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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 1.9

        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 2022-08-22, 20:28 based on data in: /scratch/gencore/logs/html/000000000-KG266/1


        General Statistics

        Showing 388/388 rows and 3/5 columns.
        Sample Name% Dups% GCM Seqs
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        1.5%
        52%
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        48%
        0.0
        000000000-KG266_l01_n02_Tr4_C7I2a_T1_rc
        0.7%
        49%
        0.1
        000000000-KG266_l01_n02_Tr4_C7I2b_T1_rc
        1.3%
        50%
        0.1
        000000000-KG266_l01_n02_Tr4_C7I2c_T1_rc
        0.4%
        49%
        0.1
        000000000-KG266_l01_n02_Tr4_C7I3a_T1_rc
        1.0%
        49%
        0.1
        000000000-KG266_l01_n02_Tr4_C7I3b_T1_rc
        1.5%
        48%
        0.1
        000000000-KG266_l01_n02_Tr4_C7I3c_T1_rc
        0.8%
        48%
        0.0
        000000000-KG266_l01_n02_Tr4_C7I4a_T1_rc
        3.2%
        47%
        0.2
        000000000-KG266_l01_n02_Tr4_C7I4b_T1_rc
        2.9%
        49%
        0.2
        000000000-KG266_l01_n02_Tr4_C7I4c_T1_rc
        1.1%
        49%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I1a_T1_rc
        1.4%
        50%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I1b_T1_rc
        0.4%
        50%
        0.0
        000000000-KG266_l01_n02_Tr4_C8I1c_T1_rc
        0.4%
        50%
        0.0
        000000000-KG266_l01_n02_Tr4_C8I2a_T1_rc
        0.7%
        50%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I2b_T1_rc
        1.8%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I2c_T1_rc
        0.1%
        53%
        0.0
        000000000-KG266_l01_n02_Tr4_C8I3a_T1_rc
        0.9%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I3b_T1_rc
        1.2%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I3c_T1_rc
        0.7%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I4a_T1_rc
        1.1%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I4b_T1_rc
        1.2%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C8I4c_T1_rc
        1.3%
        53%
        0.1
        000000000-KG266_l01_n02_Tr4_C9I1a_T1_rc
        1.2%
        50%
        0.1
        000000000-KG266_l01_n02_Tr4_C9I1b_T1_rc
        0.5%
        51%
        0.0
        000000000-KG266_l01_n02_Tr4_C9I1c_T1_rc
        0.6%
        50%
        0.0
        000000000-KG266_l01_n02_Tr4_C9I2a_T1_rc
        0.7%
        51%
        0.0
        000000000-KG266_l01_n02_Tr4_C9I2b_T1_rc
        1.5%
        51%
        0.1
        000000000-KG266_l01_n02_Tr4_C9I2c_T1_rc
        0.3%
        52%
        0.0
        000000000-KG266_l01_n02_Tr4_C9I3a_T1_rc
        0.7%
        52%
        0.0
        000000000-KG266_l01_n02_Tr4_C9I3b_T1_rc
        1.1%
        50%
        0.1
        000000000-KG266_l01_n02_Tr4_C9I3c_T1_rc
        0.5%
        51%
        0.0
        000000000-KG266_l01_n02_Tr4_C9I4a_T1_rc
        2.0%
        50%
        0.1
        000000000-KG266_l01_n02_Tr4_C9I4b_T1_rc
        1.6%
        52%
        0.1
        000000000-KG266_l01_n02_Tr4_C9I4c_T1_rc
        1.7%
        52%
        0.1
        000000000-KG266_l01_n02_Tr4_NCI1a_T1_rc
        0.4%
        56%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI1b_T1_rc
        0.1%
        54%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI1c_T1_rc
        0.0%
        50%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI2a_T1_rc
        0.4%
        55%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI2b_T1_rc
        1.1%
        56%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI2c_T1_rc
        0.1%
        56%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI3a_T1_rc
        0.5%
        54%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI3b_T1_rc
        0.9%
        49%
        0.1
        000000000-KG266_l01_n02_Tr4_NCI3c_T1_rc
        0.4%
        49%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI4a_T1_rc
        0.9%
        56%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI4b_T1_rc
        0.7%
        53%
        0.0
        000000000-KG266_l01_n02_Tr4_NCI4c_T1_rc
        1.1%
        56%
        0.1
        000000000-KG266_l01_n02_undetermined
        33.6%
        49%
        1.8

