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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 1.10.dev0

        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 2021-02-10, 17:03 based on data in: /Users/cerebis/git/qc3C/Feb7_2020/drr


        qc3C

        qc3C provides reference-free and BAM based quality control for Hi-C data

        BAM mode analysis details

        This table details various alignment features which are potentially of interest to researchers attempting to assess the quality of a Hi-C library.

        Showing 17/17 rows and 6/8 columns.
        SampleDigestAccepted pairsRead lengthInsert lengthUnobservedRead-thru
        DRR177157
        HindIII
        200000
        127bp
        217bp
        0.0%
        35.8%
        DRR177158
        HindIII
        200000
        127bp
        233bp
        0.0%
        39.1%
        DRR177159
        DpnII
        200000
        127bp
        219bp
        0.0%
        49.1%
        DRR177160
        DpnII
        200000
        127bp
        218bp
        0.0%
        46.0%
        DRR177161
        Sau3AI MluCI
        200000
        127bp
        511bp
        53.2%
        5.1%
        DRR177162
        Sau3AI MluCI
        200000
        127bp
        550bp
        56.6%
        3.6%
        DRR177163
        DpnII HinfI
        200000
        127bp
        297bp
        19.3%
        30.8%
        DRR177164
        DpnII HinfI
        200000
        127bp
        319bp
        24.4%
        29.9%
        DRR177165
        HindIII
        200000
        127bp
        248bp
        4.1%
        41.4%
        DRR177166
        HindIII
        200000
        127bp
        248bp
        4.1%
        36.7%
        DRR177167
        HindIII
        200000
        151bp
        219bp
        0.0%
        42.3%
        DRR177168
        HindIII
        200000
        151bp
        227bp
        0.0%
        46.0%
        DRR177169
        DpnII
        200000
        151bp
        218bp
        0.0%
        57.7%
        DRR177170
        DpnII
        200000
        151bp
        213bp
        0.0%
        53.7%
        DRR177171
        Sau3AI MluCI
        200000
        151bp
        485bp
        41.2%
        5.2%
        DRR177172
        DpnII HinfI
        200000
        151bp
        314bp
        8.0%
        37.1%
        DRR177173
        HindIII
        200000
        151bp
        237bp
        0.0%
        49.6%

        BAM mode read parsing

        This figure displays a breakdown of proportion of parsed reads rejected due to various criteria and the proportion that were accepted.

        loading..

        BAM mode HiC-Pro validation

        A visualisation of the read-pair categories devised by HiC-Pro.

        As the field has moved from 6-cutter to 4-cutter enzymes, and subsequently dual-enzyme digests, the higher density of sites has made this framework less useful, since it has become increasingly easy to satisfy the intervening site criteria.

        loading..

        BAM mode long-range pairs

        This plot visualises the breakdown of read-pairs based on separation distance.

        The breakdown of separation distance is only calculated for cis-mapping pairs.

        Ideally, Hi-C proximity ligation should produce many pairs which are greater than 1000 bp apart. However, these statistics are strongly influenced by the state of the reference. For draft assemblies the distance at which pairs can map is limited by the degree of fragmentation and length of contigs. As a result, many more pairs will be categorised as trans-mapping and pairs which are truly inter-molecular cannot be distinguished from those which are merely inter-contig.

        loading..

        BAM mode distribution of fragment separation

        This figure displays the a normalised histogram of read-pair separation binned uniformly in log-space.

        Due to the binning strategy, the x-axis is log-scaled and visually accommodates pair separations up to 1 million bp. The inferred insert size for each library is represented by a dashed, grey vertical line. The y-axis is log-scaled by default, allowing the density attributed to long-range pairs to be more easily seen.

        A characteristic of Hi-C libraries, is the presence of a large peak below 1000 bp. qc3C attributes this to regular (and undesirable) shotgun pairs creeping through the Hi-C protocol. The peak is used by qc3C to infer the insert size, which is later employed to estimate unobservable extent of inserts.

        Note: the inferred insert size can be significantly smaller than what a sequencing facility might report the experimentally determined insert size to be. This discrepancy can be explained by the failure to account for the additional adapter sequence when fragments are assessed during library preparation.

        loading..

        BAM mode junction breakdown

        This figure displays the frequency at which a library's possible junction sequences are actually observed in the reads. (Trivial single-digests are ignored)

        For trivial single-enzyme digests, there is only one possible junction sequence and so the result for these experiments are not plotted. For dual-enzyme (such as Phase Genomics) there are four potential junctions, while for dual-enzyme digests with one ambiguous site (such as Arima Genomics) there are 16 possible junction sequences.

        How efficiently the more complicated library protocols are at producing hybrid junctions is possibly just a point of interest.

