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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 2024-09-10, 14:48 based on data in:

        General Statistics

        Showing 65/65 rows and 5/6 columns.
        Sample NameM Reads Mapped% AssignedM Assigned% AlignedM Aligned
        G177REL15_M10_G177REL15_M10
        78.1%
        41.1
        G177REL15_M10_sorted
        67.9%
        27.9
        G177REL15_M10_statistics_for_all_accepted_reads
        105.6
        G177REL15_M10_statistics_for_primary_reads
        89.3
        G177REL15_M10_statistics_for_primary_unique_reads
        79.7
        G177REL15_M11_G177REL15_M11
        82.1%
        41.7
        G177REL15_M11_sorted
        64.9%
        27.1
        G177REL15_M11_statistics_for_all_accepted_reads
        104.3
        G177REL15_M11_statistics_for_primary_reads
        89.8
        G177REL15_M11_statistics_for_primary_unique_reads
        80.7
        G177REL15_M12_G177REL15_M12
        81.6%
        31.1
        G177REL15_M12_sorted
        66.3%
        20.6
        G177REL15_M12_statistics_for_all_accepted_reads
        78.5
        G177REL15_M12_statistics_for_primary_reads
        67.2
        G177REL15_M12_statistics_for_primary_unique_reads
        60.1
        G177REL15_M13_G177REL15_M13
        78.8%
        26.8
        G177REL15_M13_sorted
        67.1%
        18.0
        G177REL15_M13_statistics_for_all_accepted_reads
        68.9
        G177REL15_M13_statistics_for_primary_reads
        58.2
        G177REL15_M13_statistics_for_primary_unique_reads
        51.8
        G177REL15_M1_G177REL15_M1
        82.7%
        42.1
        G177REL15_M1_sorted
        66.3%
        27.9
        G177REL15_M1_statistics_for_all_accepted_reads
        107.0
        G177REL15_M1_statistics_for_primary_reads
        91.5
        G177REL15_M1_statistics_for_primary_unique_reads
        81.5
        G177REL15_M2_G177REL15_M2
        82.0%
        41.6
        G177REL15_M2_sorted
        65.4%
        27.2
        G177REL15_M2_statistics_for_all_accepted_reads
        106.5
        G177REL15_M2_statistics_for_primary_reads
        90.4
        G177REL15_M2_statistics_for_primary_unique_reads
        80.4
        G177REL15_M3_G177REL15_M3
        80.7%
        30.1
        G177REL15_M3_sorted
        67.7%
        20.4
        G177REL15_M3_statistics_for_all_accepted_reads
        77.1
        G177REL15_M3_statistics_for_primary_reads
        65.5
        G177REL15_M3_statistics_for_primary_unique_reads
        58.3
        G177REL15_M4_G177REL15_M4
        82.0%
        32.4
        G177REL15_M4_sorted
        64.0%
        20.8
        G177REL15_M4_statistics_for_all_accepted_reads
        83.9
        G177REL15_M4_statistics_for_primary_reads
        70.8
        G177REL15_M4_statistics_for_primary_unique_reads
        62.8
        G177REL15_M5_G177REL15_M5
        80.7%
        35.8
        G177REL15_M5_sorted
        64.1%
        22.9
        G177REL15_M5_statistics_for_all_accepted_reads
        92.5
        G177REL15_M5_statistics_for_primary_reads
        78.0
        G177REL15_M5_statistics_for_primary_unique_reads
        69.2
        G177REL15_M6_G177REL15_M6
        82.1%
        34.3
        G177REL15_M6_sorted
        66.6%
        22.9
        G177REL15_M6_statistics_for_all_accepted_reads
        87.7
        G177REL15_M6_statistics_for_primary_reads
        74.6
        G177REL15_M6_statistics_for_primary_unique_reads
        66.6
        G177REL15_M7_G177REL15_M7
        80.1%
        34.8
        G177REL15_M7_sorted
        63.5%
        22.1
        G177REL15_M7_statistics_for_all_accepted_reads
        88.4
        G177REL15_M7_statistics_for_primary_reads
        75.4
        G177REL15_M7_statistics_for_primary_unique_reads
        67.3
        G177REL15_M8_G177REL15_M8
        81.2%
        30.5
        G177REL15_M8_sorted
        69.2%
        21.1
        G177REL15_M8_statistics_for_all_accepted_reads
        77.2
        G177REL15_M8_statistics_for_primary_reads
        66.1
        G177REL15_M8_statistics_for_primary_unique_reads
        59.2
        G177REL15_M9_G177REL15_M9
        84.5%
        17.7
        G177REL15_M9_sorted
        67.6%
        12.0
        G177REL15_M9_statistics_for_all_accepted_reads
        43.5
        G177REL15_M9_statistics_for_primary_reads
        37.9
        G177REL15_M9_statistics_for_primary_unique_reads
        34.3

        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.