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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite all of the tools that you use in your analysis.

        About MultiQC

        This report was generated using MultiQC, version 1.32

        Video: Using MultiQC Reports MultiQC homepage MultiQC documentation Source code Issue tracker

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        MultiQC is developed by Seqera.

        Scroll to top

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-11-07, 16:57 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data

        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

        Parameters

        This table presents the parameters used in MaxQuant.
        MaxQuant parameters, extracted from parameters.txt, summarizes the settings used for the MaxQuant analysis. Key parameters are MaxQuant version, Re-quantify, Match-between-runs and mass search tolerances. A list of protein database files is also provided, allowing to track database completeness and database version information (if given in the filename).
        Showing 51/51 rows and 2/2 columns.
        No.ParameterValue
        1
        Version
        1.5.2.8
        2
        User name
        cbielow
        3
        Machine name
        CD02-WIN7
        4
        Date of writing
        08/05/2015 11:38:59
        5
        Fixed modifications
        Carbamidomethyl (C)
        6
        Decoy mode
        revert
        7
        Special AAs
        KR
        8
        Include contaminants
        True
        9
        MS/MS tol. (FTMS)
        20 ppm
        10
        Top MS/MS peaks per 100 Da. (FTMS)
        12
        11
        MS/MS deisotoping (FTMS)
        True
        12
        MS/MS tol. (ITMS)
        0.5 Da
        13
        Top MS/MS peaks per 100 Da. (ITMS)
        8
        14
        MS/MS deisotoping (ITMS)
        False
        15
        MS/MS tol. (TOF)
        40 ppm
        16
        Top MS/MS peaks per 100 Da. (TOF)
        10
        17
        MS/MS deisotoping (TOF)
        True
        18
        MS/MS tol. (Unknown)
        0.5 Da
        19
        Top MS/MS peaks per 100 Da. (Unknown)
        8
        20
        MS/MS deisotoping (Unknown)
        False
        21
        PSM FDR
        0.0
        22
        Protein FDR
        0.0
        23
        Site FDR
        0.0
        24
        Use Normalized Ratios For Occupancy
        True
        25
        Min. peptide Length
        7
        26
        Min. score for unmodified peptides
        0
        27
        Min. score for modified peptides
        40
        28
        Min. delta score for unmodified peptides
        0
        29
        Min. delta score for modified peptides
        6
        30
        Min. unique peptides
        0
        31
        Min. razor peptides
        1
        32
        Min. peptides
        1
        33
        Use only unmodified peptides and
        True
        34
        Modifications included in protein quantification
        Acetyl (Protein N-term);Oxidation (M)
        35
        Peptides used for protein quantification
        Razor
        36
        Discard unmodified counterpart peptides
        True
        37
        Min. ratio count
        2
        38
        Re-quantify
        False
        39
        Use delta score
        False
        40
        iBAQ
        False
        41
        iBAQ log fit
        False
        42
        Match between runs
        True
        43
        Matching time window [min]
        0.7
        44
        Alignment time window [min]
        20
        45
        Find dependent peptides
        False
        46
        Fasta file
        crap_withMycoplasma.fasta;uniprot_human_canonical_and_isoforms_20130513.fasta
        47
        Labeled amino acid filtering
        True
        48
        Site tables
        Oxidation (M)Sites.txt
        49
        RT shift
        False
        50
        Advanced ratios
        True
        51
        First pass AIF correlation
        0.8
        Expand table

        Results Overview

        Summary Table

        This table shows the MaxQuant summary statistics.
        This table presents summary statistics generated by MaxQuant. "#MS2 Spectra" is derived from msmsScans.txt (or msScans.txt); "#Identified MS2 Spectra" and "#Peptides Identified" are derived from evidence.txt; "#Proteins Identified" and "#Proteins Quantified" are derived from proteinGroups.txt.
        Showing 1/1 rows and 5/5 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified#Proteins Quantified
        201567
        84900
        42.12%
        28949
        4053
        4048

