A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report
generated on 2026-01-22, 15:03 UTC
based on data in:
/home/runner/work/pmultiqc/pmultiqc/data_temp
pmultiqc
pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, DIA-NN, and FragPipe.https://github.com/bigbio/pmultiqc
Experimental Design and Metadata
FragPipe Parameters
This table presents the parameters used in FragPipe analysis.
FragPipe parameters, extracted from fragpipe.workflow, summarizes the settings
used for the FragPipe analysis. Key parameters include FragPipe version, search
engine settings (enzyme, mass tolerances), modifications, database used, and
IonQuant settings like Match-Between-Runs (MBR) and normalization options.
| No. | Parameter | Value |
|---|---|---|
| 1 | Enzyme | trypsin |
| 2 | Enzyme Cut Site | KR |
| 3 | Max Missed Cleavages | 2 |
| 4 | Precursor Mass Tolerance (Lower) | -20.0 |
| 5 | Precursor Mass Tolerance (Upper) | 20 |
| 6 | Precursor Mass Units | 1 |
| 7 | Fragment Mass Tolerance | 20 |
| 8 | Fragment Mass Units | 1 |
| 9 | Variable Modification 1 | 15.9949 M 3 |
| 10 | Variable Modification 2 | 42.0106 [^ 1 |
| 11 | Database Path | 2025-03-28-decoys-contam-mouse_modified.fasta.fas |
| 12 | Match Between Runs (MBR) | 1 |
| 13 | Normalization | 1 |
| 14 | Requantify | 1 |
| 15 | TMT Channels | TMT-6 |
| 16 | TMT Reference Tag | Bridge |
| 17 | Number of Threads | 4 # Number of CPU threads to use. |
| 18 | Decoy Prefix | rev_ # Prefix of the decoy protein entries. Used for parameter optimization only. |
| 19 | Isotope Error | -1/0/1/2/3 # Also search for MS/MS events triggered on specified isotopic peaks. |
| 20 | Mass Offsets | 0.0 # Creates multiple precursor tolerance windows with specified mass offsets. |
| 21 | Precursor True Tolerance | 20 # True precursor mass tolerance (window is +/- this value). |
| 22 | Precursor True Units | 1 # True precursor mass tolerance units (0 for Da 1 for ppm). |
| 23 | Calibrate Mass | 2 # Perform mass calibration (0 for OFF 1 for ON 2 for ON and find optimal parameters 4 for ON and find the optimal fragment mass tolerance). |
| 24 | Clip N-term Met | 1 # Specifies the trimming of a protein N-terminal methionine as a variable modification (0 or 1). |
| 25 | Min Peptide Length | 7 # Minimum length of peptides to be generated during in-silico digestion. |
| 26 | Max Peptide Length | 50 # Maximum length of peptides to be generated during in-silico digestion. |
Experimental Design
This table shows the experimental design extracted from the FragPipe manifest file.
The experiment design table shows which raw files belong to which experiment
and biological replicate. This information is extracted from the FragPipe
manifest file (fp-manifest).
| No. | File Name | Experiment | BioReplicate | Data Type |
|---|---|---|---|---|
| 1 | mc38_neg1.mzML | mc38_neg | 1 | DDA |
| 2 | mc38_neg2.mzML | mc38_neg | 2 | DDA |
| 3 | mc38_neg3.mzML | mc38_neg | 3 | DDA |
| 4 | mc38_p1.mzML | mc38_p | 1 | DDA |
| 5 | mc38_p2.mzML | mc38_p | 2 | DDA |
| 6 | mc38_p3.mzML | mc38_p | 3 | DDA |
| 7 | mc38_pos1.mzML | mc38_pos | 1 | DDA |
| 8 | mc38_pos2.mzML | mc38_pos | 2 | DDA |
| 9 | mc38_pos3.mzML | mc38_pos | 3 | DDA |
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 |
|---|---|
| 7472 | 1089 |
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 |
|---|---|---|---|---|
| mc38_neg1 | 2524 | 1147 | 424 | 487 |
| mc38_neg2 | 2710 | 1217 | 471 | 500 |
| mc38_neg3 | 2576 | 1173 | 408 | 468 |
| mc38_p1 | 1686 | 745 | 284 | 333 |
| mc38_p2 | 1492 | 656 | 259 | 292 |
| mc38_p3 | 1739 | 730 | 301 | 357 |
| mc38_pos1 | 3662 | 1754 | 744 | 628 |
| mc38_pos2 | 3634 | 1739 | 708 | 610 |
| mc38_pos3 | 3836 | 1834 | 768 | 639 |
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 [MBR gain: +72.99%]
combined_peptide.tsv
combined_peptide.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.Peptide Length Distribution
Peptide length distribution per Run.
Peptide length distribution.
FragPipe: psm.tsv ('Peptide Length': number of residues in the peptide sequence).
MaxQuant: evidence.txt ('Length': the length of the sequence stored in the column 'Sequence').
DIA-NN: report.tsv (the length of the 'Stripped.Sequence').
quantms: *.mzTab (the length of sequence).
FragPipe: psm.tsv ('Peptide Length': number of residues in the peptide sequence).
MaxQuant: evidence.txt ('Length': the length of the sequence stored in the column 'Sequence').
DIA-NN: report.tsv (the length of the 'Stripped.Sequence').
quantms: *.mzTab (the length of sequence).
Created with MultiQC
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.
Quantification Analysis
Protein Intensity Distribution
Protein intensity distribution from combined_protein.tsv.
[FragPipe: combined_protein.tsv] This plot shows the log2-transformed protein
intensity distribution for each sample. The combined_protein.tsv file contains
protein-level quantification data from IonQuant.
For label-free experiments, each box represents the MaxLFQ intensity distribution.
For TMT experiments, intensity values from TMT channels are shown.
A higher median intensity and narrower distribution typically indicate better
quantification quality. Large differences between samples may indicate
normalization issues or batch effects.
Contaminant proteins (when available) are shown separately to help assess
the level of contamination in each sample.
Created with MultiQC
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.
Created with MultiQC
MS2 and Spectral Stats
Charge-state
Distribution of identified peptide charge states.
[FragPipe: psm.tsv] Charge: charge state of the identified peptide.
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.
[FragPipe: combined_ion.tsv] This plot shows the distribution of MS/MS spectral counts per ion/peak for each sample.
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