        Lane 1 Demultiplexing Report


        Total Read Count: Total number of PF (Passing Filter) reads in this library.
        Portion: The proportion of reads that represent the individual library in the entire Library Pool

        Showing 194/194 rows and 2/2 columns.
        LibraryTotal Read CountPortion (%)
        undetermined_library
        1843725
        13.4
        Tr4_C1I1a_T1_rc
        85643
        0.6
        Tr4_C2I1a_T1_rc
        157562
        1.1
        Tr4_C3I1a_T1_rc
        87138
        0.6
        Tr4_C4I1a_T1_rc
        54312
        0.4
        Tr4_C5I1a_T1_rc
        91731
        0.7
        Tr4_C6I1a_T1_rc
        69022
        0.5
        Tr4_C7I1a_T1_rc
        167548
        1.2
        Tr4_C8I1a_T1_rc
        95916
        0.7
        Tr4_C1I1b_T1_rc
        63851
        0.5
        Tr4_C2I1b_T1_rc
        27848
        0.2
        Tr4_C3I1b_T1_rc
        46992
        0.3
        Tr4_C4I1b_T1_rc
        15905
        0.1
        Tr4_C5I1b_T1_rc
        25761
        0.2
        Tr4_C6I1b_T1_rc
        15752
        0.1
        Tr4_C7I1b_T1_rc
        58023
        0.4
        Tr4_C8I1b_T1_rc
        32680
        0.2
        Tr4_C1I1c_T1_rc
        60245
        0.4
        Tr4_C2I1c_T1_rc
        34894
        0.3
        Tr4_C3I1c_T1_rc
        8587
        0.1
        Tr4_C4I1c_T1_rc
        1926
        0.0
        Tr4_C5I1c_T1_rc
        36838
        0.3
        Tr4_C6I1c_T1_rc
        15671
        0.1
        Tr4_C7I1c_T1_rc
        29659
        0.2
        Tr4_C8I1c_T1_rc
        30961
        0.2
        Tr4_C1I2a_T1_rc
        27140
        0.2
        Tr4_C2I2a_T1_rc
        30923
        0.2
        Tr4_C3I2a_T1_rc
        39524
        0.3
        Tr4_C4I2a_T1_rc
        13806
        0.1
        Tr4_C5I2a_T1_rc
        48502
        0.4
        Tr4_C6I2a_T1_rc
        2151
        0.0
        Tr4_C7I2a_T1_rc
        57189
        0.4
        Tr4_C8I2a_T1_rc
        53360
        0.4
        Tr4_C1I2b_T1_rc
        114855
        0.8
        Tr4_C2I2b_T1_rc
        89570
        0.7
        Tr4_C3I2b_T1_rc
        105901
        0.8
        Tr4_C4I2b_T1_rc
        99141
        0.7
        Tr4_C5I2b_T1_rc
        115379
        0.8
        Tr4_C6I2b_T1_rc
        114813
        0.8
        Tr4_C7I2b_T1_rc
        110326
        0.8
        Tr4_C8I2b_T1_rc
        129901
        0.9
        Tr4_C1I2c_T1_rc
        28214
        0.2
        Tr4_C2I2c_T1_rc
        22536
        0.2
        Tr4_C3I2c_T1_rc
        16036
        0.1
        Tr4_C4I2c_T1_rc
        15347
        0.1
        Tr4_C5I2c_T1_rc
        29024
        0.2
        Tr4_C6I2c_T1_rc
        21554
        0.2
        Tr4_C7I2c_T1_rc
        51244
        0.4
        Tr4_C8I2c_T1_rc
        17176
        0.1
        Tr4_C1I3a_T1_rc
        56651
        0.4
        Tr4_C2I3a_T1_rc
        64910
        0.5
        Tr4_C3I3a_T1_rc
        74401
        0.5
        Tr4_C4I3a_T1_rc
        61076
        0.4
        Tr4_C5I3a_T1_rc
        144110
        1.0
        Tr4_C6I3a_T1_rc
        93049
        0.7
        Tr4_C7I3a_T1_rc
        76685
        0.6
        Tr4_C8I3a_T1_rc
        65105
        0.5
        Tr4_C1I3b_T1_rc
        68979
        0.5
        Tr4_C2I3b_T1_rc
        111183
        0.8
        Tr4_C3I3b_T1_rc
        108082
        0.8
        Tr4_C4I3b_T1_rc
        73454
