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        Note that additional data was saved in multiqc_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 1.12

        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 2024-02-19, 14:32 based on data in: /blue/licht/runs/Evans-MDS/NS3526/NS3526-noIgG

        General Statistics

        Showing 32/32 rows and 2/2 columns.
        Sample Name% Dups% Aligned
        32D-Dnmt3a-FLAG-aH3K27Ac-1
        35.2%
        32D-Dnmt3a-FLAG-aH3K27Ac-1.bt2stats
        92.7%
        32D-Dnmt3a-FLAG-aH3K27Ac-2
        35.5%
        32D-Dnmt3a-FLAG-aH3K27Ac-2.bt2stats
        94.0%
        32D-Dnmt3a-FLAG-aH3K27m3-1
        29.6%
        32D-Dnmt3a-FLAG-aH3K27m3-1.bt2stats
        94.1%
        32D-Dnmt3a-FLAG-aH3K27m3-2
        29.6%
        32D-Dnmt3a-FLAG-aH3K27m3-2.bt2stats
        93.0%
        32D-Dnmt3a-FLAG-aH3K36me2-1
        41.1%
        32D-Dnmt3a-FLAG-aH3K36me2-1.bt2stats
        95.1%
        32D-Dnmt3a-FLAG-aH3K36me2-2
        38.9%
        32D-Dnmt3a-FLAG-aH3K36me2-2.bt2stats
        92.7%
        32D-Dnmt3a-FLAG-aH3K36me3-1
        36.9%
        32D-Dnmt3a-FLAG-aH3K36me3-1.bt2stats
        93.0%
        32D-Dnmt3a-FLAG-aH3K36me3-2
        33.9%
        32D-Dnmt3a-FLAG-aH3K36me3-2.bt2stats
        89.9%
        32D-Dnmt3a-FLAG-aH3K4me1-1
        33.2%
        32D-Dnmt3a-FLAG-aH3K4me1-1.bt2stats
        94.5%
        32D-Dnmt3a-FLAG-aH3K4me1-2
        34.0%
        32D-Dnmt3a-FLAG-aH3K4me1-2.bt2stats
        94.1%
        32D-Dnmt3a-FLAG-aH3K4me3-1
        44.6%
        32D-Dnmt3a-FLAG-aH3K4me3-1.bt2stats
        39.7%
        32D-Dnmt3a-FLAG-aH3K4me3-2
        38.3%
        32D-Dnmt3a-FLAG-aH3K4me3-2.bt2stats
        87.5%
        32D-Dnmt3a-FLAG-aIgG
        41.8%
        32D-Dnmt3a-FLAG-aIgG-1.bt2stats
        0.6%
        32D-Dnmt3a-FLAG-aIgG-2.bt2stats
        0.7%
        32D-Dnmt3a-FLAG-aIgG.bt2stats
        89.0%
        32D-Dnmt3a-FLAG-aPolII-S2-P-1
        42.0%
        32D-Dnmt3a-FLAG-aPolII-S2-P-1.bt2stats
        92.5%
        32D-Dnmt3a-FLAG-aPolII-S2-P-2
        40.4%
        32D-Dnmt3a-FLAG-aPolII-S2-P-2.bt2stats
        91.1%

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        loading..

        Bowtie 2 / HiSAT2

        Bowtie 2 and HISAT2 are fast and memory-efficient tools for aligning sequencing reads against a reference genome. Unfortunately both tools have identical log output by default, so it is impossible to distiguish which tool was used. .DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4.

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        loading..

        MultiQC v1.12 - Written by Phil Ewels, available on GitHub.

        This report uses HighCharts, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.