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        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.


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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 1.7

        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 2019-02-18, 17:31 based on data in: /home/tavinbio/transcriptome_virus_renato/rRNA_logs


        General Statistics

        Showing 36/36 rows and 1/1 columns.
        Sample Name% Aligned
        logrrna_ChikV1_L001
        15.3%
        logrrna_ChikV1_L002
        15.0%
        logrrna_ChikV1_L003
        15.2%
        logrrna_ChikV1_L004
        15.1%
        logrrna_ChikV2_L001
        12.7%
        logrrna_ChikV2_L002
        12.5%
        logrrna_ChikV2_L003
        12.7%
        logrrna_ChikV2_L004
        12.5%
        logrrna_ChikV3_L001
        51.1%
        logrrna_ChikV3_L002
        50.7%
        logrrna_ChikV3_L003
        51.1%
        logrrna_ChikV3_L004
        50.8%
        logrrna_MayV1_L001
        13.8%
        logrrna_MayV1_L002
        13.6%
        logrrna_MayV1_L003
        13.8%
        logrrna_MayV1_L004
        13.7%
        logrrna_MayV2_L001
        10.9%
        logrrna_MayV2_L002
        10.8%
        logrrna_MayV2_L003
        10.9%
        logrrna_MayV2_L004
        10.8%
        logrrna_MayV3_L001
        20.5%
        logrrna_MayV3_L002
        20.3%
        logrrna_MayV3_L003
        20.5%
        logrrna_MayV3_L004
        20.4%
        logrrna_Mock4_L001
        67.3%
        logrrna_Mock4_L002
        67.0%
        logrrna_Mock4_L003
        67.3%
        logrrna_Mock4_L004
        67.1%
        logrrna_Mock5_L001
        60.0%
        logrrna_Mock5_L002
        59.6%
        logrrna_Mock5_L003
        60.0%
        logrrna_Mock5_L004
        59.8%
        logrrna_Mock6_L001
        24.2%
        logrrna_Mock6_L002
        23.9%
        logrrna_Mock6_L003
        24.2%
        logrrna_Mock6_L004
        24.0%

        Bowtie 2

        Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences.

        This plot shows the number of reads aligning to the reference in different ways.
        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment: PE mapped uniquely: Pair has only one occurence in the reference genome. PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair. PE one mate mapped uniquely: One read of a pair has one occurence. PE multimapped: Pair has multiple occurence. PE one mate multimapped: One read of a pair has multiple occurence. PE neither mate aligned: Pair has no occurence.

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