        0.5
        Tr4_C5I3b_T1_rc
        103993
        0.8
        Tr4_C6I3b_T1_rc
        128501
        0.9
        Tr4_C7I3b_T1_rc
        100777
        0.7
        Tr4_C8I3b_T1_rc
        82658
        0.6
        Tr4_C1I3c_T1_rc
        23473
        0.2
        Tr4_C2I3c_T1_rc
        33614
        0.2
        Tr4_C3I3c_T1_rc
        34952
        0.3
        Tr4_C4I3c_T1_rc
        10644
        0.1
        Tr4_C5I3c_T1_rc
        52575
        0.4
        Tr4_C6I3c_T1_rc
        21914
        0.2
        Tr4_C7I3c_T1_rc
        47258
        0.3
        Tr4_C8I3c_T1_rc
        59613
        0.4
        Tr4_C1I4a_T1_rc
        100258
        0.7
        Tr4_C2I4a_T1_rc
        112159
        0.8
        Tr4_C3I4a_T1_rc
        107916
        0.8
        Tr4_C4I4a_T1_rc
        71085
        0.5
        Tr4_C5I4a_T1_rc
        160383
        1.2
        Tr4_C6I4a_T1_rc
        173533
        1.3
        Tr4_C7I4a_T1_rc
        155138
        1.1
        Tr4_C8I4a_T1_rc
        63908
        0.5
        Tr4_C1I4b_T1_rc
        113006
        0.8
        Tr4_C2I4b_T1_rc
        90166
        0.7
        Tr4_C3I4b_T1_rc
        106167
        0.8
        Tr4_C4I4b_T1_rc
        79130
        0.6
        Tr4_C5I4b_T1_rc
        91496
        0.7
        Tr4_C6I4b_T1_rc
        146421
        1.1
        Tr4_C7I4b_T1_rc
        169902
        1.2
        Tr4_C8I4b_T1_rc
        100044
        0.7
        Tr4_C1I4c_T1_rc
        105056
        0.8
        Tr4_C2I4c_T1_rc
        105319
        0.8
        Tr4_C3I4c_T1_rc
        157254
        1.1
        Tr4_C4I4c_T1_rc
        72913
        0.5
        Tr4_C5I4c_T1_rc
        136461
        1.0
        Tr4_C6I4c_T1_rc
        59776
        0.4
        Tr4_C7I4c_T1_rc
        92416
        0.7
        Tr4_C8I4c_T1_rc
        99638
        0.7
        Tr4_C9I1a_T1_rc
        84834
        0.6
        Tr4_C10I1a_T1_rc
        42850
        0.3
        Tr4_C11I1a_T1_rc
        9440
        0.1
        Tr4_C12I1a_T1_rc
        19522
        0.1
        Tr4_C13I1a_T1_rc
        60901
        0.4
        Tr4_C14I1a_T1_rc
        57045
        0.4
        Tr4_C15I1a_T1_rc
        61676
        0.4
        Tr4_NCI1a_T1_rc
        4667
        0.0
        Tr4_C9I1b_T1_rc
        25516
        0.2
        Tr4_C10I1b_T1_rc
        50187
        0.4
        Tr4_C11I1b_T1_rc
        69944
        0.5
        Tr4_C12I1b_T1_rc
        141803
        1.0
        Tr4_C13I1b_T1_rc
        11816
        0.1
        Tr4_C14I1b_T1_rc
        83235
        0.6
        Tr4_C15I1b_T1_rc
        63997
        0.5
        Tr4_NCI1b_T1_rc
        5435
        0.0
        Tr4_C9I1c_T1_rc
        38714
        0.3
        Tr4_C10I1c_T1_rc
        38667
        0.3
        Tr4_C11I1c_T1_rc
        13956
        0.1
        Tr4_C12I1c_T1_rc
        17868
        0.1
        Tr4_C13I1c_T1_rc
        16368
        0.1
        Tr4_C14I1c_T1_rc
        23928
        0.2
        Tr4_C15I1c_T1_rc
        5695
        0.0
        Tr4_NCI1c_T1_rc
        157.0
        0.0
        Tr4_C9I2a_T1_rc
        44814
        0.3
        Tr4_C10I2a_T1_rc
        37326
        0.3
        Tr4_C11I2a_T1_rc
        18331
        0.1
        Tr4_C12I2a_T1_rc
        27070
        0.2
        Tr4_C13I2a_T1_rc
        33327
        0.2
        Tr4_C14I2a_T1_rc
        14764
        0.1
        Tr4_C15I2a_T1_rc