        HeatMap

        This heatmap provides an overview of the performance of MaxQuant.
        This plot shows the pipeline performance overview.
        Created with MultiQC

        Identification Summary

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in the MaxQuant data.
        This statistic is extracted from the proteinGroups.txt file. Proteins supported by more peptide identifications can constitute more confident results.
        Created with MultiQC

        ProteinGroups Count [MBR gain: +8.8%]

        [Excludes Contaminants] Number of Protein groups (after FDR) per Raw file.
        If MBR was enabled, three categories ('Genuine (Exclusive)', 'Genuine + Transferred', 'Transferred (Exclusive)' are shown, so the user can judge the gain that MBR provides. Here, 'Transferred (Exclusive)' means that this protein group has peptide evidence which originates only from transferred peptide IDs. The quantification is (of course) always from the local Raw file. Proteins in the 'Genuine + Transferred' category have peptide evidence from within the Raw file by MS/MS, but at the same time also peptide IDs transferred to this Raw file using MBR were used. It is not unusual to see the 'Genuine + Transferred' category be the rather large, since a protein group usually has peptide evidence from both sources. To see of MBR worked, it is better to look at the two MBR-related metrics. If MBR would be switched off, you can expect to see the number of protein groups corresponding to 'Genuine (Exclusive)' + 'Genuine + Transferred'. In general, if the MBR gain is low and the MBR scores are bad (see the two MBR-related metrics), MBR should be switched off for the Raw files which are affected (could be a few or all).
        Created with MultiQC

        Peptide ID Count [MBR gain: +18.46%]

        [Excludes Contaminants] Number of unique (i.e. not counted twice) peptide sequences including modifications (after FDR) per Raw file.
        If MBR was enabled, three categories ('Genuine (Exclusive)', 'Genuine + Transferred', 'Transferred (Exclusive)' are shown, so the user can judge the gain that MBR provides. Peptides in the 'Genuine + Transferred' category were identified within the Raw file by MS/MS, but at the same time also transferred to this Raw file using MBR. This ID transfer can be correct (e.g. in case of different charge states), or incorrect -- see MBR-related metrics to tell the difference. Ideally, the 'Genuine + Transferred' category should be rather small, the other two should be large. If MBR would be switched off, you can expect to see the number of peptides corresponding to 'Genuine (Exclusive)' + 'Genuine + Transferred'. In general, if the MBR gain is low and the MBR scores are bad (see the two MBR-related metrics), MBR should be switched off for the Raw files which are affected (could be a few or all).
        Created with MultiQC

        Missed Cleavages Per Raw File

        [Excludes Contaminants] Missed Cleavages per raw file.
        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.

        In the rare case that 'no enzyme' was specified in MaxQuant, neither scores nor plots are shown.

        Created with MultiQC

        Modifications Per Raw File

        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 Per Raw File

        MS/MS identification rate per raw file.
        MS/MS identification rate per raw file (quantms data from mzTab and mzML files; MaxQuant data from summary.txt)
        Created with MultiQC

        Search Engine Scores

        Summary of Andromeda Scores

        This statistic is extracted from msms.txt. Andromeda score for the best associated MS/MS spectrum.
        Created with MultiQC

        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).
        Created with MultiQC

        Potential Contaminants Per File

        Potential contaminants per group from proteinGroups.txt.
        External protein contamination should be controlled for, therefore MaxQuant ships with a comprehensive, yet customizable protein contamination database, which is searched by MaxQuant by default. A contamination plot derived from the proteinGroups (PG) table, showing the fraction of total protein intensity attributable to contaminants. Note that this plot is based on experimental groups, and therefore may not correspond 1:1 to Raw files.
        Created with MultiQC

        Quantification Analysis

        Protein Intensity Distribution

        Protein intensity boxplots by experimental groups. Groups are user-defined during MaxQuant configuration. This plot displays a (customizable) threshold line for the desired mean intensity of proteins. Groups which underperform here, are likely to also suffer from a worse MS/MS id rate and higher contamination due to the lack of total protein loaded/detected. If possible, all groups should show a high and consistent amount of total protein. The height of the bar correlates to the number of proteins with non-zero abundance.
        Created with MultiQC

