGVQPPK	1.0000	4.5297	4.4950	4.5624	4.5206
31	sp\|Q96P70\|IPO9_HUMAN	AAAEEQIK	0.9999	5.2276	5.1954	5.2506	5.2273
32	sp\|P30260\|CDC27_HUMAN	AAAEGLMSLLR	1.0000	4.3024	4.2771	4.3361	4.2854
33	sp\|P00360\|G3P1_YEAST	AAAEGPMK	0.9988	3.9138	4.1444	3.8283	3.7290
34	sp\|P00360\|G3P1_YEAST	AAAEGPMK	1.0000	4.7633	5.0495	4.6743	4.4495
35	sp\|P15180\|SYKC_YEAST	AAAEGVANLHLDEATGEMVSK	1.0000	4.2037	4.4023	4.1753	4.0020
36	sp\|P15180\|SYKC_YEAST	AAAEGVANLHLDEATGEMVSK	1.0000	5.0252	5.2850	4.9685	4.7316
37	sp\|Q9NP50\|SHCAF_HUMAN	AAAEKPEEQGPEPLPISTQEW	1.0000	4.6442	4.6190	4.6637	4.6425
38	sp\|Q01780\|EXOSX_HUMAN	AAAEQAISVR	1.0000	4.9211	4.9080	4.9425	4.9085
39	sp\|O94766\|B3GA3_HUMAN	AAAEQLR	0.9998	4.8666	4.8356	4.8987	4.8555
40	sp\|Q02880\|TOP2B_HUMAN	AAAERPK	0.9991	5.2989	5.2501	5.3288	5.3027
41	sp\|P02786\|TFR1_HUMAN	AAAEVAGQFVIK	0.9999	5.7815	5.7528	5.8144	5.7681
42	sp\|P07954\|FUMH_HUMAN	AAAEVNQDYGLDPK	1.0000	5.1767	5.1675	5.1999	5.1594
43	sp\|P15705\|STI1_YEAST	AAAEYEK	1.0000	4.8083	5.0531	4.7440	4.5642
44	sp\|Q9Y490\|TLN1_HUMAN	AAAFEEQENETVVVK	1.0000	5.1944	5.1751	5.2089	5.1939
45	sp\|Q9P2E9\|RRBP1_HUMAN	AAAFEK	0.9998	5.5448	5.5160	5.5730	5.5365
46	sp\|O94826\|TOM70_HUMAN	AAAFEQLQK	1.0000	5.0643	5.0542	5.0947	5.0397
47	sp\|Q9H9Y2\|RPF1_HUMAN	AAAFPPGFSISEIK	1.0000	4.4674	4.4606	4.4868	4.4522
48	sp\|Q9Y6X3\|SCC4_HUMAN	AAAFYVR	0.9997	4.4727	4.4467	4.4918	4.4721
49	sp\|Q9P2E9\|RRBP1_HUMAN	AAAGEAK	1.0000	4.3334	4.2981	4.3611	4.3302
50	sp\|P23381\|SYWC_HUMAN	AAAGEDYK	1.0000	5.1423	5.1080	5.1395	5.1689

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`
\|\|	SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house	`SP_Saccharomyces_cerevisiae_CT_Mixture_QY_10_CV_in_house`
\|\|	SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house	`SP_Saccharomyces_cerevisiae_CT_Mixture_QY_5_CV_in_house`
\|\|	SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house	`SP_Saccharomyces_cerevisiae_CT_Mixture_QY_3_3_CV_in_house`

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 ⁶/₆ columns.

