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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 1.12.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 2023-11-01, 20:45 based on data in: /data/input/appresults

        Welcome! Not sure where to start?   Watch a tutorial video   (6:06)

        General Statistics

        Showing 7/7 rows and 11/31 columns.
        Sample NameM Input readsUnmapDupProp pairMed ISContam'nVariantsSexDepthFold Enrichment% Target Bases 30X
        20012D-115-01
        119.8
        0.5%
        8.9%
        99.0%
        202
        0.00
        24352
        X0
        5.0 x
        51 X
        97%
        20012D-115-02
        121.2
        0.5%
        9.4%
        99.0%
        205
        0.00
        23970
        X0
        5.1 x
        50 X
        97%
        20012D-115-03
        101.3
        0.6%
        8.2%
        99.0%
        197
        0.00
        24376
        XX
        4.3 x
        51 X
        97%
        20012D-115-04
        103.8
        0.4%
        8.5%
        99.2%
        196
        0.00
        24237
        XY
        4.4 x
        51 X
        97%
        20012D-115-05
        121.3
        0.5%
        9.0%
        99.0%
        205
        0.00
        24409
        X0
        5.1 x
        51 X
        97%
        20012D-115-06
        105.0
        0.6%
        8.2%
        98.8%
        208
        0.00
        24173
        X0
        4.5 x
        50 X
        97%
        20012D-115-07
        118.1
        0.5%
        8.8%
        99.0%
        204
        0.00
        24161
        X0
        5.0 x
        51 X
        97%

        DRAGEN

        DRAGEN is a Bio-IT Platform that provides ultra-rapid secondary analysis of sequencing data using field-programmable gate array technology (FPGA).

        Mapping metrics

        Mapping metrics, similar to the metrics computed by the samtools-stats command. Shown on per read group level. To see per-sample level metrics, refer to the general stats table.

        Showing 7/7 rows and 12/70 columns.
        Sample NameM Input readsPairedQC-failUnmapDupProp pairDiscordSingletonDiff chr, MQ⩾10Med ISM AlignmentsSec'ry
        20012D-115-01_L3
        119.8
        100.0%
        0.00%
        0.5%
        8.9%
        99.0%
        0.14%
        0.31%
        0.08%
        202
        119.4
        0.00%
        20012D-115-02_L3
        121.2
        100.0%
        0.00%
        0.5%
        9.4%
        99.0%
        0.16%
        0.33%
        0.10%
        205
        120.7
        0.00%
        20012D-115-03_L3
        101.3
        100.0%
        0.00%
        0.6%
        8.2%
        99.0%
        0.17%
        0.32%
        0.12%
        197
        101.0
        0.00%
        20012D-115-04_L3
        103.8
        100.0%
        0.00%
        0.4%
        8.5%
        99.2%
        0.15%
        0.22%
        0.10%
        196
        103.7
        0.00%
        20012D-115-05_L3
        121.3
        100.0%
        0.00%
        0.5%
        9.0%
        99.0%
        0.14%
        0.32%
        0.09%
        205
        120.9
        0.00%
        20012D-115-06_L3
        105.0
        100.0%
        0.00%
        0.6%
        8.2%
        98.8%
        0.15%
        0.40%
        0.10%
        208
        104.6
        0.00%
        20012D-115-07_L3
        118.1
        100.0%
        0.00%
        0.5%
        8.8%
        99.0%
        0.13%
        0.32%
        0.08%
        204
        117.7
        0.00%

        Mapped / paired / duplicated

        Distribution of reads based on pairing, duplication and mapping.

        Created with Highcharts 5.0.6ReadsChart context menuExport PlotDragen: Mapped/paired/duplicated reads per read groupPaired, properlyPaired, discordantSingletonUnmapped20012D-115-01_L320012D-115-02_L320012D-115-03_L320012D-115-04_L320012D-115-05_L320012D-115-06_L320012D-115-07_L305M10M15M20M25M30M35M40M45M50M55M60M65M70M75M80M85M90M95M100M105M110M115M120M125M13…130MCreated with MultiQC

        Variant calling

        Variant calling metrics. Metrics are reported for each sample in multi sample VCF and gVCF files. Based on the run case, metrics are reported either as standard VARIANT CALLER or JOINT CALLER. All metrics are reported for post-filter VCFs, except for the "Filtered" metrics which represent how many variants were filtered out from pre-filter VCF to generate the post-filter VCF.