        LFQ Intensity Distribution

        Label-free quantification (LFQ) intensity boxplots by experimental groups.
        Label-free quantification (LFQ) intensity boxplots by experimental groups. Groups are user-defined during MaxQuant configuration. This plot displays a (customizable) threshold line for the desired mean of LFQ intensity of proteins. Raw files which underperform in *Raw* intensity, are likely to show an *increased* mean here, since only high-abundance proteins are recovered and quantifyable by MaxQuant in this Raw file. The remaining proteins are likely to receive an LFQ value of 0 (i.e. do not contribute to the distribution). The height of the bar correlates to the number of proteins with non-zero abundance.
        Created with MultiQC

        Peptide Intensity Distribution

        Peptide precursor intensity per Raw file from evidence.txt WITHOUT match-between-runs evidence.
        Peptide precursor intensity per Raw file from evidence.txt WITHOUT match-between-runs evidence. Low peptide intensity usually goes hand in hand with low MS/MS identifcation rates and unfavourable signal/noise ratios, which makes signal detection harder. Also instrument acquisition time increases for trapping instruments. Failing to reach the intensity threshold is usually due to unfavorable column conditions, inadequate column loading or ionization issues. If the study is not a dilution series or pulsed SILAC experiment, we would expect every condition to have about the same median log-intensity.
        Created with MultiQC

        PCA of Raw Intensity

        [Excludes Contaminants] Principal components plots of experimental groups (as defined during MaxQuant configuration).
        This plot is shown only if more than one experimental group was defined. If LFQ was activated in MaxQuant, an additional PCA plot for LFQ intensities is shown. Similarly, if iTRAQ/TMT reporter intensities are detected. Since experimental groups and Raw files do not necessarily correspond 1:1, this plot may not reflect individual raw file performance.
        Created with MultiQC

        PCA of LFQ Intensity

        [Excludes Contaminants] Principal components plots of experimental groups (as defined during MaxQuant configuration).
        This plot is shown only if more than one experimental group was defined. If LFQ was activated in MaxQuant, an additional PCA plot for LFQ intensities is shown. Similarly, if iTRAQ/TMT reporter intensities are detected. Since experimental groups and Raw files do not necessarily correspond 1:1, this plot may not reflect individual raw file performance.
        Created with MultiQC

