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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 2025-03-17, 14:02 based on data in:

        General Statistics

        Showing 71/71 rows and 5/6 columns.
        Sample NameM Reads Mapped% AssignedM Assigned% AlignedM Aligned
        G192_M01_G192_M01
        80.4%
        16.7
        G192_M01_sorted
        93.9%
        15.7
        G192_M01_statistics_for_all_accepted_reads
        40.3
        G192_M01_statistics_for_primary_reads
        36.2
        G192_M01_statistics_for_primary_unique_reads
        33.5
        G192_M02_G192_M02
        83.7%
        18.3
        G192_M02_sorted
        94.2%
        17.2
        G192_M02_statistics_for_all_accepted_reads
        43.5
        G192_M02_statistics_for_primary_reads
        39.4
        G192_M02_statistics_for_primary_unique_reads
        36.6
        G192_M03_G192_M03
        79.7%
        21.8
        G192_M03_sorted
        94.8%
        20.7
        G192_M03_statistics_for_all_accepted_reads
        52.4
        G192_M03_statistics_for_primary_reads
        47.0
        G192_M03_statistics_for_primary_unique_reads
        43.6
        G192_M04_G192_M04
        83.9%
        22.1
        G192_M04_sorted
        94.3%
        20.8
        G192_M04_statistics_for_all_accepted_reads
        52.2
        G192_M04_statistics_for_primary_reads
        47.4
        G192_M04_statistics_for_primary_unique_reads
        44.2
        G192_M05_G192_M05
        81.3%
        20.3
        G192_M05_sorted
        93.4%
        18.9
        G192_M05_statistics_for_all_accepted_reads
        48.7
        G192_M05_statistics_for_primary_reads
        43.8
        G192_M05_statistics_for_primary_unique_reads
        40.5
        G192_M06_G192_M06
        85.6%
        20.0
        G192_M06_sorted
        95.3%
        19.0
        G192_M06_statistics_for_all_accepted_reads
        46.2
        G192_M06_statistics_for_primary_reads
        42.5
        G192_M06_statistics_for_primary_unique_reads
        40.0
        G192_M07_G192_M07
        85.2%
        24.1
        G192_M07_sorted
        94.8%
        22.9
        G192_M07_statistics_for_all_accepted_reads
        56.7
        G192_M07_statistics_for_primary_reads
        51.6
        G192_M07_statistics_for_primary_unique_reads
        48.2
        G192_M08_G192_M08
        84.3%
        21.6
        G192_M08_sorted
        92.6%
        20.0
        G192_M08_statistics_for_all_accepted_reads
        52.2
        G192_M08_statistics_for_primary_reads
        46.9
        G192_M08_statistics_for_primary_unique_reads
        43.1
        G192_M09_G192_M09
        91.9%
        21.6
        G192_M09_sorted
        96.0%
        20.7
        G192_M09_statistics_for_all_accepted_reads
        48.0
        G192_M09_statistics_for_primary_reads
        45.2
        G192_M09_statistics_for_primary_unique_reads
        43.2
        G192_M10_sorted
        96.5%
        24.6
        G192_M11_sorted
        95.7%
        19.6
        G192_M12_sorted
        96.5%
        20.8
        G192_M13_sorted
        96.1%
        19.8
        G192_M14_sorted
        95.3%
        19.0
        G192_M15_sorted
        95.3%
        18.7
        G192_M16_sorted
        95.2%
        18.3
        G192_M17_sorted
        95.6%
        19.9
        G192_M18_sorted
        94.9%
        19.0
        G192_M19_sorted
        94.8%
        22.9
        G192_M20_sorted
        93.1%
        16.9
        G192_M21_sorted
        95.1%
        21.8
        G192_M22_sorted
        95.1%
        18.9
        G192_M23_sorted
        95.1%
        20.0
        G192_M24_sorted
        94.8%
        20.1
        G192_M25_sorted
        95.1%
        20.8
        G192_M26_sorted
        95.4%
        22.8
        G192_M27_sorted
        94.8%
        17.7
        G192_M28_sorted
        95.1%
        19.5
        G192_M29_sorted
        95.4%
        19.8
        G192_M30_sorted
        94.4%
        18.4
        G192_M31_sorted
        95.4%
        23.1
        G192_M32_sorted
        93.7%
        18.1
        G192_M33_sorted
        94.7%
        22.4
        G192_M34_sorted
        94.9%
        22.6
        statistics_for_primary_unique_reads
        51.0

        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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        STAR

        STAR is an ultrafast universal RNA-seq aligner.

        Alignment Scores

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        Gene Counts

        Statistics from results generated using --quantMode GeneCounts. The three tabs show counts for unstranded RNA-seq, counts for the 1st read strand aligned with RNA and counts for the 2nd read strand aligned with RNA.

           
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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.