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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in sample_report_multiqc_report_data when this report was generated.


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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 the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.14

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        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

        MGscan 16S/18S/ITS Sequencing report for sample_report

        This report has been generated using MGscan 16S/18S/ITS Sequencing Pipeline. For information about how to interpret these results, please see the report documentation.


        General Statistics

        Showing 68/68 rows and 8/13 columns.
        Sample NameNo. Seqs% GC% Passing trimming filterDADA2 inputDADA2 qual filterDADA2 merge pairsDADA2 non-chimeraDADA2 filter tax
        SRR12553769
        63847
        52%
        98.2%
        62674
        42349
        30204
        19879
        19833
        SRR12553770
        48613
        52%
        97.9%
        47570
        32190
        27674
        17892
        17885
        SRR12553771
        68933
        51%
        98.4%
        67848
        46281
        35959
        18626
        18488
        SRR12553772
        64869
        51%
        98.6%
        63929
        43967
        37744
        22905
        22846
        SRR12553774
        71221
        52%
        98.2%
        69933
        47410
        35994
        24361
        24290
        SRR12553775
        82016
        52%
        98.3%
        80613
        54173
        43086
        26161
        26037
        SRR12553776
        91888
        53%
        98.2%
        90213
        59124
        50469
        36296
        36255
        SRR12553777
        65809
        52%
        98.2%
        64601
        43744
        34580
        20724
        20671
        SRR12553778
        66139
        53%
        98.0%
        64808
        43273
        37513
        20718
        20691
        SRR12553779
        61664
        53%
        98.1%
        60481
        40511
        28357
        19743
        19665
        SRR12553780
        82473
        52%
        97.9%
        80775
        52543
        43313
        26920
        26874
        SRR12553781
        98082
        50%
        98.1%
        96236
        65821
        54413
        23799
        23646
        SRR12553782
        55675
        51%
        98.1%
        54605
        37079
        31645
        16801
        16730
        SRR12553783
        74949
        53%
        98.2%
        73610
        48219
        44727
        31594
        31529
        SRR12553786
        66647
        51%
        98.2%
        65451
        44839
        36114
        23730
        23648
        SRR12553787
        103525
        52%
        98.4%
        101841
        66923
        49914
        33688
        33570
        SRR12553788
        71846
        51%
        98.3%
        70601
        48344
        43565
        31037
        31013
        SRR12553789
        53725
        51%
        98.4%
        52876
        36270
        29394
        19951
        19909
        SRR12553790
        64434
        54%
        98.3%
        63307
        42848
        39957
        33295
        33276
        SRR12553791
        95189
        52%
        98.1%
        93420
        61335
        50608
        33927
        33873
        SRR12553792
        74062
        52%
        98.0%
        72567
        49213
        46750
        40297
        40272
        SRR12553793
        59321
        53%
        98.1%
        58182
        38907
        31774
        21322
        21254
        SRR12553794
        60351
        52%
        97.2%
        58652
        38350
        36488
        23030
        23029
        SRR12553795
        35199
        52%
        96.6%
        33995
        22114
        18155
        11971
        11925
        SRR12553797
        48219
        51%
        96.8%
        46663
        30873
        28945
        20358
        20345
        SRR12553798
        47678
        53%
        97.3%
        46410
        30363
        28196
        24001
        23994
        SRR12553799
        52980
        50%
        96.9%
        51342
        34084
        31547
        21078
        21035
        SRR12553800
        54814
        50%
        97.2%
        53275
        35332
        31901
        24416
        24395
        SRR12553801
        64026
        53%
        97.6%
        62459
        41515
        36579
        24545
        24482
        SRR12553802
        49108
        53%
        96.8%
        47547
        30579
        28305
        18213
        18182
        SRR12553803
        53327
        53%
        97.2%
        51830
        34052
        30681
        20849
        20822
        SRR12553804
        62664
        52%
        96.9%
        60724
        40082
        38698
        30043
        30034
        SRR12553805
        72922
        55%
        97.0%
        70728
        46589
        44642
        37997
        37957
        SRR12553806
        57045
        54%
        96.6%
        55106
        35877
        32310
        21244
        21218
        SRR12553808
        46516
        50%
        97.1%
        45179
        30088
        29011
        25754
        25744
        SRR12553809
        58109
        53%
        96.8%
        56231
        36293
        32542
        25847
        25827
        SRR12553810
        55326
        52%
        97.3%
        53820
        35208
        32354
        24626
        24617
        SRR12553811
        58691
        55%
        97.1%
        56963
        37125
        34921
        29753
        29747
        SRR12553812
        59860
        51%
        96.9%
        57985
        37878
        31938
        17830
        17770
        SRR12553813
        64811
        54%
        97.2%
        63001
        41191
        38122
        29627
        29614
        SRR12553814
        58884
        54%
        97.3%
        57309
        37895
        35655
        27280
        27266
        SRR12553815
        31451
        55%
        95.6%
        30060
        16736
        13631
        12019
        12007
        SRR12553816
        67822
        54%
        96.3%
        65294
        40970
        38016
        29195
        29180
        SRR12553817
        42648
        54%
        96.9%
        41346
        26855
        24444
        16260
        16247
        SRR12553819
        40557
        52%
        97.1%
        39374
        25934
        18976
        14641
        14586
        SRR12553820
        44295
        53%
        96.8%
        42895
        28298
        26346
        17768
        17743
        SRR12553821
        62258
        53%
        97.3%
        60591
        39956
        39008
        36648
        36632
        SRR12553822
        60167
        53%
        96.1%
        57840
        36819
        35510
        29340
        29336
        SRR12553823
        40622
        53%
        96.7%
        39299
        25788
        25348
        24101
        24101
        SRR12553824
        43409
        51%
        97.2%
        42194
        28149
        27138
        24480
        24461
        SRR12553825
        62080
        52%
        97.0%
        60231
        39272
        38594
        33483
        33478
        SRR12553826
        58028
        54%
        96.8%
        56179
        36470
        33164
        25382
        25374
        SRR12553827
        75999
        52%
        97.2%
        73834
        48441
        47998
        43858
        43817
        SRR12553828
        51858
        52%
        97.4%
        50514
        33210
        32700
        29119
        29094
        SRR12553830
        60439
        53%
        97.4%
        58839
        37966
        32940
        20480
        20444
        SRR12553831
        48496
        51%
        96.6%
        46863
        30972
        26754
        21952
        21919
        SRR12553832
        66639
        53%
        97.0%
        64608
        42103
        33578
        28147
        28069
        SRR12553833
        20502
        53%
        96.2%
        19718
        12818
        12052
        11377
        11368
        SRR12553834
        55527
        52%
        97.1%
        53891
        35862
        33433
        25036
        25020
        SRR12553835
        49991
        55%
        96.3%
        48139
        30767
        29813
        27919
        27893
        SRR12553836
        48042
        53%
        97.1%
        46656
        30449
        28985
        25268
        25248
        SRR12553837
        53988
        53%
        96.9%
        52300
        34700
        29586
        18835
        18798
        SRR12553838
        59306
        54%
        97.5%
        57797
        38571
        36644
        29214
        29195
        SRR12553839
        46969
        54%
        96.2%
        45201
        29297
        28409
        26686
        26684
        SRR12553841
        51148
        53%
        97.0%
        49597
        32794
        31985
        26285
        26266
        SRR12553842
        66267
        53%
        97.0%
        64274
        42303
        39566
        32555
        32535
        SRR12553843
        49032
        52%
        96.3%
        47199
        30366
        30023
        29136
        29136
        SRR12553844
        58287
        54%
        96.7%
        56361
        36588
        32476
        24040
        24001

        Composition Barplots

        Taxa composition plots illustrate the microbial composition at different taxonomy levels from Kingdom to Species. The interactive figure below shows the microbial composition at species level by default. Lower abundance taxa can be grouped into an 'Others' category to simplify visualization.
        Additional composition barplots can be accessed by clicking the buttons above the plot and abundance tables can be downloaded using the Toolbox.

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        Taxonomy Abundance Heatmap

        The taxonomy abundance heatmap is a plot of the relative abundance of taxa (ranging from 0-1) for each sample and is a quick way to identify patterns of microbial distribution amongst samples. Each row represents the relative abundance of each taxon, with the taxonomy ID shown on the right. Each column represents a sample, with the sample ID shown at the bottom. Each box, or tile, on the heatmap represents relative abundance, colored according to the key scale on the far right of the plot.
        Hierarchical clustering is performed on samples and taxa based on the Euclidean distance of log-transformed relative abundance. More similar samples and taxa are closer to each other. The heatmaps are made using the top 20 most abundant taxa of each sample group. Heatmaps are only made when there are >= 5 taxa at a taxonomic rank.
        Please note that long taxa names (right of plot) may be partially obscured, however full details can be observed by hovering the mouse cursor over each tile.
        Heatmaps at different taxonomic ranks can be accessed by clicking the buttons above the plot. By default, the plot shows relative abundance values, however results can be displayed as log-transformed and normalized. Relative abundances are log10 transformed and centered on each row. This view can be activated by selecting 'Log_normalized' from the drop down-list that appears when clicking the taxonomic rank buttons at the top of the plot.