        Peptides Quantification Table

        This plot shows the quantification information of peptides in evidence.txt.
        The table shows the quantitative level and distribution of peptides in different study variables, run and peptidoforms. The distribution shows all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptidoforms. Contaminants have been removed from the data by filtering using the 'Potential contaminant' field. * BestSearchScore: maximum score (Andromeda score). * Average Intensity: Average intensity of each peptide sequence (0 or NA ignored).
        Showing 50/50 rows and 4/4 columns.
        PeptideIDProtein NamePeptide SequenceBest Search ScoreAverage Intensity
        1
        Q86U42-2;Q86U42
        AAAAAAAAAAGAAGGR
        128.6800
        7.1389
        2
        P37108
        AAAAAAAAAPAAAATAPTTAATTAATAAQ
        82.8310
        7.1284
        3
        P36578
        AAAAAAALQAK
        135.4300
        7.8269
        4
        Q9H3H3-2;Q9H3H3-3
        AAAAAAAVAGVGR
        108.3100
        6.4210
        5
        Q96P70
        AAAAAAGAASGLPGPVAQGLK
        85.9370
        6.6024
        6
        P28482
        AAAAAAGAGPEMVR
        94.1220
        6.4680
        7
        Q8WVM8
        AAAAAATAAAAASIR
        146.5900
        6.6441
        8
        Q86X55-1;Q86X55;Q86X55-2
        AAAAAAVGPGAGGAGSAVPGGAGPCATVSVFPGAR
        59.5360
        6.6710
        9
        O00410
        AAAAAEQQQFYLLLGNLLSPDNVVR
        99.1100
        6.7391
        10
        O00410
        AAAAAEQQQFYLLLGNLLSPDNVVRK
        74.7030
        6.7286
        11
        P86791;P86790
        AAAAAGAGSGPWAAQEK
        82.9420
        6.2996
        12
        Q9Y2Z0-2;Q9Y2Z0
        AAAAAGTATSQR
        124.4200
        7.5048
        13
        Q7L5D6
        AAAAAMAEQESAR
        117.0300
        6.0297
        14
        Q13049
        AAAAASHLNLDALR
        88.7060
        6.3835
        15
        Q9P258
        AAAAAWEEPSSGNGTAR
        137.2700
        6.1486
        16
        O43324-2;O43324
        AAAAELSLLEK
        169.5200
        6.9630
        17
        Q96KQ7-2;Q96KQ7;Q96KQ7-3
        AAAAGAAAAAAAEGEAPAEMGALLLEK
        89.4620
        5.9631
        18
        Q00796
        AAAAKPNNLSLVVHGPGDLR
        112.8200
        7.1325
        19
        P55036
        AAAASAAEAGIATTGTEDSDDALLK
        54.0470
        6.4029
        20
        Q15005
        AAAAVQGGR
        127.4600
        6.8615
        21
        Q8N1G4
        AAAAVSESWPELELAER
        104.2100
        6.1032
        22
        O00231;O00231-2
        AAAAVVEFQR
        105.9800
        6.9886
        23
        Q8NI27
        AAAAVVVPAEWIK
        59.2270
        6.5836
        24
        P30153
        AAADGDDSLYPIAVLIDELR
        85.2030
        6.1322
        25
        Q9UNF1-2;Q9UNF1
        AAAEAAAEAK
        91.7010
        5.2648
        26
        Q9NQP4
        AAAEDVNVTFEDQQK
        147.7500
        5.9965
        27
        P55263;P55263-3
        AAAEEEPKPK
        86.8980
        6.2054
        28
        Q99567
        AAAEGPVGDGELWQTWLPNHVVFLR
        80.2360
        6.2906
        29
        Q13523
        AAAETQSLR
        76.1700
        6.4857
        30
        P02786
        AAAEVAGQFVIK
        117.2000
        6.6548
        31
        Q9Y490
        AAAFEEQENETVVVK
        130.4700
        6.2308
        32
        O94826
        AAAFEQLQK
        78.5560
        6.9091
        33
        P35221;P35221-2
        AAAGEFADDPCSSVK
        120.2100
        6.7622
        34
        Q96C19
        AAAGELQEDSGLCVLAR
        170.4700
        6.5967
        35
        Q9UL25
        AAAGGGGGGAAAAGR
        182.7100
        6.7549
        36
        Q96T51;Q96T51-3
        AAAGLGGGDSGDGTAR
        108.6600
        6.6020
        37
        P51970
        AAAHHYGAQCDKPNK
        44.4360
        5.2035
        38
        P08107;P08107-2
        AAAIGIDLGTTYSCVGVFQHGK
        60.0620
        6.7559
        39
        Q14008-2;Q14008;Q14008-3
        AAALATVNAWAEQTGMK
        172.4200
        6.1280
        40
        P31948
        AAALEFLNR
        82.2870
        6.3951
        41
        P31948
        AAALEFLNRFEEAK
        137.8100
        7.3349
        42
        O60716-13;O60716-11;O60716-10;O60716-9;O60716-21;O60716-19;O60716-16;O60716-18;O60716-15;O60716-17;O60716-14;O60716-12;O60716-5;O60716-3;O60716-2;O60716;O60716-24;O60716-23;O60716-22;O60716-20;O60716-8;O60716-7;O60716-6;O60716-4;O60716-29;O60716-27;O60716-26;O60716-25;O60716-32;O60716-31;O60716-30;O60716-28
        AAALVLQTIWGYK
        145.8100
        6.3166
        43
        Q14498-2;Q14498;Q14498-3
        AAAMANNLQK
        73.2330
        5.4044
        44
        P62877
        AAAMDVDTPSGTNSGAGK
        168.4200
        5.9619
        45
        P62877
        AAAMDVDTPSGTNSGAGKK
        186.7200
        6.6983
        46
        Q9NX63
        AAANEQLTR
        107.4500
        6.4553
        47
        P26641
        AAAPAPEEEMDECEQALAAEPK
        131.5600
        6.6380
        48
        P20810-7;P20810-6;P20810-5;P20810-4;P20810-8;P20810;P20810-2;P20810-3
        AAAPAPVSEAVCR
        90.7930
        7.3560
        49
        Q12765
        AAAPPSYCFVAFPPR
        56.5690
        5.7088
        50
        P53618
        AAAQCYIDLIIK
        77.6440
        6.8017
        Expand table