        6459
        0.0
        Tr4_NCI2a_T1_rc
        9852
        0.1
        Tr4_C9I2b_T1_rc
        96644
        0.7
        Tr4_C10I2b_T1_rc
        105327
        0.8
        Tr4_C11I2b_T1_rc
        87920
        0.6
        Tr4_C12I2b_T1_rc
        55803
        0.4
        Tr4_C13I2b_T1_rc
        58139
        0.4
        Tr4_C14I2b_T1_rc
        94072
        0.7
        Tr4_C15I2b_T1_rc
        104893
        0.8
        Tr4_NCI2b_T1_rc
        47362
        0.3
        Tr4_C9I2c_T1_rc
        22641
        0.2
        Tr4_C10I2c_T1_rc
        13111
        0.1
        Tr4_C11I2c_T1_rc
        6932
        0.1
        Tr4_C12I2c_T1_rc
        8072
        0.1
        Tr4_C13I2c_T1_rc
        12884
        0.1
        Tr4_C14I2c_T1_rc
        8673
        0.1
        Tr4_C15I2c_T1_rc
        19577
        0.1
        Tr4_NCI2c_T1_rc
        4738
        0.0
        Tr4_C9I3a_T1_rc
        41043
        0.3
        Tr4_C10I3a_T1_rc
        33288
        0.2
        Tr4_C11I3a_T1_rc
        51132
        0.4
        Tr4_C12I3a_T1_rc
        6268
        0.0
        Tr4_C13I3a_T1_rc
        51646
        0.4
        Tr4_C14I3a_T1_rc
        34127
        0.2
        Tr4_C15I3a_T1_rc
        67343
        0.5
        Tr4_NCI3a_T1_rc
        16732
        0.1
        Tr4_C9I3b_T1_rc
        71264
        0.5
        Tr4_C10I3b_T1_rc
        57732
        0.4
        Tr4_C11I3b_T1_rc
        77660
        0.6
        Tr4_C12I3b_T1_rc
        48168
        0.4
        Tr4_C13I3b_T1_rc
        76560
        0.6
        Tr4_C14I3b_T1_rc
        64749
        0.5
        Tr4_C15I3b_T1_rc
        70691
        0.5
        Tr4_NCI3b_T1_rc
        50567
        0.4
        Tr4_C9I3c_T1_rc
        29619
        0.2
        Tr4_C10I3c_T1_rc
        22263
        0.2
        Tr4_C11I3c_T1_rc
        20456
        0.1
        Tr4_C12I3c_T1_rc
        23144
        0.2
        Tr4_C13I3c_T1_rc
        22491
        0.2
        Tr4_C14I3c_T1_rc
        23464
        0.2
        Tr4_C15I3c_T1_rc
        19610
        0.1
        Tr4_NCI3c_T1_rc
        18093
        0.1
        Tr4_C9I4a_T1_rc
        119863
        0.9
        Tr4_C10I4a_T1_rc
        73295
        0.5
        Tr4_C11I4a_T1_rc
        74441
        0.5
        Tr4_C12I4a_T1_rc
        95666
        0.7
        Tr4_C13I4a_T1_rc
        91874
        0.7
        Tr4_C14I4a_T1_rc
        40940
        0.3
        Tr4_C15I4a_T1_rc
        104317
        0.8
        Tr4_NCI4a_T1_rc
        32343
        0.2
        Tr4_C9I4b_T1_rc
        108400
        0.8
        Tr4_C10I4b_T1_rc
        92007
        0.7
        Tr4_C11I4b_T1_rc
        69462
        0.5
        Tr4_C12I4b_T1_rc
        89001
        0.6
        Tr4_C13I4b_T1_rc
        89633
        0.7
        Tr4_C14I4b_T1_rc
        41084
        0.3
        Tr4_C15I4b_T1_rc
        77804
        0.6
        Tr4_NCI4b_T1_rc
        48608
        0.4
        Tr4_C9I4c_T1_rc
        114187
        0.8
        Tr4_C10I4c_T1_rc
        95631
        0.7
        Tr4_C11I4c_T1_rc
        61894
        0.5
        Tr4_C12I4c_T1_rc
        72089
        0.5
        Tr4_C13I4c_T1_rc
        89685
        0.7
        Tr4_C14I4c_T1_rc
        71144
        0.5
        Tr4_C15I4c_T1_rc
        107271
        0.8
        Tr4_NCI4c_T1_rc
        57961
        0.4
        MEJ082_rc
        51277
        0.4

