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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 1.0

        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.

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        Report generated on 2017-05-24, 21:48 based on data in: /tmp/tmpWNBij6/job_working_directory/000/46/working/multiqc_WDir


        General Statistics

        Showing 15/15 rows and 17/21 columns.
        Sample Name% AssignedM Assigned% DupsInsert SizeError rateM Non-PrimaryM Reads Mapped% MappedM Total seqsM Reads Mapped% Aligned% AlignedM Aligned% Trimmed% Dups% GCM Seqs
        70: TopHat on data 1, data 4, and data 3: accepted_hits
        70.8%
        0.3
        75: TopHat on data 1, data 6, and data 5: accepted_hits
        69.6%
        0.4
        80: TopHat on data 1, data 8, and data 7: accepted_hits
        71.8%
        0.4
        85: TopHat on data 1, data 10, and data 9: accepted_hits
        72.0%
        0.4
        90: TopHat on data 1, data 12, and data 11: accepted_hits
        71.3%
        0.4
        95: TopHat on data 1, data 14, and data 13: accepted_hits
        70.7%
        0.5
        bismark_data
        69.7%
        dataset_114
        0.6%
        dataset_197
        176bp
        dataset_33
        10.8%
        poulet5_1
        36.3%
        48%
        0.3
        poulet5_2
        36.2%
        48%
        0.3
        samtools_flagstat
        20.7
        samtools_stats
        0.42%
        0.0
        0.6
        100.0%
        0.6
        tophat_data
        99.5%
        0.3

        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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        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Mark Duplicates

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        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

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        GC Coverage Bias

        This plot shows bias in coverage across regions of the genome with varying GC content. A perfect library would be a flat line at y = 1.

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        Samtools

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        Percent Mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads.

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        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

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        Samtools Flagstat

        This module parses the output from samtools flagstat. All numbers in millions.

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        Bismark

        Bismark is a tool to map bisulfite converted sequence reads and determine cytosine methylation states.

        Alignment Rates

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        Strand Alignment

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        Tophat

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

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        Cutadapt

        Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.

        This plot shows the number of reads with certain lengths of adapter trimmed. Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length. See the cutadapt documentation for more information on how these numbers are generated.

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        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.

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        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.

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        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 sample 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.

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        Per Base N Content

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

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        Sequence Length Distribution

        All samples have sequences of a single length (101bp).


        Sequence Duplication Levels

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

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        Overrepresented sequences

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

        2 samples had less than 1% of reads made up of overrepresented sequences

        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%