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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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        About MultiQC

        This report was generated using MultiQC, version 1.11

        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

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

        Report generated on 2021-07-06, 09:01 based on data in:


        General Statistics

        Showing 8/8 rows and 10/12 columns.
        Sample Name% Reads assigned% Unclassifiedmillions Reads assigned% Muribaculum gordoncarteri% Top 5 Species% Unclassified% DuplicationGC content% PF% Adapter
        Sample13
        39.5%
        24.1%
        0.4
        0.8%
        3.5%
        81.7%
        6.0%
        48.1%
        94.8%
        9.1%
        Sample22
        33.0%
        28.3%
        0.2
        1.5%
        7.1%
        73.9%
        6.5%
        47.1%
        89.4%
        17.2%
        Sample25
        38.6%
        23.9%
        0.4
        1.3%
        4.6%
        80.1%
        6.8%
        48.2%
        94.0%
        11.8%
        Sample3
        35.8%
        22.5%
        0.3
        3.3%
        7.8%
        76.7%
        6.2%
        48.5%
        92.6%
        14.6%
        Sample30
        37.1%
        20.4%
        0.3
        5.5%
        11.9%
        70.1%
        6.6%
        49.3%
        93.1%
        12.8%
        Sample31
        40.2%
        24.0%
        0.4
        1.4%
        3.9%
        81.6%
        8.1%
        47.9%
        91.7%
        16.1%
        Sample4
        39.5%
        26.8%
        0.4
        1.0%
        3.9%
        82.1%
        7.3%
        46.9%
        92.2%
        14.2%
        Sample6
        35.8%
        24.9%
        0.3
        2.6%
        7.8%
        76.2%
        7.2%
        48.2%
        90.0%
        18.1%

        Kaiju

        Kaiju a fast and sensitive taxonomic classification for metagenomics.

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top five taxa are then plotted for each of the taxa ranks found in logs. The unclassified count is always shown across all taxa ranks. The 'Cannot be assigned' count correspond to reads classified but not at this taxa rank.

        The category "Other" shows the difference between the above total assingned read count and the sum of the read counts in the top 5 taxa shown. This should cover all taxa not in the top 5.

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        Kraken

        Kraken is a taxonomic classification tool that uses exact k-mer matches to find the lowest common ancestor (LCA) of a given sequence.

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top five taxa are then plotted for each of the 9 different taxa ranks. The unclassified count is always shown across all taxa ranks.

        The total number of reads is approximated by dividing the number of unclassified reads by the percentage of the library that they account for. Note that this is only an approximation, and that kraken percentages don't always add to exactly 100%.

        The category "Other" shows the difference between the above total read count and the sum of the read counts in the top 5 taxa shown + unclassified. This should cover all taxa not in the top 5, +/- any rounding errors.

        Note that any taxon that does not exactly fit a taxon rank (eg. - or G2) is ignored.

           
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        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...)

        Filtered Reads

        Filtering statistics of sampled reads.

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        Duplication Rates

        Duplication rates of sampled reads.

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        Insert Sizes

        Insert size estimation of sampled reads.

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        Sequence Quality

        Average sequencing quality over each base of all reads.

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        GC Content

        Average GC content over each base of all reads.

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        N content

        Average N content over each base of all reads.

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