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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, 18:55 based on data in: /home/tavinbio/driver/Transcritoma_Virus_Renato/STAR/bam_name/htseq


        General Statistics

        Showing 36/36 rows and 2/2 columns.
        Sample Name% AssignedM Assigned
        Mock1_L001_sorted_name_count
        14.1%
        3.6
        Mock1_L002_sorted_name_count
        14.1%
        3.6
        Mock1_L003_sorted_name_count
        14.1%
        3.7
        Mock1_L004_sorted_name_count
        14.1%
        3.6
        Mock2_L001_sorted_name_count
        32.5%
        6.9
        Mock2_L002_sorted_name_count
        32.5%
        6.7
        Mock2_L003_sorted_name_count
        32.5%
        7.0
        Mock2_L004_sorted_name_count
        32.5%
        6.9
        Mock3_L001_sorted_name_count
        19.7%
        5.1
        Mock3_L002_sorted_name_count
        19.7%
        5.0
        Mock3_L003_sorted_name_count
        19.7%
        5.2
        Mock3_L004_sorted_name_count
        19.7%
        5.1
        OROV1_L001_sorted_name_count
        2.2%
        0.7
        OROV1_L002_sorted_name_count
        2.2%
        0.7
        OROV1_L003_sorted_name_count
        2.2%
        0.7
        OROV1_L004_sorted_name_count
        2.2%
        0.7
        OROV2_L001_sorted_name_count
        1.2%
        0.5
        OROV2_L002_sorted_name_count
        1.2%
        0.5
        OROV2_L003_sorted_name_count
        1.2%
        0.5
        OROV2_L004_sorted_name_count
        1.2%
        0.5
        OROV3_L001_sorted_name_count
        1.4%
        0.5
        OROV3_L002_sorted_name_count
        1.4%
        0.5
        OROV3_L003_sorted_name_count
        1.4%
        0.5
        OROV3_L004_sorted_name_count
        1.4%
        0.5
        ZIKAV1_L001_sorted_name_count
        3.0%
        1.0
        ZIKAV1_L002_sorted_name_count
        2.9%
        1.0
        ZIKAV1_L003_sorted_name_count
        2.9%
        1.0
        ZIKAV1_L004_sorted_name_count
        3.0%
        1.0
        ZIKAV2_L001_sorted_name_count
        3.9%
        1.7
        ZIKAV2_L002_sorted_name_count
        3.9%
        1.6
        ZIKAV2_L003_sorted_name_count
        3.9%
        1.7
        ZIKAV2_L004_sorted_name_count
        3.9%
        1.7
        ZIKAV3_L001_sorted_name_count
        5.6%
        2.1
        ZIKAV3_L002_sorted_name_count
        5.6%
        2.1
        ZIKAV3_L003_sorted_name_count
        5.6%
        2.1
        ZIKAV3_L004_sorted_name_count
        5.6%
        2.1

        HTSeq Count

        HTSeq Count is part of the HTSeq Python package - it takes a file with aligned sequencing reads, plus a list of genomic features and counts how many reads map to each feature.

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