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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 2026-08-19, 08:06 based on data in:

        General Statistics

        Showing 54/54 rows and 3/4 columns.
        Sample NameM Reads Mapped% AssignedM Assigned
        G262_M01_sorted
        93.8%
        10.5
        G262_M02_sorted
        93.4%
        13.7
        G262_M03_sorted
        93.5%
        14.4
        G262_M04_sorted
        94.7%
        10.2
        G262_M05_sorted
        93.6%
        17.5
        G262_M06_sorted
        93.3%
        17.8
        G262_M07_sorted
        95.6%
        15.5
        G262_M08_sorted
        93.1%
        17.0
        G262_M09_sorted
        92.3%
        15.9
        G262_M10_sorted
        90.6%
        11.2
        G262_M11_sorted
        93.0%
        18.0
        G262_M12_sorted
        93.2%
        18.8
        G262_M13_sorted
        94.7%
        13.6
        G262_M16_sorted
        91.9%
        15.7
        G262_M17_sorted
        92.6%
        15.2
        G262_M18_sorted
        88.6%
        15.1
        G262_M19_sorted
        93.5%
        17.2
        G262_M20_sorted
        88.9%
        16.2
        G262_M21_sorted
        92.8%
        19.0
        G262_M22_sorted
        94.0%
        18.3
        G262_M23_sorted
        89.5%
        16.2
        G262_M25_sorted
        92.9%
        13.2
        G262_M26_sorted
        93.4%
        14.1
        G262_M27_sorted
        90.1%
        21.2
        G262_M29_sorted
        92.3%
        9.6
        G262_M30_sorted
        91.3%
        19.0
        G262_M31_sorted
        94.8%
        18.7
        G262_M32_sorted
        95.0%
        18.1
        G262_M33_sorted
        93.1%
        19.2
        G262_M34_sorted
        94.8%
        16.7
        G262_M35_sorted
        94.4%
        15.3
        G262_M36_sorted
        93.3%
        16.3
        G262_M37_sorted
        93.8%
        13.0
        G262_M39_sorted
        92.5%
        18.5
        G262_M40_sorted
        90.1%
        19.6
        G262_M41_sorted
        91.6%
        17.6
        G262_M42_sorted
        92.0%
        21.4
        G262_M43_sorted
        90.9%
        17.7
        G262_M44_sorted
        90.8%
        17.4
        G262_M45_sorted
        91.1%
        20.0
        G262_M46_sorted
        94.2%
        22.9
        G262_M47_sorted
        94.8%
        19.3
        G262_M48_sorted
        93.8%
        20.9
        G262_M50_sorted
        92.0%
        11.9
        G262_M51_sorted
        92.5%
        13.8
        G262_M52_sorted
        92.3%
        17.2
        G262_M53_sorted
        92.0%
        18.5
        G262_M54_sorted
        91.4%
        20.8
        G262_M55_sorted
        93.1%
        15.9
        G262_M56_sorted
        94.0%
        14.0
        G262_M57_sorted
        93.4%
        9.0
        G262_M58_sorted
        92.0%
        19.8
        G262_M59_sorted
        91.9%
        17.9
        statistics_for_primary_unique_reads
        36.5

        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.