        Showing 7/7 rows and 9/31 columns.
        Sample NameVariantsMultiallelicSNPInsDelTi/TvHet/HomCallabilityM VC reads
        20012D-115-01
        24352
        0.1%
        97.3%
        1.2%
        1.5%
        2.9
        1.5
        NA
        101.8
        20012D-115-02
        23970
        0.1%
        97.3%
        1.2%
        1.5%
        2.9
        1.6
        NA
        102.4
        20012D-115-03
        24376
        0.1%
        97.3%
        1.3%
        1.4%
        2.9
        1.6
        NA
        86.8
        20012D-115-04
        24237
        0.1%
        97.4%
        1.2%
        1.4%
        2.9
        1.7
        NA
        88.7
        20012D-115-05
        24409
        0.1%
        97.3%
        1.2%
        1.5%
        2.9
        1.6
        NA
        103.2
        20012D-115-06
        24173
        0.1%
        97.3%
        1.2%
        1.5%
        2.9
        1.5
        NA
        89.9
        20012D-115-07
        24161
        0.1%
        97.4%
        1.2%
        1.4%
        2.9
        1.6
        NA
        100.6

        WGS Coverage Metrics

        Coverage metrics over a region (where the region can be a target region, a QC coverage region, or the whole genome). Press the Help button for details.

        The following criteria are used when calculating coverage:

        • Duplicate reads and clipped bases are ignored.
        • Only reads with MAPQ > min MAPQ and bases with BQ > min BQ are considered

        Considering only bases usable for variant calling, i.e. excluding:

        1. Clipped bases
        2. Bases in duplicate reads
        3. Reads with MAPQ < min MAPQ (default 20)
        4. Bases with BQ < min BQ (default 10)
        5. Reads with MAPQ = 0 (multimappers)
        6. Overlapping mates are double-counted
        Showing 7/7 rows and 7/14 columns.
        Sample NameM Aln readsMb Aln basesReads on targetBases on targetDepthUniformity (>0.2×mean)Mean/med autosomal coverage
        20012D-115-01
        103.5
        15096.0 
        100.0%
        100.0%
        5.0 x
        5.9 %
        inf
        20012D-115-02
        104.1
        15193.7 
        100.0%
        100.0%
        5.1 x
        6.0 %
        inf
        20012D-115-03
        88.4
        12836.5 
        100.0%
        100.0%
        4.3 x
        8.0 %
        inf
        20012D-115-04
        90.3
        13100.8 
        100.0%
        100.0%
        4.4 x
        7.9 %
        inf
        20012D-115-05
        105.1
        15347.9 
        100.0%
        100.0%
        5.1 x
        6.0 %
        inf
        20012D-115-06
        91.5
        13405.6 
        100.0%
        100.0%
        4.5 x
        8.1 %
        inf
        20012D-115-07
        102.3
        14954.0 
        100.0%
        100.0%
        5.0 x
        8.4 %
        inf

        QC Region 1 Coverage Metrics

        Coverage metrics over a region (where the region can be a target region, a QC coverage region, or the whole genome). Press the Help button for details.

        The following criteria are used when calculating coverage:

        • Duplicate reads and clipped bases are ignored.
        • Only reads with MAPQ > min MAPQ and bases with BQ > min BQ are considered

        Considering only bases usable for variant calling, i.e. excluding:

