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        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.


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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 1.10.1

        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 2022-03-31, 10:28 based on data in:


        General Statistics

        Showing 38/38 rows and 17/28 columns.
        Sample NameM Reads Mapped% GCIns. size≥ 30XMedian covMean cov% Aligned% DupsError rateM Non-PrimaryM Reads Mapped% Mapped% Proper PairsM Total seqs% Dups% GCM Seqs
        NanoMB1Rep1_HJK2GDSX3_CGGCTAAT-CTCGTTCT_L001
        324.7
        50%
        294
        100.0%
        9166.0X
        9922.5X
        99.3%
        79.7%
        0.52%
        0.0
        324.6
        99.3%
        98.7%
        326.9
        77.0%
        49%
        163.4
        NanoMB1Rep1_HJK2GDSX3_CGGCTAAT-CTCGTTCT_L001_R2
        74.1%
        49%
        163.4
        NanoMB1Rep2_HJK2GDSX3_ATCGATCG-TGGAAGCA_L001
        273.4
        50%
        311
        100.0%
        7782.0X
        8349.9X
        99.2%
        72.2%
        0.52%
        0.0
        273.4
        99.2%
        98.7%
        275.6
        70.6%
        49%
        137.8
        NanoMB1Rep2_HJK2GDSX3_ATCGATCG-TGGAAGCA_L001_R2
        67.6%
        49%
        137.8
        NanoMB2Rep1_HJK2GDSX3_GCAAGATC-AGTCGAAG_L001
        293.1
        50%
        303
        100.0%
        8335.0X
        8957.0X
        99.3%
        75.8%
        0.51%
        0.0
        293.0
        99.3%
        98.7%
        295.1
        73.5%
        49%
        147.6
        NanoMB2Rep1_HJK2GDSX3_GCAAGATC-AGTCGAAG_L001_R2
        71.4%
        49%
        147.6
        NanoMB2Rep2_HJK2GDSX3_GCTATCCT-AACAGGTG_L001
        245.8
        50%
        271
        100.0%
        6947.0X
        7508.0X
        99.3%
        72.6%
        0.52%
        0.0
        245.8
        99.3%
        98.7%
        247.5
        70.5%
        49%
        123.7
        NanoMB2Rep2_HJK2GDSX3_GCTATCCT-AACAGGTG_L001_R2
        67.8%
        49%
        123.7
        NanoMB2_S1Rep1_HJK2GDSX3_AGAGTAGC-AAGGCGTA_L001
        236.7
        50%
        299
        100.0%
        6730.0X
        7224.7X
        99.3%
        53.2%
        0.54%
        0.0
        236.6
        99.3%
        98.6%
        238.2
        55.8%
        49%
        119.1
        NanoMB2_S1Rep1_HJK2GDSX3_AGAGTAGC-AAGGCGTA_L001_R2
        52.7%
        49%
        119.1
        NanoMB2_S1Rep2_HJK2GDSX3_ATCCAGAG-TAACGTCG_L001
        233.9
        50%
        307
        100.0%
        6635.0X
        7143.9X
        99.3%
        61.3%
        0.55%
        0.0
        233.9
        99.3%
        98.5%
        235.5
        61.9%
        49%
        117.8
        NanoMB2_S1Rep2_HJK2GDSX3_ATCCAGAG-TAACGTCG_L001_R2
        58.1%
        49%
        117.8
        NanoMB3Rep1_HJK2GDSX3_TACGCTAC-CGTGTGAT_L001
        282.3
        50%
        279
        100.0%
        8140.0X
        8622.1X
        99.2%
        76.0%
        0.51%
        0.0
        282.2
        99.2%
        98.5%
        284.5
        73.9%
        49%
        142.3
        NanoMB3Rep1_HJK2GDSX3_TACGCTAC-CGTGTGAT_L001_R2
        71.7%
        49%
        142.3
        NanoMB3Rep2_HJK2GDSX3_TGGACTCT-TCTTACGG_L001
        295.5
        50%
        291
        100.0%
        8406.0X
        9026.1X
        99.4%
        77.3%
        0.52%
        0.0
        295.4
        99.4%
        98.7%
        297.3
        74.8%
        49%
        148.7
        NanoMB3Rep2_HJK2GDSX3_TGGACTCT-TCTTACGG_L001_R2
        72.6%
        49%
        148.7
        xGEN-xGENRep1_HJK2GDSX3_GTGCCATA-CTCCTAGT_L001
        154.6
        51%
        290
        100.0%
        4476.0X
        4621.4X
        99.7%
        6.5%
        0.58%
        0.0
        154.6
        99.7%
        99.4%
        155.0
        22.7%
        50%
        77.9
        xGEN-xGENRep1_HJK2GDSX3_GTGCCATA-CTCCTAGT_L001_R2
        19.9%
        50%
        77.9
        xGEN-xGENRep2_HJK2GDSX3_GACGATCT-TCGTGCAT_L001
        252.5
        52%
        272
        100.0%
        7306.0X
        7539.1X
        99.8%
        77.9%
        0.51%
        0.0
        252.4
        99.8%
        99.4%
        253.0
        73.5%
        50%
        127.2
        xGEN-xGENRep2_HJK2GDSX3_GACGATCT-TCGTGCAT_L001_R2
        71.6%
        50%
        127.2
        xGEN-xGENRep3_HJK2GDSX3_AACTGAGC-CAATCAGG_L001
        227.2
        51%
        289
        100.0%
        6579.0X
        6792.9X
        99.7%
        70.2%
        0.57%
        0.0
        227.2
        99.7%
        99.4%
        227.8
        66.9%
        50%
        114.5
        xGEN-xGENRep3_HJK2GDSX3_AACTGAGC-CAATCAGG_L001_R2
        63.4%
        50%
        114.5
        xGENMB1Rep1_HJK2GDSX3_CTGATCGT-GCGCATAT_L001
        258.2
        52%
        268
        100.0%
        7481.0X
        7717.6X
        99.7%
        78.1%
        0.52%
        0.0
        258.2
        99.7%
        99.4%
        258.8
        73.6%
        51%
        130.2
        xGENMB1Rep1_HJK2GDSX3_CTGATCGT-GCGCATAT_L001_R2
        71.4%
        51%
        130.2
        xGENMB1Rep2_HJK2GDSX3_ACTCTCGA-CTGTACCA_L001
        220.5
        52%
        306
        100.0%
        6386.0X
        6590.1X
        99.7%
        75.2%
        0.57%
        0.0
        220.5
        99.7%
        99.4%
        221.1
        71.0%
        51%
        111.3
        xGENMB1Rep2_HJK2GDSX3_ACTCTCGA-CTGTACCA_L001_R2
        66.9%
        51%
        111.3
        xGENMB2Rep1_HJK2GDSX3_TGAGCTAG-GAACGGTT_L001
        239.1
        52%
        279
        100.0%
        6932.0X
        7148.9X
        99.8%
        72.8%
        0.51%
        0.0
        239.1
        99.8%
        99.4%
        239.7
        69.5%
        51%
        120.6
        xGENMB2Rep1_HJK2GDSX3_TGAGCTAG-GAACGGTT_L001_R2
        66.8%
        51%
        120.6
        xGENMB2Rep2_HJK2GDSX3_GAGACGAT-ACCGGTTA_L001
        320.7
        52%
        279
        100.0%
        9300.0X
        9590.0X
        99.8%
        79.3%
        0.51%
        0.0
        320.6
        99.8%
        99.4%
        321.4
        75.1%
        51%
        161.7
        xGENMB2Rep2_HJK2GDSX3_GAGACGAT-ACCGGTTA_L001_R2
        73.1%
        51%
        161.7
        xGENMB2_S1Rep1_HJK2GDSX3_CGCATGAT-AAGCCTGA_L001
        272.0
        52%
        285
        100.0%
        7886.0X
        8131.2X
        99.7%
        79.4%
        0.54%
        0.0
        271.9
        99.7%
        99.3%
        272.7
        75.2%
        51%
        137.2
        xGENMB2_S1Rep1_HJK2GDSX3_CGCATGAT-AAGCCTGA_L001_R2
        72.2%
        51%
        137.2
        xGENMB2_S1Rep2_HJK2GDSX3_ACGGAACA-ACGAGAAC_L001
        148.2
        52%
        288
        100.0%
        4300.0X
        4425.6X
        99.7%
        69.9%
        0.55%
        0.0
        148.2
        99.7%
        99.3%
        148.6
        66.2%
        51%
        74.8
        xGENMB2_S1Rep2_HJK2GDSX3_ACGGAACA-ACGAGAAC_L001_R2
        63.3%
        51%
        74.8
        xGENMB3Rep1_HJK2GDSX3_CTTGTCGA-CGATGTTC_L001
        237.8
        52%
        284
        100.0%
        6890.0X
        7107.6X
        99.8%
        73.8%
        0.53%
        0.0
        237.8
        99.8%
        99.3%
        238.3
        70.5%
        51%
        119.9
        xGENMB3Rep1_HJK2GDSX3_CTTGTCGA-CGATGTTC_L001_R2
        67.6%
        51%
        119.9
        xGENMB3Rep2_HJK2GDSX3_TTCCAAGG-CTACAAGG_L001
        206.5
        52%
        265
        100.0%
        5974.0X
        6166.7X
        99.7%
        72.1%
        0.54%
        0.0
        206.4
        99.7%
        99.3%
        206.9
        68.1%
        51%
        104.2
        xGENMB3Rep2_HJK2GDSX3_TTCCAAGG-CTACAAGG_L001_R2
        65.7%
        51%
        104.2

