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        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 0.9

        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 2017-02-20, 14:02 based on data in: /cbcb/nelsayed-scratch/atb/small_rna/mmusculus_exosomev2/preprocessing


        General Statistics

        Showing 139/139 rows and 15/17 columns.
        Sample Name% AssignedM Assigned% Duplicates% Mapped% AlignedM Aligned% AlignedM Aligned% Aligned% AlignedM Aligned% Dropped% Dups% GCM Seqs
        accepted_hits
        0.0%
        100.0%
        align_summary
        78.8%
        13.6
        bowtie.left_kept_reads
        28.9%
        bowtie.left_kept_reads.m2g_um
        77.5%
        bowtie.left_kept_reads.m2g_um_seg1
        5.5%
        bowtie.left_kept_reads.m2g_um_seg2
        6.1%
        hpgl0522_forward-trimmed
        88.1%
        30.6
        hpgl0523_forward
        1.5%
        hpgl0523_forward-trimmed
        5.4%
        1.8
        hpgl0523_forward-trimmed-def
        0.0%
        74.2%
        hpgl0523_forward-trimmed-def.err
        74.2%
        24.2
        hpgl0523_forward-trimmed-v0M1
        0.0%
        24.1%
        hpgl0523_forward-trimmed-v0M1.err
        5.0%
        1.6
        hpgl0523_forward-trimmed-v1M1
        0.0%
        66.6%
        hpgl0523_forward-trimmed-v1M1.err
        12.6%
        4.1
        hpgl0523_forward-trimmed-v1M1_mi_stranded.count
        0.0%
        0.0
        hpgl0523_forward-trimmed-v2M1
        0.0%
        74.3%
        hpgl0523_forward-trimmed-v2M1.err
        13.2%
        4.3
        hpgl0523_forward-v1M1.err
        0.0%
        0.0
        hpgl0523_mmusculus_align_summary
        77.7%
        25.3
        hpgl0524
        1.1%
        hpgl0524-trimmed
        1.9%
        0.6
        hpgl0524_forward
        1.1%
        hpgl0524_forward-trimmed-def
        0.0%
        67.3%
        hpgl0524_forward-trimmed-def.err
        67.3%
        22.2
        hpgl0524_mmusculus_align_summary
        64.1%
        21.1
        hpgl0525
        0.6%
        hpgl0525-trimmed
        1.1%
        0.4
        hpgl0525_forward
        0.6%
        hpgl0525_forward-trimmed-def
        0.0%
        77.0%
        hpgl0525_forward-trimmed-def.err
        77.0%
        25.3
        hpgl0525_mmusculus_align_summary
        73.7%
        24.2
        hpgl0526-trimmed
        5.6%
        1.9
        hpgl0527-trimmed
        26.0%
        6.0
        hpgl0555
        0.7%
        hpgl0555-trimmed
        0.2%
        0.0
        hpgl0555_forward
        0.7%
        hpgl0555_mmusculus_align_summary
        25.1%
        5.5
        hpgl0555a
        0.7%
        hpgl0555a-trimmed
        0.0%
        0.0
        hpgl0555b
        0.6%
        hpgl0555b-trimmed
        0.4%
        0.0
        hpgl0556
        1.5%
        hpgl0556-trimmed
        17.9%
        1.0
        hpgl0556_forward
        0.9%
        hpgl0556_forward-trimmed-V1M1
        0.0%
        70.7%
        hpgl0556_forward-trimmed-V1M1.err
        54.7%
        10.0
        hpgl0556_forward-trimmed-def
        0.0%
        78.8%
        hpgl0556_forward-trimmed-def.err
        78.8%
        14.5
        hpgl0556_forward-trimmed-v0M1
        0.0%
        44.8%
        hpgl0556_forward-trimmed-v0M1.err
        38.2%
        7.0
        hpgl0556_forward-trimmed-v1M1l20
        0.0%
        70.7%
        hpgl0556_forward-trimmed-v1M1l20.err
        54.7%
        10.0
        hpgl0556_forward-trimmed-v2M1
        0.0%
        78.9%
        hpgl0556_forward-trimmed-v2M1.err
        57.1%
        10.5
        hpgl0556_mmusculus_align_summary
        78.4%
        14.4
        hpgl0556a
        1.5%
        hpgl0556a-trimmed
        10.1%
        0.6
        hpgl0556b
        0.6%
        hpgl0556b-trimmed
        8.4%
        1.1
        hpgl0557
        1.0%
        hpgl0557-trimmed
        16.4%
        4.3
        hpgl0557_forward
        0.7%
        hpgl0557_forward-V1M1
        0.0%
        62.0%
        hpgl0557_forward-V1M1.err
        50.0%
        9.9
        hpgl0557_forward-def
        0.0%
        67.7%
        hpgl0557_forward-def.err
        67.7%
        13.5
        hpgl0557_forward-v0M1
        0.0%
        47.6%
        hpgl0557_forward-v0M1.err
        40.5%
        8.1
        hpgl0557_forward-v1M1l20
        0.0%
        62.0%
        hpgl0557_forward-v1M1l20.err
        50.0%
        9.9
        hpgl0557_forward-v2M1
        0.0%
        67.7%
        hpgl0557_forward-v2M1.err
        52.9%
        10.5
        hpgl0557_mmusculus_align_summary
        66.7%
        13.3
        hpgl0557a
        1.0%
        hpgl0557a-trimmed
        19.8%
        1.3
        hpgl0557b
        0.5%
        hpgl0557b-trimmed
        1.8%
        0.2
        hpgl0558
        0.9%
        hpgl0558-trimmed
        0.0%
        0.0
        hpgl0558a
        0.8%
        hpgl0558a-trimmed
        13.7%
        0.4
        hpgl0558b
        0.4%
        hpgl0558b-trimmed
        7.8%
        1.1
        hpgl0559
        47.8%
        hpgl0559-trimmed
        8.4%
        1.4
        hpgl0559_forward
        30.2%
        hpgl0559_forward-V1M1
        0.0%
        48.5%
        hpgl0559_forward-V1M1.err
        29.2%
        3.7
        hpgl0559_forward-def
        0.0%
        56.8%
        hpgl0559_forward-def.err
        56.8%
        7.1
        hpgl0559_forward-trimmed
        0.1%
        hpgl0559_forward-v0M1
        0.0%
        25.0%
        hpgl0559_forward-v0M1.err
        18.2%
        2.3
        hpgl0559_forward-v1M1l20
        0.0%
        48.5%
        hpgl0559_forward-v1M1l20.err
        29.2%
        3.7
        hpgl0559_forward-v2M1
        0.0%
        56.9%
        hpgl0559_forward-v2M1.err
        32.0%
        4.0
        hpgl0559_mmusculus_align_summary
        55.6%
        7.0
        hpgl0559a
        47.8%
        hpgl0559a-trimmed
        10.4%
        0.4
        hpgl0559b
        18.7%
        hpgl0559b-trimmed
        6.8%
        0.6
        hpgl0560
        1.6%
        hpgl0560-trimmed
        3.3%
        1.0
        hpgl0560_forward
        0.9%
        hpgl0560_forward-trimmed-V1M1
        0.0%
        47.7%
        hpgl0560_forward-trimmed-V1M1.err
        8.7%
        2.2
        hpgl0560_forward-trimmed-def
        0.0%
        53.4%
        hpgl0560_forward-trimmed-def.err
        53.4%
        13.4
        hpgl0560_forward-trimmed-v0M1
        0.0%
        25.6%
        hpgl0560_forward-trimmed-v0M1.err
        1.9%
        0.5
        hpgl0560_forward-trimmed-v1M1l20
        0.0%
        47.7%
        hpgl0560_forward-trimmed-v1M1l20.err
        8.7%
        2.2
        hpgl0560_forward-trimmed-v2M1
        0.0%
        53.7%
        hpgl0560_forward-trimmed-v2M1.err
        5.8%
        1.5
        hpgl0560_mmusculus_align_summary
        36.7%
        9.2
        hpgl0560a
        1.6%
        hpgl0560a-trimmed
        0.6%
        0.0
        hpgl0560b
        0.7%
        hpgl0560b-trimmed
        3.9%
        0.7
        hpgl0673_forward-trimmed
        9.0%
        1.6
        94.3%
        52%
        17.3
        hpgl0674_forward-trimmed
        9.4%
        1.7
        hpgl0675_forward-trimmed
        6.7%
        1.2
        hpgl0676_forward-trimmed
        9.8%
        5.1
        hpgl0677_forward-trimmed
        56.0%
        9.0
        hpgl0678_forward-trimmed
        3.2%
        0.6
        hpgl0679_forward-trimmed
        3.4%
        0.5
        hpgl0680_forward-trimmed
        1.8%
        0.3
        hpgl0681_forward-trimmed
        28.4%
        4.3
        hpgl0682_forward-trimmed
        2.6%
        0.4
        hpgl0683_forward-trimmed
        2.8%
        0.4
        hpgl0684_forward-trimmed
        2.5%
        0.2
        hpgl0685_forward-trimmed
        0.6%
        0.1
        hpgl0686_forward-trimmed
        0.4%
        0.1
        hpgl0687_forward-trimmed
        0.7%
        0.1
        hpgl0688_forward-trimmed
        79.4%
        11.4
        kallisto_mmusculus_mi.stats
        0.0%
        11.1%
        unmapped
        0.0%
        0.0%