        1. Clipped bases
        2. Bases in duplicate reads
        3. Reads with MAPQ < min MAPQ (default 20)
        4. Bases with BQ < min BQ (default 10)
        5. Reads with MAPQ = 0 (multimappers)
        6. Overlapping mates are double-counted
        Showing 7/7 rows and 5/12 columns.
        Sample NameReads on targetBases on targetDepthUniformity (>0.2×mean)Mean/med autosomal coverage
        20012D-115-01
        86.3%
        58.6%
        271.7 x
        99.0 %
        1.03
        20012D-115-02
        85.8%
        58.2%
        271.2 x
        99.0 %
        1.03
        20012D-115-03
        86.9%
        59.0%
        232.2 x
        98.9 %
        1.03
        20012D-115-04
        87.0%
        58.9%
        236.9 x
        99.1 %
        1.03
        20012D-115-05
        86.0%
        58.2%
        273.1 x
        99.1 %
        1.03
        20012D-115-06
        85.7%
        58.0%
        238.3 x
        99.1 %
        1.03
        20012D-115-07
        86.1%
        58.3%
        267.1 x
        99.1 %
        1.03

        QC Region 2 Coverage Metrics

        Coverage metrics over a region (where the region can be a target region, a QC coverage region, or the whole genome). Press the Help button for details.

        The following criteria are used when calculating coverage:

        • Duplicate reads and clipped bases are ignored.
        • Only reads with MAPQ > min MAPQ and bases with BQ > min BQ are considered

        Considering only bases usable for variant calling, i.e. excluding:

        1. Clipped bases
        2. Bases in duplicate reads
        3. Reads with MAPQ < min MAPQ (default 20)
        4. Bases with BQ < min BQ (default 10)
        5. Reads with MAPQ = 0 (multimappers)
        6. Overlapping mates are double-counted
        Showing 7/7 rows and 5/12 columns.
        Sample NameReads on targetBases on targetDepthUniformity (>0.2×mean)Mean/med autosomal coverage
        20012D-115-01
        93.6%
        91.2%
        162.5 x
        85.0 %
        1.16
        20012D-115-02
        93.6%
        91.0%
        163.1 x
        86.0 %
        1.15
        20012D-115-03
        93.8%
        91.6%
        138.4 x
        84.6 %
        1.14
        20012D-115-04
        93.8%
        91.6%
        141.6 x
        84.4 %
        1.13
        20012D-115-05
        93.7%
        91.2%
        164.5 x
        86.4 %
        1.13
        20012D-115-06
        93.6%
        91.0%
        143.6 x
        86.3 %
        1.15
        20012D-115-07
        93.7%
        91.2%
        160.7 x
        85.6 %
        1.14

        Coverage distribution

        Number of locations in the reference genome with a given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

        Created with Highcharts 5.0.6Depth (x)Number of bases in genome covered by X readsChart context menuExport PlotDragen: Coverage distribution0102030405060708090100110120130140150160170180190200050000000010000000001500000000200000000025000000003000000000Created with MultiQC

        Cumulative coverage hist

        Number of locations in the reference genome with at least given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with Highcharts 5.0.6Depth (x)% of bases in genome covered by at least X readsChart context menuExport PlotDragen: Cumulative coverage hist0102030405060708090100110120130140150160170180190200020406080100Created with MultiQC

        Coverage per contig

        Average coverage per contig or chromosome. Calculated as the number of bases (excluding duplicate marked reads, reads with MAPQ=0, and clipped bases), divided by the length of the contig or (if a target bed is used) the total length of the target region spanning that contig.

        Created with Highcharts 5.0.6RegionAverage coverageChart context menuExport PlotDragen: Average coverage per contig (main contigs)chr1chr2chr3chr4chr5chr6chr7chr8chr9chr10chr11chr12chr13chr14chr15chr16chr17chr18chr19chr20chr21chr22Autosomal regionschrXchrY05101520Created with MultiQC

        Coverage per contig (non-main)

        Non-main contigs: unlocalized (random), unplaced (chrU), alts (*_alt), mitochondria (chrM), EBV, HLA. Zoom in to see more contigs as all labels don't fit the screen.