ProteinID	Protein Name	Number of Peptides	Average Intensity	SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house
1	sp\|A0A096LP55\|QCR6L_HUMAN;sp\|P07919\|QCR6_HUMAN	1	3.7629	3.7228	3.7953	3.7581
2	sp\|A0A0B4J2D5\|GAL3B_HUMAN;sp\|P0DPI2\|GAL3A_HUMAN	9	6.5573	6.5406	6.5786	6.5475
3	sp\|A0A0B4J2F0\|PIOS1_HUMAN	1	4.2780	4.2463	4.3042	4.2739
4	sp\|A0AVF1\|IFT56_HUMAN	3	4.4387	4.4337	4.4436	4.4376
5	sp\|A0AVT1\|UBA6_HUMAN	31	6.7475	6.7247	6.7693	6.7418
6	sp\|A0FGR8\|ESYT2_HUMAN	15	6.2588	6.2497	6.2776	6.2462
7	sp\|A0JNW5\|UH1BL_HUMAN	5	5.2052	5.1740	5.2309	5.2013
8	sp\|A0MZ66\|SHOT1_HUMAN	27	6.3095	6.2787	6.3350	6.3057
9	sp\|A0PJW6\|TM223_HUMAN	3	5.4129	5.3812	5.4432	5.4043
10	sp\|A0PK00\|T120B_HUMAN	1	4.7299	4.7075	4.7537	4.7216
11	sp\|A1A4S6\|RHG10_HUMAN	6	5.5921	5.5618	5.6207	5.5843
12	sp\|A1IGU5\|ARH37_HUMAN	1	4.5503	4.5026	4.5592	4.5746
13	sp\|A1L020\|MEX3A_HUMAN	9	6.0197	5.9861	6.0425	6.0206
14	sp\|A1L0T0\|HACL2_HUMAN	14	6.1952	6.1741	6.2165	6.1891
15	sp\|A1L157\|TSN11_HUMAN	1	4.4176	4.3933	4.4480	4.4036
16	sp\|A1L170\|CA226_HUMAN	1	4.3816	4.3458	4.3944	4.3942
17	sp\|A1L390\|PKHG3_HUMAN	5	5.0416	4.9997	5.0701	5.0423
18	sp\|A1X283\|SPD2B_HUMAN	36	6.7276	6.7006	6.7492	6.7250
19	sp\|A1XBS5\|FA92A_HUMAN	1	3.8100	3.7434	3.8548	3.8091
20	sp\|A2AJT9\|BCLA3_HUMAN	3	5.1031	5.0738	5.1206	5.1064
21	sp\|A2RRD8\|ZN320_HUMAN	1	4.3234	4.2796	4.3412	4.3363
22	sp\|A2RRP1\|NBAS_HUMAN	30	6.4328	6.3973	6.4582	6.4323
23	sp\|A2RTX5\|SYTC2_HUMAN	1	4.1704	4.1527	4.1942	4.1588
24	sp\|A2RU67\|F234B_HUMAN	2	4.7223	4.7009	4.7357	4.7243
25	sp\|A2RUC4\|TYW5_HUMAN	4	5.0917	5.0634	5.1187	5.0844
26	sp\|A3KMH1\|VWA8_HUMAN	33	6.4954	6.5007	6.5080	6.4784
27	sp\|A3KN83\|SBNO1_HUMAN	18	6.1917	6.1675	6.2129	6.1877
28	sp\|A4D161\|F221A_HUMAN	5	5.2597	5.2262	5.2892	5.2533
29	sp\|A4D1E9\|GTPBA_HUMAN	5	5.6777	5.6564	5.6990	5.6714
30	sp\|A4D1P6\|WDR91_HUMAN	13	5.9108	5.8814	5.9328	5.9095
31	sp\|A4D1U4\|DEN11_HUMAN	3	4.5285	4.4789	4.5649	4.5257
32	sp\|A4D2B0\|MBLC1_HUMAN	2	4.1057	4.0507	4.1030	4.1454
33	sp\|A5D8V6\|VP37C_HUMAN	3	5.1016	5.0754	5.1207	5.1012
34	sp\|A5PLL7\|PDES1_HUMAN	2	5.1872	5.1639	5.2028	5.1883
35	sp\|A5PLN9\|TPC13_HUMAN	3	5.6313	5.5989	5.6598	5.6251
36	sp\|A5YKK6\|CNOT1_HUMAN	53	6.9213	6.8919	6.9446	6.9186
37	sp\|A6H8Y1\|BDP1_HUMAN	2	4.9599	4.9276	4.9915	4.9504
38	sp\|A6NC57\|ANR62_HUMAN	1	3.3462	3.2325	3.3558	3.4070
39	sp\|A6NCE7\|MP3B2_HUMAN;sp\|Q9GZQ8\|MLP3B_HUMAN	2	5.3332	5.3261	5.3676	5.3015
40	sp\|A6NCS6\|CB072_HUMAN	4	4.7068	4.6902	4.7139	4.7118
41	sp\|A6ND36\|FA83G_HUMAN	3	4.9101	4.9036	4.9309	4.8935
42	sp\|A6NDB9\|PALM3_HUMAN	11	5.7112	5.6822	5.7320	5.7110
43	sp\|A6NDE4\|RBY1B_HUMAN;sp\|A6NEQ0\|RBY1E_HUMAN;sp\|P0C7P1\|RBY1D_HUMAN;sp\|P0DJD3\|RBY1A_HUMAN;sp\|P0DJD4\|RBY1C_HUMAN;sp\|Q15415\|RBY1F_HUMAN	1	4.7747	4.7245	4.7941	4.7901
44	sp\|A6NDG6\|PGP_HUMAN	5	5.8649	5.8390	5.8840	5.8643
45	sp\|A6NDU8\|CE051_HUMAN	2	4.8114	4.7871	4.8268	4.8133
46	sp\|A6NDX5\|ZN840_HUMAN	1	4.3298	4.2876	4.3691	4.3187
47	sp\|A6NED2\|RCCD1_HUMAN	3	4.7508	4.7096	4.7662	4.7645
48	sp\|A6NFI3\|ZN316_HUMAN	2	4.9098	4.8857	4.9296	4.9071
49	sp\|A6NGN9\|IGLO5_HUMAN	2	5.0687	5.0540	5.0837	5.0642
50	sp\|A6NHL2\|TBAL3_HUMAN	1	4.0439	3.9831	4.0709	4.0582

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`
\|\|	SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house	`SP_Saccharomyces_cerevisiae_CT_Mixture_QY_10_CV_in_house`
\|\|	SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house	`SP_Saccharomyces_cerevisiae_CT_Mixture_QY_5_CV_in_house`
\|\|	SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house	SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house	`SP_Saccharomyces_cerevisiae_CT_Mixture_QY_3_3_CV_in_house`

Peptide Intensity Distribution

Peptide intensity per file from mzTab.

Calculate the average of peptide_abundance_study_variable[1-n] values for each peptide from the peptide table in the mzTab file, and then 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 ¹¹/₁₁ rows and ³/₃ columns.

File	Acquisition Date Time	log10(Total Current)	log10(Scan Current)
a05058	2017-04-05 00:38:45	13.3186	10.7826
a05059	2017-04-05 04:14:11	13.1799	10.6932
a05060	2017-04-05 07:49:24	13.3878	10.7992
a05061	2017-04-05 11:24:37	13.4434	10.8149
a05062	2017-04-05 15:00:06	13.3927	10.8790
a05063	2017-04-05 18:32:46	13.3876	10.9186
a05064	2017-04-05 22:05:41	13.4376	10.9428
a05065	2017-04-06 10:43:06	13.3235	10.8011
a05066	2017-04-06 14:15:46	13.4091	10.8419
a05067	2017-04-06 17:48:21	13.4054	10.8081
a05068	2017-04-06 21:21:20	12.7353	10.4647

Expand table

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`

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 ¹¹/₁₁ rows and ⁶/₆ columns.

Spectra File	#MS1 Spectra	#MS2 Spectra	MSGF	Comet	#PSMs from quant. peptides	#Peptides quantified
a05058	6870	37330	24401	24117	13935	11763
a05059	6471	37536	24920	24702	14683	13183
a05060	7266	38047	24711	24518	14027	12475
a05061	7649	37733	24986	24790	14661	12995
a05062	5657	41445	26957	26729	15121	13672
a05063	5982	42856	29469	29222	18909	17336
a05064	7075	40809	27720	27427	17667	15735
a05065	5947	40698	26495	26127	15303	13323
a05066	6051	41078	26992	26761	15630	13808
a05067	6062	39997	26228	25738	14819	13170
a05068	4297	36307	17727	17634	17	12

Expand table

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`
\|\|	MSGF	Number of spectra identified by MSGF search engine	`MSGF`
\|\|	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

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

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
CONSENSUSID	ConsensusID	`3.3.0-pre-exported-20250122`
DECOYDATABASE	DecoyDatabase	`3.3.0-pre-exported-20250122`
FDRCONSENSUSID	FalseDiscoveryRate	`3.3.0-pre-exported-20250122`
IDFILTER	IDFilter	`3.3.0-pre-exported-20250122`
MS2RESCORE	MS2Rescore	`3.1.4`
	deeplc	`3.1.8`
	ms2pip	`4.1.0`
	quantms-rescoring	`0.0.5`
MSGFDBINDEXING	msgf_plus	`MS-GF+ Release (v2024.03.26) (26 March 2024)`
MZMLSTATISTICS	quantms-utils	`0.0.20`
PERCOLATOR	PercolatorAdapter	`3.3.0-pre-exported-20250122`
	percolator	`3.05.0, Build Date Aug 31 2020 19:03:04`
PROTEOMICSLFQ	ProteomicsLFQ	`3.3.0-pre-exported-20250122`
SAMPLESHEET_CHECK	quantms-utils	`0.0.20`
SDRFPARSING	sdrf-pipelines	`0.0.31`
SEARCHENGINECOMET	Comet	`2023.01 rev. 2`
	CometAdapter	`3.3.0-pre-exported-20250122`
SEARCHENGINEMSGF	MSGFPlusAdapter	`3.3.0-pre-exported-20250122`
	msgf_plus	`MS-GF+ Release (v2023.01.12) (12 January 2023)`
THERMORAWFILEPARSER	ThermoRawFileParser	`1.3.4`
Workflow	Nextflow	`24.10.5`
	bigbio/quantms	`v1.4.0dev`

bigbio/quantms Workflow Summary

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

Input/output options

export_decoy_psm: true
input: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv
local_input_type: raw
outdir: ./PXD007683_msgf_comet_ms2rescore_quant
root_folder: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683