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (evidence.txt).
        The quantification information of peptides is obtained from evidence.txt. The table shows the quantitative level and distribution of peptides in different study variables, run and peptidoforms. The distribution shows all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptidoforms. Contaminants have been removed from the data by filtering using the 'Potential contaminant' field. * Peptides_Number: The number of peptides for each protein. * Average Intensity: Average intensity of each protein (0 or NA ignored).
        Showing 50/50 rows and 3/3 columns.
        ProteinIDProtein NameNumber of PeptidesAverage Intensity
        1
        A0AV96-2;A0AV96
        2
        5.8834
        2
        A0AVT1
        1
        6.9224
        3
        A0AVT1;A0AVT1-2
        19
        6.4333
        4
        A0AVT1;A0AVT1-3
        1
        6.0380
        5
        A0AVT1;A0AVT1-3;A0AVT1-4
        6
        6.3704
        6
        A0FGR8-2
        1
        5.4654
        7
        A0FGR8-2;A0FGR8;A0FGR8-6
        1
        5.5375
        8
        A0FGR8-2;A0FGR8;A0FGR8-6;A0FGR8-4
        6
        6.5283
        9
        A0FGR8-2;A0FGR8;A0FGR8-6;A0FGR8-4;A0FGR8-5
        1
        6.6685
        10
        A0FGR8-2;A0FGR8;A0FGR8-6;A0FGR8-5
        4
        6.4869
        11
        A0MZ66-2;A0MZ66-8;A0MZ66-4;A0MZ66-5;A0MZ66-6;A0MZ66;A0MZ66-3
        2
        6.2240
        12
        A1L0T0
        12
        6.3070
        13
        A2RRP1-2;A2RRP1
        3
        5.7738
        14
        A4D1E9
        1
        5.7467
        15
        A4D1E9;A4D1E9-2
        1
        6.2192
        16
        A4UGR9-2;A4UGR9-3;A4UGR9
        1
        6.4718
        17
        A5D8W1-2;A5D8W1-5;A5D8W1
        1
        6.5837
        18
        A5D8W1-2;A5D8W1-5;A5D8W1;A5D8W1-4;A5D8W1-3
        1
        6.5649
        19
        A5YKK6-2;A5YKK6
        1
        6.1066
        20
        A5YKK6-2;A5YKK6;A5YKK6-3
        4
        5.8559
        21
        A5YKK6-2;A5YKK6;A5YKK6-3;A5YKK6-4
        4
        6.0792
        22
        A6NDG6
        4
        6.1718
        23
        A6NDU8
        1
        6.1976
        24
        A6NFZ4
        1
        5.3669
        25
        A6NHL2-2;A6NHL2
        1
        6.6717
        26
        A6NHQ2
        1
        6.9258
        27
        A6NHR9
        1
        6.3482
        28
        A6NHR9;A6NHR9-2
        9
        6.1953
        29
        A6NHR9;A6NHR9-2;A6NHR9-3
        6
        6.3258
        30
        A6NKF9;B7ZAQ6-2;B7ZAQ6-3;P0CG08;B7ZAQ6
        1
        5.9271
        31
        A6NKT7;Q7Z3J3
        1
        5.9068
        32
        A6NKT7;Q7Z3J3;P0DJD0;P0DJD1;Q8IWJ2
        1
        5.6636
        33
        A8MT69
        1
        5.4836
        34
        A8MXV4
        3
        6.2205
        35
        C9JLW8
        1
        6.1450
        36
        CONTAMINANT_sp|ANXA5_HUMAN|;P08758
        25
        7.4325
        37
        CONTAMINANT_sp|B2MG_HUMAN|;P61769
        3
        7.6794
        38
        CONTAMINANT_sp|BID_HUMAN|;P55957;P55957-2
        1
        5.7736
        39
        CONTAMINANT_sp|BID_HUMAN|;P55957;P55957-2;P55957-3
        1
        6.5969
        40
        CONTAMINANT_sp|BID_HUMAN|;P55957;P55957-2;P55957-4
        4
        6.2732
        41
        CONTAMINANT_sp|CATA_HUMAN|;P04040
        13
        6.7989
        42
        CONTAMINANT_sp|CATD_HUMAN|;P07339
        21
        7.2581
        43
        CONTAMINANT_sp|CYC_HUMAN|;P99999
        5
        6.9857
        44
        CONTAMINANT_sp|CYC_HUMAN|;P99999;CONTAMINANT_sp|CYC_HORSE|;CON__P62894
        2
        7.0820
        45
        CONTAMINANT_sp|NEDD8_HUMAN|;Q15843
        2
        6.6189
        46
        CONTAMINANT_sp|NQO2_HUMAN|;P16083
        2
        6.4140
        47
        CONTAMINANT_sp|PRDX1_HUMAN|;Q06830
        12
        7.8054
        48
        CONTAMINANT_sp|PRDX1_HUMAN|;Q06830;P32119
        1
        8.0652
        49
        CONTAMINANT_sp|PRDX1_HUMAN|;Q06830;Q13162
        2
        7.8547
        50
        CONTAMINANT_sp|RS27A_HUMAN|;P62979
        2
        7.2087
        Expand table