        Created with Highcharts 5.0.6RegionAverage coverageChart context menuExport PlotDragen: Average coverage of non-main contigschrMchrUn_KI270302v1chrUn_KI270529v1chrUn_KI270383v1chr1_KI270760v1_altchr5_KI270791v1_altchr9_KI270823v1_altchr16_KI270854v1_altchr21_GL383579v2_altchr17_KI270908v1_altchr19_KI270938v1_altchrUn_KN707647v1_decoychrUn_KN707690v1_decoychrUn_KN707733v1_decoychrUn_KN707776v1_decoychrUn_KN707819v1_decoychrUn_KN707862v1_decoychrUn_KN707905v1_decoychrUn_KN707948v1_decoychrUn_KN707991v1_decoychrUn_JTFH01000042v1_decoychrUn_JTFH01000085v1_decoychrUn_JTFH01000128v1_decoychrUn_JTFH01000171v1_decoychrUn_JTFH01000214v1_decoychrUn_JTFH01000257v1_decoychrUn_JTFH01000300v1_decoychrUn_JTFH01000343v1_decoychrUn_JTFH01000386v1_decoychrUn_JTFH01000429v1_decoychrUn_JTFH01000472v1_decoychrUn_JTFH01000515v1_decoychrUn_JTFH01000558v1_decoychrUn_JTFH01000601v1_decoychrUn_JTFH01000644v1_decoychrUn_JTFH01000687v1_decoychrUn_JTFH01000730v1_decoychrUn_JTFH01000773v1_decoychrUn_JTFH01000816v1_decoychrUn_JTFH01000859v1_decoychrUn_JTFH01000902v1_decoychrUn_JTFH01000945v1_decoychrUn_JTFH01000988v1_decoychrUn_JTFH01001031v1_decoychrUn_JTFH01001074v1_decoychrUn_JTFH01001117v1_decoychrUn_JTFH01001160v1_decoychrUn_JTFH01001203v1_decoychrUn_JTFH01001246v1_decoychrUn_JTFH01001289v1_decoychrUn_JTFH01001332v1_decoychrUn_JTFH01001375v1_decoychrUn_JTFH01001418v1_decoychrUn_JTFH01001461v1_decoychrUn_JTFH01001504v1_decoychrUn_JTFH01001547v1_decoychrUn_JTFH01001590v1_decoychrUn_JTFH01001633v1_decoychrUn_JTFH01001676v1_decoychrUn_JTFH01001719v1_decoychrUn_JTFH01001762v1_decoychrUn_JTFH01001805v1_decoychrUn_JTFH01001848v1_decoychrUn_JTFH01001891v1_decoychrUn_JTFH01001934v1_decoychrUn_JTFH01001977v1_decoyHLA-A*02:10HLA-A*23:01:01HLA-A*33:07HLA-B*08:79HLA-B*27:05:02HLA-B*40:150HLA-B*52:01:01:01HLA-C*01:40HLA-C*04:71HLA-C*07:67HLA-C*18:01HLA-DQB1*06:01:01050100150200Created with MultiQC

        Fragment length hist

        Distribution of estimated fragment lengths of mapped reads per read group. Only points supported by at least 5 reads are shown to prevent long flat tail. The plot is also smoothed down to showing 300 points on the X axis to reduce noise.

        Created with Highcharts 5.0.6Fragment length (bp)Number of readsChart context menuExport PlotDragen: Fragment length hist0501001502002503003504004505005506006500100200300400500600700Created with MultiQC

        Trimmer Metrics

        Metrics on trimmed reads.