        Lane Statistics

        Showing 1/1 rows and 4/4 columns.
        LaneTotal # of Single-End ReadsTotal # PF Reads% Undetermined% PhiX Aligned
        1.0
        14790728
        13733943
        13.4
        4.8

        Barcodes of Undetermined Reads


        We have determined the barcodes of your undetermined reads (reads containing a barcode that you did not encode in your metadata). Here are the top 20 barcodes belonging to the undetermined reads. The full list is available here.

        Showing 20/20 rows and 2/2 columns.
        Barcode Sequence(s)CountFrequency (%)
        CCTTAATATCTCCATA
        95138.0
        5.2
        GTCTGCTATCTTTCCC
        8463.0
        0.5
        GTCTATGATCTTTCCC
        7569.0
        0.4
        CGAGCGACTCTTTCCC
        6307.0
        0.3
        TATAGCGATCTTTCCC
        6229.0
        0.3
        TGAGTACGTCTTTCCC
        6165.0
        0.3
        TAGTCTCCTCTTTCCC
        5720.0
        0.3
        TCTTAATATCTCCATA
        5553.0
        0.3
        CGAGAGTTTCTTTCCC
        4600.0
        0.2
        TTCTTCTCTCTTTCCC
        4126.0
        0.2
        TCTCTCTCTCTTTCCC
        3347.0
        0.2
        CTCTCTCCTCTTTCCC
        3143.0
        0.2
        GTCTTCTATCTTTCCC
        3141.0
        0.2
        CTCTCTTTTCTTTCCC
        3080.0
        0.2
        TTCTCTTCTCTTTCCC
        2978.0
        0.2
        TCTTCTCCTCTTTCCC
        2775.0
        0.1
        TTCTTCTATCTTTCCC
        2754.0
        0.1
        TTCTTCCTTCTTTCCC
        2751.0
        0.1
        GTCTATTATCTTTCCC
        2582.0
        0.1
        ACTCACTGTCTTTCCC
        2360.0
        0.1

        FastQC

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

        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.

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


        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.

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


        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.

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


        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: -

        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.

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


        Per Base 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.

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


        Sequence Length Distribution

        All samples have sequences of a single length (251bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        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.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

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


        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

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


        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.

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


        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

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