Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.

        Choose Plots

        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

        Save Settings

        You can save the toolbox settings for this report to the browser.

        Load Settings

        Choose a saved report profile from the dropdown box below:

        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 2024-07-23, 13:07 based on data in:

        General Statistics

        Showing 65/65 rows and 5/6 columns.
        Sample NameM Reads Mapped% AssignedM Assigned% AlignedM Aligned
        G178_M10_G178_M10
        67.9%
        19.4
        G178_M10_sorted
        84.6%
        16.5
        G178_M10_statistics_for_all_accepted_reads
        43.3
        G178_M10_statistics_for_primary_reads
        40.4
        G178_M10_statistics_for_primary_unique_reads
        38.9
        G178_M11_G178_M11
        66.8%
        14.9
        G178_M11_sorted
        90.0%
        13.4
        G178_M11_statistics_for_all_accepted_reads
        32.9
        G178_M11_statistics_for_primary_reads
        30.9
        G178_M11_statistics_for_primary_unique_reads
        29.8
        G178_M12_G178_M12
        68.8%
        16.5
        G178_M12_sorted
        88.5%
        14.6
        G178_M12_statistics_for_all_accepted_reads
        36.4
        G178_M12_statistics_for_primary_reads
        34.2
        G178_M12_statistics_for_primary_unique_reads
        33.0
        G178_M13_G178_M13
        66.9%
        14.7
        G178_M13_sorted
        82.8%
        12.2
        G178_M13_statistics_for_all_accepted_reads
        32.7
        G178_M13_statistics_for_primary_reads
        30.5
        G178_M13_statistics_for_primary_unique_reads
        29.4
        G178_M1_G178_M1
        67.5%
        17.3
        G178_M1_sorted
        83.2%
        14.4
        G178_M1_statistics_for_all_accepted_reads
        38.6
        G178_M1_statistics_for_primary_reads
        35.9
        G178_M1_statistics_for_primary_unique_reads
        34.6
        G178_M2_G178_M2
        70.1%
        14.4
        G178_M2_sorted
        87.3%
        12.5
        G178_M2_statistics_for_all_accepted_reads
        32.1
        G178_M2_statistics_for_primary_reads
        29.9
        G178_M2_statistics_for_primary_unique_reads
        28.7
        G178_M3_G178_M3
        69.4%
        14.8
        G178_M3_sorted
        88.4%
        13.1
        G178_M3_statistics_for_all_accepted_reads
        32.7
        G178_M3_statistics_for_primary_reads
        30.7
        G178_M3_statistics_for_primary_unique_reads
        29.5
        G178_M4_G178_M4
        68.1%
        16.5
        G178_M4_sorted
        83.3%
        13.7
        G178_M4_statistics_for_all_accepted_reads
        37.4
        G178_M4_statistics_for_primary_reads
        34.5
        G178_M4_statistics_for_primary_unique_reads
        33.0
        G178_M5_G178_M5
        65.8%
        21.6
        G178_M5_sorted
        85.5%
        18.4
        G178_M5_statistics_for_all_accepted_reads
        48.4
        G178_M5_statistics_for_primary_reads
        44.9
        G178_M5_statistics_for_primary_unique_reads
        43.1
        G178_M6_G178_M6
        67.2%
        19.4
        G178_M6_sorted
        89.6%
        17.4
        G178_M6_statistics_for_all_accepted_reads
        43.8
        G178_M6_statistics_for_primary_reads
        40.5
        G178_M6_statistics_for_primary_unique_reads
        38.8
        G178_M7_G178_M7
        65.1%
        15.4
        G178_M7_sorted
        84.7%
        13.0
        G178_M7_statistics_for_all_accepted_reads
        33.7
        G178_M7_statistics_for_primary_reads
        31.8
        G178_M7_statistics_for_primary_unique_reads
        30.7
        G178_M8_G178_M8
        66.8%
        16.8
        G178_M8_sorted
        89.6%
        15.1
        G178_M8_statistics_for_all_accepted_reads
        37.1
        G178_M8_statistics_for_primary_reads
        34.8
        G178_M8_statistics_for_primary_unique_reads
        33.6
        G178_M9_G178_M9
        65.4%
        15.4
        G178_M9_sorted
        89.9%
        13.9
        G178_M9_statistics_for_all_accepted_reads
        34.4
        G178_M9_statistics_for_primary_reads
        32.0
        G178_M9_statistics_for_primary_unique_reads
        30.8

        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).

        loading..

        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.

        loading..

        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.

        loading..

        STAR

        STAR is an ultrafast universal RNA-seq aligner.

        Alignment Scores

        loading..

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

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