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        Diversity Analysis

        Alpha Diversity

        Alpha diversity is a measurement of species diversity within a community. For analyses with group comparison, a box-and-whisker plot of the alpha diversity of each group is shown. Alpha diversity plots generated by other metrics can be found by clicking on the dropdown menu.
        Observed features is simply the number of taxa present in the community. Shannon diversity index takes into account the number of taxa and their relative abundance. Faith’s Phylogenetic Diversity (Faith PD) is a measure of community richness that incorporates phylogenetic relationships between the taxa. Pielou's Evenness is another measure which considers how evenly taxa are distributed in a community, derived from the Shannon index.


        Alpha Rarefaction

        This section shows an interactive alpha rarefaction plot, which is the alpha diversity (species richness) as a function of the sampling depth (sequencing read counts). Rarefaction is a technique to correct for uneven sampling when performing diversity analyses. Rarefaction curves can help to identify if sampling depth is sufficient to capture the true diversity of the community. Rarefaction curves are generated by randomly subsampling sequencing reads over a range of values and determining the alpha diversity at each read depth (as an average of 5 iterations).
        Curves computed by different metrics can be found by clicking on the dropdown menu to the left of the plot.


        Beta Diversity

        Beta diversity is a measurement of species diversity differences between communities. The figure below is an interactive, 3D principal coordinate analysis (PCoA) plot created from a matrix of pairwise distances between samples calculated by the Bray-Curtis, Jaccard, Weighted Unifrac and Unweighted Unifrac metrics. Different plots can be accessed by clicking the dropdown menu to the left of the plot. Each dot on the beta diversity plot represents the whole microbial composition profile of a sample. Samples with a more similar microbial composition profile will cluster closer together, while samples with more dissimilar profiles will appear farther away from each other.


        ANCOM Differential Taxa Diversity Results

        ANCOM-BC is a software designed to find differentially expressed taxa between user specified groups. By default, the first alphabetical group is used as the reference in the below plots. Positive log fold change values (enriched in the legend) indicate that the taxa is expressed more commonly in the subject group as opposed to the reference group. Negative log fold change values (depleted in the legend) indicate that the taxa is expressed more commonly in the reference group as opposed to the subject group. Black lines in the center of each bar depict the standard error range of log fold changes.

        suspected vs healthy Results

        ANCOM-BC differential taxa diversity results for subject group 'suspected' compared to the reference group 'healthy'.


        suspected_recovered vs healthy Results

        ANCOM-BC differential taxa diversity results for subject group 'suspected_recovered' compared to the reference group 'healthy'.


        FastQC

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

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        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

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        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

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        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

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        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

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        Sequence Length Distribution

        All samples have sequences of a single length (301bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

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        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

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        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

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        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

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        Cutadapt

        Cutadapt is a tool to find and remove adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.

        Filtered Reads

        This plot shows the number of single-end (SE) / paired-end (PE) reads removed by Cutadapt.

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        Trimmed Sequence Lengths (5')

        This plot shows the number of reads with certain lengths of adapter trimmed for the 5' end.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Software Versions

        Software versions are collected at run time from the software output. This pipeline is adapted from nf-core ampliseq pipeline v2.2.0.

        cutadapt
        v3.4
        R
        v4.1.1
        dada2
        v1.22.0
        fastqc
        v0.11.9
        KronaTools
        v2.8.1
        qiime2
        v2021.8.0;2023.2.0
        biom
        v2.1.12
        Nextflow
        v23.10.1
        aladdin-ampliseq
        v0.2.0

        Workflow Summary

        Summary of parameters when the pipeline run is initiated.

        revision
        dev
        sequencing_type
        Illumina_paired_end
        min_frequency
        10
        forward_primer
        CCTACGGGNGGCWGCAG[341F]
        reverse_primer
        GACTACHVGGGTATCTAATCC[785R]
        qiime_ref_taxonomy
        silva=138

        Report generated on 2025-06-05, 13:55 PDT.