        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.

        loading..

        Bamtools

        Bamtools provides both a programmer's API and an end-user's toolkit for handling BAM files.

        Bamtools Stats

        loading..

        Kallisto

        Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data.

        loading..

        Tophat

        Tophat is a fast splice junction mapper for RNA-Seq reads. It aligns RNA-Seq reads to mammalian-sized genomes.

        loading..

        Bowtie 2

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

        loading..

        Bowtie 1

        Bowtie 1 is an ultrafast, memory-efficient short read aligner.

        loading..

        Trimmomatic

        Trimmomatic is a flexible read trimming tool for Illumina NGS data.

        loading..

        FastQC

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Quality Histograms

        The mean quality value across each base position in the read. See the FastQC help.

        loading..

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality. See the FastQC help.

        loading..

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called. See the FastQC help.

        Click a heatmap row to see a line plot for that dataset.

        rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content. See the FastQC help.

        loading..

        Per Base N Content

        The percentage of base calls at each position for which an N was called. See the FastQC help.

        loading..

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help.

        loading..

        Sequence Duplication Levels

        The relative level of duplication found for every sequence. See the FastQC help.

        loading..

        Overrepresented sequences

        The total amount of overrepresented sequences found in each library. See the FastQC help for further information.

        loading..

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. See the FastQC help. Only samples with ≥ 0.1% adapter contamination are shown.

        No samples found with any adapter contamination > 0.1%