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

        Explain with AI

        Configure AI settings to get explanations of plots and data in this report.

        Keys entered here will be stored in your browser's local storage. See the docs.


        Anonymize samples 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
        Settings are automatically saved. You can also save named configurations below.

        Save Settings

        You can save the toolbox settings for this report to the browser or as a file.


        Load Settings

        Choose a saved report profile from the browser or load from a file:

          Load from File

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.28

        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/MultiQC/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 2025-03-20, 16:38 CET based on data in: /Users/vlad/git/seqeralabs/web/packages/website/public/examples/bs-seq

        General Statistics

        Showing 8/8 rows and 10/15 columns.
        Sample NamemCpGmCHGmCHHC'sDupsUniqueAlignedAlignedTrimmed basesDupsGCAvg lenMedian lenFailedSeqs
        MethylC-Seq_mm_fc_1wk_SRR921767_1
        77.0%
        0.5%
        0.5%
        1568.0
        2.9%
        83.5M
        86.0M
        86.0%
        2.6%
        8.8%
        21.0%
        98bp
        100bp
        8%
        99.9M
        MethylC-Seq_mm_fc_1wk_SRR921768_1
        77.0%
        0.5%
        0.5%
        1559.9
        2.9%
        83.2M
        85.7M
        85.7%
        2.9%
        9.3%
        21.0%
        97bp
        100bp
        17%
        99.9M
        MethylC-Seq_mm_fc_1wk_SRR921769_1
        77.0%
        0.5%
        0.5%
        321.4
        1.6%
        17.2M
        17.5M
        85.7%
        3.0%
        6.0%
        21.0%
        97bp
        100bp
        8%
        20.4M
        MethylC-Seq_mm_fc_1wk_SRR921770_1
        77.0%
        0.5%
        0.5%
        1562.8
        2.9%
        83.4M
        85.9M
        85.9%
        2.8%
        8.7%
        21.0%
        97bp
        100bp
        8%
        99.9M
        MethylC-Seq_mm_fc_2wk_SRR921694_1
        75.7%
        0.9%
        1.1%
        1928.2
        7.0%
        111.7M
        120.1M
        78.4%
        13.5%
        15.5%
        26.0%
        92bp
        100bp
        17%
        153.1M
        MethylC-Seq_mm_fc_2wk_SRR921695_1
        75.8%
        0.9%
        1.1%
        1930.1
        7.0%
        111.8M
        120.2M
        78.4%
        13.6%
        14.6%
        26.0%
        92bp
        100bp
        8%
        153.4M
        MethylC-Seq_mm_fc_2wk_SRR921696_1
        75.7%
        0.9%
        1.1%
        1366.7
        6.1%
        84.1M
        89.6M
        77.1%
        20.7%
        11.5%
        25.0%
        86bp
        94bp
        8%
        116.2M
        MethylC-Seq_mm_fc_2wk_SRR921773_1
        75.8%
        0.9%
        1.1%
        1472.5
        6.3%
        91.6M
        97.7M
        76.3%
        23.6%
        10.9%
        25.0%
        85bp
        94bp
        8%
        128.1M

        Bismark

        Version: 0.14.4

        Maps bisulfite converted sequence reads and determine cytosine methylation states.URL: http://www.bioinformatics.babraham.ac.uk/projects/bismarkDOI: 10.1093/bioinformatics/btr167

        Alignment Rates

        Created with MultiQC

        Deduplication

        Created with MultiQC

        Strand Alignment

        All samples were run with --directional mode; alignments to complementary strands (CTOT, CTOB) were ignored.

        Created with MultiQC

        Cytosine Methylation

        Created with MultiQC

        M-Bias

        This plot shows the average percentage methylation and coverage across reads. See the bismark user guide for more information on how these numbers are generated.

        Created with MultiQC

        Cutadapt

        Version: 1.8

        Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

        Created with MultiQC

        Trimmed Sequence Lengths (3')

        This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

        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.

        Created with MultiQC

        FastQC: trimmed

        Version: 0.11.2

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

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

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

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

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -