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 2025-12-25, 13:02 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, 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
| 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 | |||
| 1 | 1 | 1 | ||||||
| Sample 2 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 2 | |||
| 2 | 1 | 1 | ||||||
| Sample 3 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 3 | |||
| 3 | 1 | 1 | ||||||
| Sample 4 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 4 | |||
| 4 | 1 | 1 | ||||||
| Sample 5 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 5 | |||
| 5 | 1 | 1 | ||||||
| Sample 6 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 6 | |||
| 6 | 1 | 1 | ||||||
| Sample 7 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 7 | |||
| 7 | 1 | 1 | ||||||
| Sample 8 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 8 | |||
| 8 | 1 | 1 | ||||||
| Sample 9 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 9 | |||
| 9 | 1 | 1 | ||||||
| Sample 10 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 10 | |||
| 10 | 1 | 1 | ||||||
| Sample 11 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 11 | |||
| 11 | 1 | 1 |
Results Overview
Summary Table
This table shows the quantms pipeline summary statistics.
This table shows the quantms pipeline summary statistics.
| #MS2 Spectra | #Identified MS2 Spectra | %Identified MS2 Spectra | #Peptides Identified | #Proteins Identified | #Proteins Quantified |
|---|---|---|---|---|---|
| 806748 | 537848 | 66.67% | 61110 | 9346 | 8458 |
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.
| 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 | 35347 | 32810 | 4730 | 7417 | |
| 1 | 35347 | 32810 | 4730 | 7417 | |||||
| Sample 2 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 37165 | 34620 | 2533 | 7859 | |
| 1 | 37165 | 34620 | 2533 | 7859 | |||||
| Sample 3 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 37815 | 35263 | 1912 | 7959 | |
| 1 | 37815 | 35263 | 1912 | 7959 | |||||
| Sample 4 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 37130 | 34616 | 2013 | 7757 | |
| 1 | 37130 | 34616 | 2013 | 7757 | |||||
| Sample 5 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 37172 | 34700 | 1772 | 7710 | |
| 1 | 37172 | 34700 | 1772 | 7710 | |||||
| Sample 6 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 37289 | 34787 | 1781 | 7696 | |
| 1 | 37289 | 34787 | 1781 | 7696 | |||||
| Sample 7 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 37053 | 34559 | 1740 | 7726 | |
| 1 | 37053 | 34559 | 1740 | 7726 | |||||
| Sample 8 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 36879 | 34406 | 1946 | 7611 | |
| 1 | 36879 | 34406 | 1946 | 7611 | |||||
| Sample 9 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 37300 | 34850 | 1927 | 7632 | |
| 1 | 37300 | 34850 | 1927 | 7632 | |||||
