A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report
generated on 2026-01-13, 08:58 UTC
based on data in:
/home/runner/work/pmultiqc/pmultiqc/data_temp
pmultiqc
pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, DIA-NN, and FragPipe.https://github.com/bigbio/pmultiqc
Results Overview
Summary Table
This table shows the summary statistics of the submitted data.
This table shows the summary statistics of the submitted data.
| #Peptides Quantified | #Proteins Quantified |
|---|---|
| 42367 | 5035 |
HeatMap
This heatmap provides an overview of the performance of FragPipe.
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.
| Sample Name | #Peptide IDs | #Unambiguous Peptide IDs | #Modified Peptide IDs | #Protein (group) IDs |
|---|---|---|---|---|
| 20231020_C33075_002_S577768_33075_multiplexed_fraction_1 | 8007 | 3314 | 8094 | 2973 |
| 20231020_C33075_003_S577769_33075_multiplexed_fraction_2 | 4719 | 1955 | 4668 | 2129 |
| 20231020_C33075_004_S577770_33075_multiplexed_fraction_3 | 6510 | 2760 | 6414 | 2653 |
| 20231020_C33075_005_S577771_33075_multiplexed_fraction_4 | 5652 | 2353 | 5489 | 2379 |
| 20231020_C33075_006_S577772_33075_multiplexed_fraction_5 | 4212 | 1755 | 4130 | 1892 |
| 20231020_C33075_007_S577773_33075_multiplexed_fraction_6 | 5632 | 2293 | 5672 | 2370 |
| 20231020_C33075_008_S577774_33075_multiplexed_fraction_7 | 5165 | 2124 | 5373 | 2265 |
| 20231020_C33075_009_S577775_33075_multiplexed_fraction_8 | 5227 | 2252 | 5436 | 2255 |
| 20231020_C33075_010_S577776_33075_multiplexed_fraction_9 | 5887 | 2446 | 5871 | 2510 |
| 20231020_C33075_011_S577777_33075_multiplexed_fraction_10 | 5265 | 2212 | 5330 | 2245 |
| 20231020_C33075_012_S577778_33075_multiplexed_fraction_11 | 5538 | 2357 | 5586 | 2420 |
| 20231020_C33075_013_S577779_33075_multiplexed_fraction_12 | 4985 | 2105 | 4990 | 2148 |
Identification Summary
Number of Peptides identified Per Protein
This plot shows the number of peptides per protein in the submitted data
Proteins supported by more peptide identifications can constitute more confident results.
ProteinGroups Count
Number of protein groups per raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), DIA-NN report files, or FragPipe psm.tsv.
Peptide ID Count
Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), DIA-NN report files, or FragPipe psm.tsv.
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.
FragPipe: [Number of Missed Cleavages] number of potential enzymatic cleavage sites within the identified sequence.
Modifications
Compute an occurrence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified (but without contaminants).
Post-translational modifications contained within the identified peptide sequence.
1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
3. DPFIANGER
* 33% of 'Oxidation (M)'
* 33% of '2 Oxidation (M)'
* 33% of 'Unmodified'
The plot will show percentages, i.e. is normalized by the total number of peptide sequences (where different charge state counts as a separate peptide) per Raw file. The sum of frequencies may exceed 100% per Raw file, since a peptide can have multiple modifications.
E.g. given three peptides in a single Raw file1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
3. DPFIANGER
, the following frequencies arise:
* 33% of 'Acetyl (Protein N-term)'* 33% of 'Oxidation (M)'
* 33% of '2 Oxidation (M)'
* 33% of 'Unmodified'
Thus, 33% of sequences are unmodified, implying 66% are modified at least once. If a modification, e.g. Oxidation(M), occurs multiple times in a single peptide it's listed as a separate modification (e.g. '2 Oxidation (M)' for double oxidation of a single peptide).
FragPipe: Extracting information from the 'Assigned Modifications' column in psm.tsv. [Assigned Modifications] variable modifications (listed by mass in Da) with modified residue and location within the peptide.Search Engine Scores
Summary of Hyperscore
This statistic is extracted from psm.tsv.
[Hyperscore] Similarity score between observed and theoretical spectra, higher values indicate greater similarity.
[Hyperscore] Similarity score between observed and theoretical spectra, higher values indicate greater similarity.
Contaminants
Top5 Contaminants Per Raw File
The five most abundant external protein contaminants by Raw file.
pmultiqc will explicitly show the five most abundant external protein contaminants
(as detected via MaxQuant's contaminants FASTA file) by Raw file, and summarize the
remaining contaminants as 'other'. This allows to track down which proteins exactly
contaminate your sample. Low contamination is obviously better.
If you see less than 5 contaminants, it either means there are actually less, or that
one (or more) of the shortened contaminant names subsume multiple of the top5
contaminants (since they start with the same prefix).
Potential Contaminants Per File
Potential contaminants per group from psm.tsv.
A contamination plot derived from the psm.tsv.
Identify based on whether the 'Protein' column contains 'cont'.
Quantification Analysis
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.
FragPipe: Use the 'Intensity' column from psm.tsv and apply a log2 transformation.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
Ion Intensity Distribution
Ion-level intensity distribution per sample from ion.tsv.
[FragPipe: ion.tsv] This plot shows the log2-transformed ion intensity
distribution for each sample/channel. The ion.tsv file contains precursor-level
quantification data from IonQuant.
For TMT experiments, each box represents a TMT channel.
For label-free experiments, each box represents a sample/run.
A higher median intensity and narrower distribution typically indicate
better quantification quality. Large differences between samples may
indicate normalization issues or batch effects.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
MS2 and Spectral Stats
Charge-state
Distribution of identified peptide charge states.
[FragPipe: psm.tsv] Charge: charge state of the identified peptide.
Mass Error Trends
Delta Mass [Da]
This plot is based on the "Mass Error" column.
From the evidence.txt ("Mass Error [Da]") generated by MaxQuant and the psm.tsv ("Delta Mass") file generated by FragPipe.
Mass error of the recalibrated mass-over-charge value of the precursor ion in comparison to the
predicted monoisotopic mass of the identified peptide sequence in milli-Dalton.
FragPipe: [Delta Mass] difference between calibrated observed peptide mass and calculated peptide mass (in Da).
FragPipe: [Delta Mass] difference between calibrated observed peptide mass and calculated peptide mass (in Da).
Created with MultiQC
RT Quality Control
IDs over RT
Distribution of retention time, derived from the FragPipe.
Retention in psm.tsv: precursor retention time of the MS2 scan.
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