        Junctions are named for which enzymes was responsible for creating the 5' and 3' ends. E.g. Sau3AI/MluCI would involve two different enzymes, while Sau3AI/Sau3AI only one, as would be the case in a single-enzyme digest. Proceeding the name is the actual junction sequence.

        The junctions are grouped by their 5' and then 3' enzyme, while the color spectrum used across each bar aims to emphasise these enzymatic sources.

        Note: in BAM mode, the counts are controlled for false positives, in the sense that read alignments must terminate at a cutsite, but the read sequence must continue and contain the observed junction.

        loading..

        K-mer mode runtime details

        This table includes user specified input options, observed read-length and unobservable fraction.

        Showing 17/17 rows and 5/8 columns.
        SampleDigestAccepted readsInsert lengthRead lengthUnobservable extent
        DRR177157
        HindIII
        200000
        214bp
        127bp
        3.1%
        DRR177158
        HindIII
        200000
        231bp
        127bp
        3.6%
        DRR177159
        DpnII
        200000
        216bp
        127bp
        4.7%
        DRR177160
        DpnII
        200000
        215bp
        127bp
        4.5%
        DRR177161
        Sau3AI MluCI
        200000
        509bp
        127bp
        54.4%
        DRR177162
        Sau3AI MluCI
        200000
        549bp
        127bp
        57.9%
        DRR177163
        DpnII HinfI
        200000
        295bp
        127bp
        22.8%
        DRR177164
        DpnII HinfI
        200000
        315bp
        127bp
        27.7%
        DRR177165
        HindIII
        200000
        244bp
        127bp
        7.9%
        DRR177166
        HindIII
        200000
        244bp
        127bp
        10.0%
        DRR177167
        HindIII
        200000
        216bp
        151bp
        1.6%
        DRR177168
        HindIII
        200000
        225bp
        151bp
        2.0%
        DRR177169
        DpnII
        200000
        215bp
        151bp
        2.5%
        DRR177170
        DpnII
        200000
        210bp
        151bp
        2.2%
        DRR177171
        Sau3AI MluCI
        200000
        486bp
        151bp
        41.8%
        DRR177172
        DpnII HinfI
        200000
        311bp
        151bp
        11.2%
        DRR177173
        HindIII
        200000
        234bp
        151bp
        2.3%

        K-mer mode Hi-C fraction

        This table lists the inferred proportion of Hi-C proximity ligation fragments.

        Here, Mean adjusted Hi-C fraction represents the best estimate of the proportion of a library's read-pairs which are a product of proximity ligation. This figure is arrived at by correcting the raw estimate for the fraction of insert extent which was not observable.

        The observable extent is limited by the length of reads relative to the supplied insert size, as well as a further constraint on flanking sequence around any suspected junction sequence.

        Showing 17/17 rows and 2/2 columns.
        SampleMean raw Hi-C fractionMean adjusted Hi-C fraction
        DRR177157
        40.4%
        43.1%
        DRR177158
        45.5%
        49.0%
        DRR177159
        51.0%
        56.5%
        DRR177160
        49.3%
        54.4%
        DRR177161
        6.8%
        15.4%
        DRR177162
        4.8%
        11.8%
        DRR177163
        41.5%
        56.2%
        DRR177164
        41.0%
        59.4%
        DRR177165
        49.4%
        55.8%
        DRR177166
        43.8%
        51.9%
        DRR177167
        46.0%
        47.9%
        DRR177168
        51.3%
        53.8%
        DRR177169
        55.4%
        59.0%
        DRR177170
        52.7%
        55.8%
        DRR177171
        6.7%
        11.6%
        DRR177172
        47.1%
        54.9%
        DRR177173
        53.4%
        56.3%

        K-mer mode read parsing

        This figure displays a breakdown of proportion of parsed reads rejected due to various criteria and the proportion that were accepted.

        loading..

        K-mer mode junction breakdown

        This figure displays the frequency at which a library's possible junction sequences are actually observed in the reads. (Trivial single-digests are ignored)

        For trivial single-enzyme digests, there is only one possible junction sequence and so the 
        result for these experiments are not plotted. For dual-enzyme (such as Phase Genomics) there are 
        four potential junctions, while for dual-enzyme digests with one ambiguous site (such as Arima 
        Genomics) there are 16 possible junction sequences.
        
        How efficiently the more complicated library protocols are at producing hybrid junctions is 
        possibly just a point of interest.
        
        Junctions are named for which enzymes was responsible for creating the 5' and 3' ends.
        E.g. `Sau3AI/MluCI` would involve two different enzymes, while `Sau3AI/Sau3AI` only one, as 
        would be the case in a single-enzyme digest. Proceeding the name is the actual junction 
        sequence.
        
        The junctions are grouped by their 5' and then 3' enzyme, while the color spectrum used across 
        each bar aims to emphasise these enzymatic sources.
        

        Note: in k-mer mode, the counts are not controlled for false positives.

        loading..