| Sample 10 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 37239 | 34751 | 1735 | 7633 | |
| 1 | 37239 | 34751 | 1735 | 7633 | |||||
| Sample 11 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 36919 | 34486 | 1697 | 7637 | |
| 1 | 36919 | 34486 | 1697 | 7637 |
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), or DIA-NN report files.
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.
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.
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).
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)
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).
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.
Summary of Search Engine PEP
This statistic is extracted from idXML files.
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.
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 | 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 | 1.0000 | 7.5635 | 7.8547 | 7.1305 | 7.2778 |
| 2 | sp|Q5TF21|SOGA3_HUMAN | AAAAAAAAQMHAK | 0.9999 | 6.8194 | 6.8194 | 0.0000 | 0.0000 |
| 3 | sp|Q9Y4H2|IRS2_HUMAN | AAAAAAAAVPSAGPAGPAPTSAAGR | 1.0000 | 7.5487 | 7.5525 | 0.0000 | 7.5409 |
| 4 | sp|P36578|RL4_HUMAN | AAAAAAALQAK | 1.0000 | 9.0107 | 9.0444 | 8.8642 | 9.0992 |
| 5 | sp|Q6SPF0|SAMD1_HUMAN | AAAAAATAPPSPGPAQPGPR | 1.0000 | 7.5572 | 7.4772 | 7.5710 | 7.6038 |
| 6 | sp|Q8WUQ7|CATIN_HUMAN | AAAAALSQQQSLQER | 1.0000 | 7.2535 | 0.0000 | 0.0000 | 7.2535 |
| 7 | sp|A6NIH7|U119B_HUMAN | AAAAASAAGPGGLVAGK | 0.9999 | 7.6843 | 7.6746 | 7.6609 | 7.7244 |
| 8 | sp|Q9P258|RCC2_HUMAN | AAAAAWEEPSSGNGTAR | 1.0000 | 8.0796 | 7.9541 | 8.1012 | 8.1353 |
| 9 | sp|Q96L91|EP400_HUMAN | AAAAPFQTSQASASAPR | 1.0000 | 7.1562 | 7.2321 | 7.1622 | 7.1302 |
| 10 | sp|P52701|MSH6_HUMAN | AAAAPGASPSPGGDAAWSEAGPGPR | 1.0000 | 7.2481 | 7.2991 | 7.2426 | 7.1980 |
| 11 | sp|P52701|MSH6_HUMAN | AAAAPGASPSPGGDAAWSEAGPGPRPLAR | 1.0000 | 7.5203 | 7.4933 | 7.5347 | 7.5254 |
| 12 | sp|P55036|PSMD4_HUMAN | AAAASAAEAGIATTGTEDSDDALLK | 1.0000 | 7.9610 | 7.8871 | 7.9954 | 7.9677 |
| 13 | sp|A1X283|SPD2B_HUMAN | AAAASVPNADGLK | 1.0000 | 8.0871 | 0.0000 | 0.0000 | 8.0871 |
| 14 | sp|Q8WWH5|TRUB1_HUMAN | AAAAVVAAAAR | 1.0000 | 7.4872 | 7.5504 | 7.3935 | 7.5348 |
| 15 | sp|P09938|RIR2_YEAST | AAADALSDLEIK | 1.0000 | 7.8338 | 8.0377 | 7.7404 | 7.5226 |
| 16 | sp|Q96GQ5|RUSF1_HUMAN | AAADGSLQWEVGGWR | 0.9977 | 7.3983 | 7.3983 | 0.0000 | 0.0000 |
| 17 | sp|O95159|ZFPL1_HUMAN | AAADSDPNLDPLMNPHIR | 1.0000 | 7.4766 | 7.4766 | 0.0000 | 0.0000 |
| 18 | sp|Q6P2E9|EDC4_HUMAN | AAADTLQGPMQAAYR | 1.0000 | 8.2114 | 8.1032 | 0.0000 | 8.2710 |
| 19 | sp|Q9NQP4|PFD4_HUMAN | AAAEDVNVTFEDQQK | 1.0000 | 8.4535 | 8.4058 | 8.4546 | 8.4851 |
| 20 | sp|O15357|SHIP2_HUMAN | AAAEELLAR | 0.9999 | 7.5581 | 0.0000 | 7.6075 | 7.5024 |
| 21 | sp|P30260|CDC27_HUMAN | AAAEGLMSLLR | 1.0000 | 7.2680 | 6.9462 | 7.3082 | 7.3230 |