SDRF validation

skip_factor_validation: true
use_ols_cache_only: true
validate_ontologies: true

Protein database

add_decoys: true
database: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta

Database search

search_engines: msgf,comet

Modification localization

luciphor_debug: 0

PSM re-scoring (general)

ms2pip_model: HCD
ms2pip_model_dir: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/
ms2rescore: true
run_fdr_cutoff: 0.10

PSM re-scoring (Percolator)

description_correct_features: 0

Consensus ID

consensusid_considered_top_hits: 0
min_consensus_support: 0

Isobaric analyzer

quant_activation_method: HCD

Protein Quantification (LFQ)

feature_with_id_min_score: 0.10

Statistical post-processing

contrasts: pairwise
skip_post_msstats: true

Quality control

enable_pmultiqc: true
pmultiqc_idxml_skip: true

Generic options

trace_report_suffix: 2025-03-30_11-14-03

Core Nextflow options

configFiles: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/nextflow.config, /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config
containerEngine: docker
launchDir: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
profile: docker
projectDir: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
runName: mad_hodgkin
userName: daicx
workDir: /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/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 v1.4.0dev (doi: 10.5281/zenodo.7754148) 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 v24.10.5 (Di Tommaso et al., 2017) with the following command:

nextflow run main.nf --input /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv --database /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta --add_decoys true --search_engines msgf,comet -resume PXD007683 -c /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config -profile docker --ms2rescore true --ms2pip_model_dir /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/ --root_folder /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683 --local_input_type raw --ms2pip_model HCD --outdir ./PXD007683_msgf_comet_ms2rescore_quant --skip_post_msstats true

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

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.

MultiQC Toolbox

Apply Highlight Samples

Rename Samples Bulk input Apply

Apply Show / Hide Samples

Explain with AI

Export Plots

Choose Plots All None

Save Settings

Load Settings

Tool Citations

About MultiQC

Report AI Summary

pmultiqc

Experimental Design and Metadata

Experimental Design Help

AI Summary

Experimental Design: Columns

Results Overview

Summary Table Help

AI Summary

Summary Table: Columns

HeatMap Help

AI Summary

Pipeline Result Statistics Help

AI Summary

Pipeline Result Statistics: Columns

Identification Summary

Number of Peptides identified Per Protein Help

AI Summary

ProteinGroups Count Help

AI Summary

Peptide ID Count Help

AI Summary

Missed Cleavages Help

AI Summary

Modifications Help

AI Summary

MS/MS Identified Help

AI Summary

Search Engine Scores

Summary of Spectral E-values Help

AI Summary

Summary of cross-correlation scores Help

AI Summary

Summary of Search Engine PEP Help

AI Summary

Consensus Across Search Engines Help

AI Summary

Quantification Analysis

Peptides Quantification Table Help

AI Summary

Peptides Quantification Table: Columns

Protein Quantification Table Help

AI Summary

Protein Quantification Table: Columns

Peptide Intensity Distribution Help

AI Summary

MS1 Analysis

Total Ion Chromatograms Help

AI Summary

MS1 Base Peak Chromatograms Help

AI Summary

MS1 Peaks Help

AI Summary

General stats for MS1 information Help

AI Summary

General stats for MS1 information: Columns

MS2 and Spectral Stats

Number of Peaks per MS/MS spectrum Help

AI Summary

Peak Intensity Distribution Help

AI Summary

Pipeline Spectrum Tracking Help

AI Summary

Pipeline Spectrum Tracking: Columns

Distribution of Precursor Charges Help

AI Summary

Charge-state Help

AI Summary

MS/MS Counts Per 3D-peak Help

Highlight Samples

Rename Samples

Show / Hide Samples

Choose Plots

Experimental Design

Summary Table

HeatMap

Pipeline Result Statistics

Number of Peptides identified Per Protein

ProteinGroups Count

Peptide ID Count

Missed Cleavages

Modifications

MS/MS Identified

Summary of Spectral E-values

Summary of cross-correlation scores

Summary of Search Engine PEP

Consensus Across Search Engines

Peptides Quantification Table

Protein Quantification Table

Peptide Intensity Distribution

Total Ion Chromatograms

MS1 Base Peak Chromatograms

MS1 Peaks

General stats for MS1 information

Number of Peaks per MS/MS spectrum

Peak Intensity Distribution

Pipeline Spectrum Tracking

Distribution of Precursor Charges

Charge-state

MS/MS Counts Per 3D-peak

Delta Mass [Da]

Delta Mass [ppm]

IDs over RT