        Showing 7/7 rows and 22/22 columns.
        Sample NameTotal input readsTotal input basesTotal input bases R1Total input bases R2Average input read lengthTotal trimmed readsTotal trimmed basesAverage bases trimmed per readAverage bases trimmed per trimmed readRemaining poly-G K-mers R1 3primeRemaining poly-G K-mers R2 3primePoly-G soft trimmed reads unfiltered R1 3primePoly-G soft trimmed reads unfiltered R2 3primePoly-G soft trimmed reads filtered R1 3primePoly-G soft trimmed reads filtered R2 3primePoly-G soft trimmed bases unfiltered R1 3primePoly-G soft trimmed bases unfiltered R2 3primePoly-G soft trimmed bases filtered R1 3primePoly-G soft trimmed bases filtered R2 3primeTotal filtered readsReads filtered for minimum read length R1Reads filtered for minimum read length R2
        20012D-115-01
        119827730.0
        18093987230.0
        9046993615.0
        9046993615.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        11969.0 (0.02%)
        215850.0 (0.36%)
        795150.0 (1.33%)
        431186.0 (0.72%)
        0.0 (0.00%)
        0.0 (0.00%)
        11735312.0 (0.13%)
        49264732.0 (0.54%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        20012D-115-02
        121161170.0
        18295336670.0
        9147668335.0
        9147668335.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        22679.0 (0.04%)
        225557.0 (0.37%)
        946582.0 (1.56%)
        487562.0 (0.80%)
        0.0 (0.00%)
        0.0 (0.00%)
        15164771.0 (0.17%)
        52590382.0 (0.57%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        20012D-115-03
        101309120.0
        15297677120.0
        7648838560.0
        7648838560.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        35717.0 (0.07%)
        176758.0 (0.35%)
        1126356.0 (2.22%)
        376048.0 (0.74%)
        0.0 (0.00%)
        0.0 (0.00%)
        19083333.0 (0.25%)
        41191675.0 (0.54%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        20012D-115-04
        103807162.0
        15674881462.0
        7837440731.0
        7837440731.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        7953.0 (0.02%)
        120784.0 (0.23%)
        226112.0 (0.44%)
        297121.0 (0.57%)
        0.0 (0.00%)
        0.0 (0.00%)
        3161906.0 (0.04%)
        28414583.0 (0.36%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        20012D-115-05
        121258376.0
        18310014776.0
        9155007388.0
        9155007388.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        8536.0 (0.01%)
        225476.0 (0.37%)
        236937.0 (0.39%)
        444087.0 (0.73%)
        0.0 (0.00%)
        0.0 (0.00%)
        3499942.0 (0.04%)
        51426040.0 (0.56%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        20012D-115-06
        105037410.0
        15860648910.0
        7930324455.0
        7930324455.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        27511.0 (0.05%)
        247830.0 (0.47%)
        841935.0 (1.60%)
        452103.0 (0.86%)
        0.0 (0.00%)
        0.0 (0.00%)
        13910589.0 (0.18%)
        56407394.0 (0.71%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        20012D-115-07
        118060290.0
        17827103790.0
        8913551895.0
        8913551895.0
        151.0
        0.0 (0.00%)
        0.0 (0.00%)
        0.0
        0.0
        12703.0 (0.02%)
        215256.0 (0.36%)
        784646.0 (1.33%)
        427888.0 (0.72%)
        0.0 (0.00%)
        0.0 (0.00%)
        11804927.0 (0.13%)
        49289180.0 (0.55%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)
        0.0 (0.00%)

        Time Metrics

        Time metrics for DRAGEN run. Total run time is less than the sum of individual steps because of parallelization.

           
        Created with Highcharts 5.0.6Time (minutes)Chart context menuExport PlotDragen: Time MetricsTotal runtime20012D-115-0120012D-115-0220012D-115-0320012D-115-0420012D-115-0520012D-115-0620012D-115-0700.250.50.7511.251.51.7522.252.52.7533.253.53.7544.254.54.7555.255.55.7566.256.56.7577.257.57.758Created with MultiQC

        DRAGEN-FastQc

        DRAGEN-FastQc is a Bio-IT Platform that provides ultra-rapid secondary analysis of sequencing data using field-programmable gate array technology (FPGA).

        Per-Position Quality Score Ranges

        The range of quality value across each base position in each sample or read

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per-Position Mean Quality Scores

        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 Highcharts 5.0.6Position (bp)Phred Quality ScoreChart context menuExport PlotDRAGEN-QC: Per-Position Quality Scores01020304050607080901001101201301401500510152025303540Created 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 Highcharts 5.0.6Mean Sequence Quality (Phred Quality Score)CountChart context menuExport PlotDRAGEN-QC: Per-Sequence Quality Scores0246810121416182022242628303234363801000000020000000300000004000000050000000Created with MultiQC

        Sequence Length Distribution

        All samples have sequences within a single length bin (151bp).