| 22 | sp|P15180|SYKC_YEAST | AAAEGVANLHLDEATGEMVSK | 1.0000 | 7.8182 | 7.9145 | 7.7394 | 7.5220 |
| 23 | sp|Q9NP50|SHCAF_HUMAN | AAAEKPEEQGPEPLPISTQEW | 1.0000 | 7.0767 | 7.2119 | 0.0000 | 6.8793 |
| 24 | sp|Q01780|EXOSX_HUMAN | AAAEQAISVR | 1.0000 | 7.8330 | 0.0000 | 0.0000 | 7.8330 |
| 25 | sp|P02786|TFR1_HUMAN | AAAEVAGQFVIK | 1.0000 | 7.8392 | 7.8840 | 7.8143 | 7.7924 |
| 26 | sp|P07954|FUMH_HUMAN | AAAEVNQDYGLDPK | 1.0000 | 8.6800 | 8.6560 | 8.6742 | 8.7027 |
| 27 | sp|Q9Y490|TLN1_HUMAN | AAAFEEQENETVVVK | 1.0000 | 8.2954 | 8.2969 | 8.3068 | 8.2885 |
| 28 | sp|O94826|TOM70_HUMAN | AAAFEQLQK | 1.0000 | 8.4147 | 8.3728 | 8.4191 | 8.4394 |
| 29 | sp|Q96S52|PIGS_HUMAN | AAAGAAATHLEVAR | 1.0000 | 8.1765 | 8.1765 | 0.0000 | 0.0000 |
| 30 | sp|Q9H3U1|UN45A_HUMAN | AAAGGLAMLTSMR | 1.0000 | 7.4325 | 0.0000 | 7.4304 | 7.4346 |
| 31 | sp|Q9GZT9|EGLN1_HUMAN | AAAGGQGSAVAAEAEPGK | 1.0000 | 7.5986 | 7.5986 | 0.0000 | 0.0000 |
| 32 | sp|Q9GZT9|EGLN1_HUMAN | AAAGGQGSAVAAEAEPGKEEPPAR | 1.0000 | 7.3369 | 7.3028 | 7.2321 | 7.5142 |
| 33 | sp|P17544|ATF7_HUMAN | AAAGPLDMSLPSTPDIK | 1.0000 | 7.1283 | 6.8848 | 7.1499 | 7.1940 |
| 34 | sp|O14497|ARI1A_HUMAN | AAAGQESEGPAVGPPQPLGK | 1.0000 | 7.8403 | 7.8201 | 7.8202 | 7.8712 |
| 35 | sp|Q9BR61|ACBD6_HUMAN | AAAHLQGLIQVASR | 1.0000 | 7.3271 | 7.2245 | 7.3748 | 7.3664 |
| 36 | sp|P0DMV8|HS71A_HUMAN;sp|P0DMV9|HS71B_HUMAN | AAAIGIDLGTTYSCVGVFQHGK | 1.0000 | 7.1289 | 7.1289 | 0.0000 | 0.0000 |
| 37 | sp|Q8TAE8|G45IP_HUMAN | AAALAAAVAQDPAASGAPSS | 1.0000 | 7.6476 | 7.6741 | 0.0000 | 7.6290 |
| 38 | sp|Q14008|CKAP5_HUMAN | AAALATVNAWAEQTGMK | 1.0000 | 7.6494 | 7.5710 | 7.6614 | 7.6891 |
| 39 | sp|P31948|STIP1_HUMAN | AAALEAMK | 1.0000 | 8.3820 | 8.3476 | 8.4448 | 8.3372 |
| 40 | sp|P31948|STIP1_HUMAN | AAALEFLNR | 1.0000 | 9.0951 | 9.0992 | 9.1237 | 9.0613 |
| 41 | sp|Q68DK7|MSL1_HUMAN | AAALGGPEDEPGAAEAHFLPR | 1.0000 | 7.4992 | 0.0000 | 7.4992 | 0.0000 |
| 42 | sp|Q12955|ANK3_HUMAN | AAALLLQNDNNADVESK | 1.0000 | 6.7923 | 6.7923 | 0.0000 | 0.0000 |
| 43 | sp|Q8NAF0|ZN579_HUMAN | AAALQALQAQAPTSPPPPPPPLK | 0.9996 | 7.0332 | 0.0000 | 0.0000 | 7.0332 |
| 44 | sp|P56945|BCAR1_HUMAN | AAALQYPSPSAAQDMVER | 1.0000 | 7.4761 | 7.4345 | 0.0000 | 7.4956 |
| 45 | sp|Q14498|RBM39_HUMAN | AAAMANNLQK | 1.0000 | 8.0971 | 8.0314 | 8.1297 | 8.1088 |
| 46 | sp|Q9BQ04|RBM4B_HUMAN | AAAMLPTVGEGYGYGPESELSQASAATR | 1.0000 | 6.8052 | 6.8052 | 0.0000 | 0.0000 |
| 47 | sp|Q9BQ04|RBM4B_HUMAN | AAAMLPTVGEGYGYGPESELSQASAATR | 1.0000 | 7.6162 | 7.4976 | 7.7093 | 0.0000 |
| 48 | sp|Q15042|RB3GP_HUMAN | AAAMTPPEEELK | 1.0000 | 8.1021 | 0.0000 | 0.0000 | 8.1021 |
| 49 | sp|Q6UB99|ANR11_HUMAN | AAAPAEGPPGGIQPEAAEPKPTAEAPK | 1.0000 | 7.1285 | 0.0000 | 7.0511 | 7.2512 |
| 50 | sp|P31350|RIR2_HUMAN | AAAPGVEDEPLLR | 1.0000 | 8.4047 | 8.4146 | 8.4046 | 8.3971 |
