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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 2024-03-11, 11:15 based on data in:

        General Statistics

        Showing 13/13 rows and 3/4 columns.
        Sample NameM Reads Mapped% AssignedM Assigned
        G221_M69_sorted
        93.3%
        5.8
        G221_M70_sorted
        93.9%
        6.7
        G221_M71_sorted
        93.6%
        7.4
        G221_M72_sorted
        94.7%
        5.7
        G221_M73_sorted
        93.8%
        17.4
        G221_M74_sorted
        92.8%
        11.8
        G221_M75_sorted
        93.8%
        17.2
        G221_M76_sorted
        93.5%
        6.7
        G221_M77_sorted
        94.3%
        6.4
        G221_M78_sorted
        94.3%
        7.1
        G221_M79_sorted
        94.4%
        6.7
        G221_M80_sorted
        94.7%
        6.7
        statistics_for_primary_unique_reads
        14.1

        RSeQC

        RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.

        Infer experiment

        Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

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        featureCounts

        Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.

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        Samtools

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        Samtools Flagstat

        This module parses the output from samtools flagstat. All numbers in millions.

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        MultiQC v1.11 - Written by Phil Ewels, available on GitHub.

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