        MS2 and Spectral Stats

        Charge-state of Per File

        The distribution of the charge-state of the precursor ion, excluding potential contaminants.
        Charge distribution per Raw file. 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.

        This plot ignores charge states of contaminants.

        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.

        If DIA-Data: this metric is skipped.

        Created with MultiQC

        RT Quality Control

        IDs over RT

        Distribution of retention time, derived from the evidence table.
        The uncalibrated retention time in minutes in the elution profile of the precursor ion, and does not include potential contaminants.

        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

        Peak width over RT

        Distribution of widths of peptide elution peaks, derived from the evidence table.
        The distribution of the widths of peptide elution peaks, derived from the evidence table and excluding potential contaminants, is one parameter of optimal and reproducible chromatographic separation. Ideally, all Raw files show a similar distribution, e.g. to allow for equal conditions during dynamic precursor exclusion, RT alignment or peptide quantification.
        Created with MultiQC

        Ion Injection Time over RT

        Ion injection time score - should be as low as possible to allow fast cycles. Correlated with peptide intensity. Note that this threshold needs customization depending on the instrument used (e.g., ITMS vs. FTMS).
        Created with MultiQC

        TopN over RT

        TopN over retention time.
        TopN over retention time. Similar to ID over RT, this metric reflects the complexity of the sample at any point in time. Ideally complexity should be made roughly equal (constant) by choosing a proper (non-linear) LC gradient. See [Moruz 2014, DOI: 10.1002/pmic.201400036](https://pubmed.ncbi.nlm.nih.gov/24700534/) for details.
        Created with MultiQC

        TopN

        This metric somewhat summarizes "TopN over RT"
        Reaching TopN on a regular basis indicates that all sections of the LC gradient deliver a sufficient number of peptides to keep the instrument busy. This metric somewhat summarizes "TopN over RT".
        Created with MultiQC

        MultiQC v1.32 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap and FileSaver.js.