        Per-Sequence GC Content

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

        From the FastQC help: This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with Highcharts 5.0.6% GCCountChart context menuExport PlotDRAGEN-QC: Per-Sequence GC Content0510152025303540455055606570758085909510001234Created with MultiQC

        GC Content Mean Quality Scores

        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 Highcharts 5.0.6% GCPhred Quality ScoreChart context menuExport PlotDRAGEN-QC: GC Content Mean Quality Scores051015202530354045505560657075808590951000510152025303540Created with MultiQC

        Per-Position N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help: If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with Highcharts 5.0.6Position in Read (bp)Percentage N-CountChart context menuExport PlotDRAGEN-QC: Per-Position N Content01020304050607080901001101201301401500123456Created with MultiQC

        Per-Position Sequence Content
        1
        0
        0

        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: -

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with Highcharts 5.0.6Position (bp)% of SequencesChart context menuExport PlotFastQC: Adapter Content010203040506070809010011012013014002.557.51012.51517.5Created with MultiQC

        Picard

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

        HSMetrics

        Showing 7/7 rows and 22/27 columns.
        Sample NameBait design efficiencyFold 80 base penaltyFold enrichmentHet SNP qHet SNP sensitivityMax target coverageMean bait coverageMean target coverageMedian target coverageMb Near-bait basesMb Off-bait basesMb On-bait basesOn-bait vs selectedMb On-target bases% Usable bases on-bait% Usable bases on-targetMb PF bases alignedM PF readsM PF unique readsMb PF unique bases alignedM PF unique reads aligned% Zero coverage targets
        20012D-115-01
        0.8
        1.6
        50.7
        17.0
        1.0
        1428.0
        273.7
        180.8
        174.0
        4829.3
        960.4
        11585.8
        0.7
        6173.5
        64.0%
        34.1%
        17375.5
        119.8
        108.9
        15813.8
        108.5
        1.3%
        20012D-115-02
        0.8
        1.6
        50.4
        17.0
        1.0
        1219.0
        275.3
        182.9
        176.0
        4962.8
        961.5
        11652.7
        0.7
        6246.6
        63.7%
        34.1%
        17577.0
        121.2
        109.5
        15904.6
        109.1
        1.2%
        20012D-115-03
        0.8
        1.6
        51.2
        17.0
        1.0
        1139.0
        232.9
        152.4
        147.0
        3975.9
        797.5
        9855.9
        0.7
        5205.9
        64.4%
        34.0%
        14629.3
        101.3
        92.8
        13418.5
        92.4
        1.4%
        20012D-115-04
        0.8
        1.6
        51.3
        17.0
        1.0
        1986.0
        239.2
        154.7
        149.0
        4057.3
        820.8
        10123.1
        0.7
        5282.0
        64.6%
        33.7%
        15001.2
        103.8
        94.9
        13718.2
        94.6
        1.3%
        20012D-115-05
        0.8
        1.6
        50.5
        17.0
        1.0
        1167.0
        276.6
        183.8
        177.0
        4960.4
        947.7
        11706.5
        0.7
        6278.1
        63.9%
        34.3%
        17614.7
        121.3
        110.1
        16021.9
        109.8
        1.2%
        20012D-115-06
        0.8
        1.6
        50.2
        17.0
        1.0
        1113.0
        238.4
        161.4
        155.0
        4361.0
        835.1
        10089.1
        0.7
        5512.0
        63.6%
        34.8%
        15285.2
        105.0
        96.1
        14016.4
        95.7
        1.3%
        20012D-115-07
        0.8
        1.6
        50.5
        17.0
        1.0
        1478.0
        269.5
        179.0
        173.0
        4821.5
        931.2
        11407.7
        0.7
        6111.3
        64.0%
        34.3%
        17160.5
        118.1
        107.4
        15639.1
        107.1
        1.3%

        Target Region Coverage

        The percentage of all target bases with at least x fold coverage.

        Created with Highcharts 5.0.6Fold CoveragePct of basesChart context menuExport PlotPicard: Percentage of target bases05000100001500020000250003000035000400004500050000550006000065000700007500080000850009000095000100000020406080100Created with MultiQC

        HS Penalty

        The "hybrid selection penalty" incurred to get 80% of target bases to a given coverage.

        Can be used with the following formula:

        required_aligned_bases = bait_size_bp * desired_coverage * hs_penalty

        Created with Highcharts 5.0.6Fold CoveragePenaltyChart context menuExport PlotPicard: Hybrid Selection Penalty05101520253035404550556065707580859095100012345Created with MultiQC