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 | 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|A0A024RBG1|NUD4B_HUMAN;sp|Q9NZJ9|NUDT4_HUMAN | 1 | 7.6182 | 7.7904 | 7.6751 | 7.4865 |
| 2 | sp|A0A0A6YYC5|TVA14_HUMAN | 1 | 7.8080 | 0.0000 | 7.8080 | 0.0000 |
| 3 | sp|A0A0B4J2D5|GAL3B_HUMAN;sp|P0DPI2|GAL3A_HUMAN | 7 | 9.0060 | 8.9366 | 8.9477 | 9.0210 |
| 4 | sp|A0AVF1|IFT56_HUMAN | 3 | 7.6152 | 7.4290 | 7.3572 | 7.4632 |
| 5 | sp|A0AVT1|UBA6_HUMAN | 19 | 9.1927 | 9.0894 | 9.1329 | 9.2011 |
| 6 | sp|A0FGR8|ESYT2_HUMAN | 10 | 8.6129 | 8.6282 | 8.6154 | 8.4218 |
| 7 | sp|A0JLT2|MED19_HUMAN | 1 | 7.0329 | 7.1629 | 7.0350 | 6.9910 |
| 8 | sp|A0JNW5|UH1BL_HUMAN | 4 | 7.6573 | 7.0527 | 6.9881 | 7.6277 |
| 9 | sp|A0MZ66|SHOT1_HUMAN | 11 | 8.5320 | 8.1621 | 8.5007 | 8.3048 |
| 10 | sp|A0PJW6|TM223_HUMAN | 1 | 7.2444 | 7.1459 | 7.2162 | 7.3152 |
| 11 | sp|A0PK00|T120B_HUMAN | 2 | 7.3972 | 0.3010 | 7.1447 | 7.4736 |
| 12 | sp|A1L020|MEX3A_HUMAN | 8 | 8.5322 | 8.4172 | 8.4686 | 8.3708 |
| 13 | sp|A1L0T0|HACL2_HUMAN | 10 | 8.4628 | 8.4028 | 8.2699 | 8.3568 |
| 14 | sp|A1L390|PKHG3_HUMAN | 3 | 7.4518 | 7.0501 | 6.9547 | 7.3954 |
| 15 | sp|A1X283|SPD2B_HUMAN | 24 | 9.1792 | 9.0516 | 9.1259 | 9.1847 |
| 16 | sp|A2RRP1|NBAS_HUMAN | 19 | 8.6161 | 8.5332 | 8.4991 | 8.5837 |
| 17 | sp|A2RUC4|TYW5_HUMAN | 4 | 7.5948 | 7.3995 | 6.9391 | 7.3579 |
| 18 | sp|A3KMH1|VWA8_HUMAN | 24 | 8.7303 | 8.6333 | 8.4186 | 8.5408 |
| 19 | sp|A3KN83|SBNO1_HUMAN | 9 | 8.2706 | 8.1618 | 7.9632 | 7.8503 |
| 20 | sp|A4D161|F221A_HUMAN | 2 | 8.3032 | 7.0180 | 8.3041 | 7.0936 |
| 21 | sp|A4D1E9|GTPBA_HUMAN | 5 | 7.8848 | 7.2447 | 7.6046 | 7.7357 |
| 22 | sp|A4D1P6|WDR91_HUMAN | 4 | 8.2410 | 6.9871 | 7.3815 | 8.1742 |
| 23 | sp|A4D2B8|PM2P1_HUMAN | 1 | 7.9174 | 0.0000 | 0.0000 | 7.9174 |
| 24 | sp|A5D8V6|VP37C_HUMAN | 1 | 7.6780 | 7.6054 | 7.7104 | 7.7100 |
| 25 | sp|A5YKK6|CNOT1_HUMAN | 41 | 9.2554 | 9.1394 | 9.0876 | 9.2288 |
| 26 | sp|A5YM72|CRNS1_HUMAN | 1 | 7.5357 | 7.5357 | 0.0000 | 0.0000 |
| 27 | sp|A5Z2X5|YP010_YEAST | 1 | 6.8123 | 6.8123 | 0.0000 | 0.0000 |
| 28 | sp|A6NCE7|MP3B2_HUMAN;sp|Q9GZQ8|MLP3B_HUMAN | 1 | 8.4373 | 8.4216 | 8.4021 | 8.4650 |
| 29 | sp|A6NDB9|PALM3_HUMAN | 4 | 7.6389 | 7.0067 | 7.4330 | 7.5512 |
| 30 | sp|A6NDG6|PGP_HUMAN | 4 | 8.3821 | 8.3570 | 8.3769 | 8.4088 |
| 31 | sp|A6NDU8|CE051_HUMAN | 2 | 7.8688 | 7.7968 | 7.7529 | 7.8533 |
| 32 | sp|A6NFI3|ZN316_HUMAN | 3 | 8.2524 | 7.5023 | 7.4077 | 8.0845 |
| 33 | sp|A6NGN9|IGLO5_HUMAN | 5 | 8.1099 | 7.8933 | 8.0293 | 8.1413 |
| 34 | sp|A6NHL2|TBAL3_HUMAN | 2 | 8.1946 | 8.0283 | 8.1988 | 8.1949 |
| 35 | sp|A6NHQ2|FBLL1_HUMAN | 5 | 8.9518 | 8.9035 | 8.9699 | 8.9462 |
| 36 | sp|A6NHR9|SMHD1_HUMAN | 46 | 9.7252 | 9.5768 | 9.5881 | 9.7305 |
| 37 | sp|A6NIH7|U119B_HUMAN | 2 | 7.7692 | 7.6746 | 7.7509 | 7.8008 |
| 38 | sp|A6NJ78|MET15_HUMAN | 3 | 7.7809 | 7.2598 | 0.4771 | 7.8244 |
| 39 | sp|A6NKD9|CC85C_HUMAN | 4 | 7.7193 | 7.3300 | 6.9448 | 7.6628 |
| 40 | sp|A6NKG5|RTL1_HUMAN | 41 | 10.0303 | 9.9912 | 10.0242 | 10.0413 |
| 41 | sp|A6NNE9|MARHB_HUMAN | 1 | 6.7344 | 0.0000 | 0.0000 | 6.7344 |
| 42 | sp|A6ZKI3|RTL8C_HUMAN | 2 | 7.1764 | 0.3010 | 6.7994 | 7.1581 |
| 43 | sp|A7E2V4|ZSWM8_HUMAN | 7 | 8.3233 | 7.4292 | 7.7053 | 8.3208 |
| 44 | sp|A7KAX9|RHG32_HUMAN | 2 | 7.8542 | 0.3010 | 0.3010 | 7.8542 |
| 45 | sp|A8CG34|P121C_HUMAN | 2 | 7.7171 | 7.6553 | 7.6905 | 7.7313 |
| 46 | sp|A8MT69|CENPX_HUMAN | 1 | 7.7040 | 7.6315 | 7.7263 | 7.6915 |
| 47 | sp|A8MTJ3|GNAT3_HUMAN | 1 | 7.3227 | 0.0000 | 7.3227 | 0.0000 |
| 48 | sp|A8MUU1|FB5L3_HUMAN | 1 | 8.3119 | 8.3119 | 0.0000 | 0.0000 |
| 49 | sp|A8MVJ9|HPF1L_HUMAN | 1 | 7.2282 | 0.0000 | 0.0000 | 7.2282 |
| 50 | sp|A8MVW0|F1712_HUMAN | 3 | 7.6607 | 7.5248 | 7.1330 | 7.6752 |
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.
| File | Acquisition Date Time | log10(Total Current) | log10(Scan Current) |
|---|---|---|---|
| a05191 | 2017-04-19 12:31:30 | 13.2781 | 11.3916 |
| a05192 | 2017-04-19 16:00:49 | 13.3983 | 11.4832 |
| a05194 | 2017-04-19 23:00:02 | 13.4088 | 11.5156 |
| a05195 | 2017-04-20 02:29:46 | 13.3459 | 11.4725 |
| a05196 | 2017-04-20 05:59:32 | 13.3672 | 11.4841 |
| a05197 | 2017-04-20 09:29:26 | 13.3514 | 11.4879 |
| a05198 | 2017-04-20 12:59:18 | 13.3825 | 11.4890 |
| a05199 | 2017-04-20 16:29:14 | 13.4123 | 11.5023 |
| a05200 | 2017-04-20 19:59:09 | 13.3301 | 11.4724 |
| a05201 | 2017-04-20 23:29:04 | 13.4156 | 11.5317 |
| a05202 | 2017-04-21 02:58:59 | 13.3374 | 11.4784 |
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.
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
| Spectra File | #MS1 Spectra | #MS2 Spectra | MSGF | Comet | #PSMs from quant. peptides | #Peptides quantified |
|---|---|---|---|---|---|---|
| a05191 | 13776 | 71049 | 57924 | 56965 | 46612 | 32650 |
| a05192 | 13613 | 73549 | 60638 | 59470 | 49573 | 35498 |
| a05194 | 13450 | 74198 | 60932 | 59956 | 49928 | 36652 |
| a05195 | 13296 | 72696 | 59554 | 58516 | 48629 | 35805 |
| a05196 | 13420 | 73023 | 59967 | 58738 | 48929 | 36063 |
| a05197 | 13258 | 73458 | 60032 | 59227 | 49038 | 36150 |
| a05198 | 13423 | 73438 | 60113 | 59008 | 48700 | 35939 |
| a05199 | 13443 | 73666 | 60381 | 59432 | 49130 | 35596 |
| a05200 | 12979 | 73290 | 60109 | 59215 | 49118 | 36025 |
| a05201 | 13360 | 74857 | 61266 | 60081 | 49555 | 36140 |
| a05202 | 13243 | 73524 | 60284 | 59242 | 48636 | 35837 |
Distribution of Precursor Charges
Bar chart of precursor ion charge distribution.
Use to identify potential ionization problems or unexpected distributions.
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.
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.
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 trueReferences
- 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.