        QualiMap

        QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

        Coverage histogram

        Distribution of the 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).

        loading..

        Cumulative genome coverage

        Percentage of the reference genome with at least the 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.

        loading..

        Insert size histogram

        Distribution of estimated insert sizes of mapped reads.

        To overcome limitations in the length of DNA or RNA sequencing reads, many sequencing instruments can produce two or more shorter reads from one longer fragment in which the relative position of reads is approximately known, such as paired-end or mate-pair reads (Mardis 2013). Such techniques can extend the reach of sequencing technology, allowing for more accurate placement of reads (Reinert et al. 2015) and better resolution of repeat regions (Reinert et al. 2015), as well as detection of structural variation (Alkan et al. 2011) and chimeric transcripts (Maher et al. 2009).

        All these methods assume that the approximate size of an insert is known. (Insert size can be defined as the length in bases of a sequenced DNA or RNA fragment, excluding technical sequences such as adapters, which are typically removed before alignment.) This plot allows for that assumption to be assessed. With the set of mapped fragments for a given sample, QualiMap groups the fragments by insert size, then plots the frequency of mapped fragments (y-axis) over a range of insert sizes (x-axis). In an ideal case, the distribution of fragment sizes for a sequencing library would culminate in a single peak indicating average insert size, with a narrow spread indicating highly consistent fragment lengths.

        QualiMap calculates insert sizes as follows: for each fragment in which every read mapped successfully to the same reference sequence, it extracts the insert size from the TLEN field of the leftmost read (see the Qualimap 2 documentation), where the TLEN (or 'observed Template LENgth') field contains 'the number of bases from the leftmost mapped base to the rightmost mapped base' (SAM format specification). Note that because it is defined in terms of alignment to a reference sequence, the value of the TLEN field may differ from the insert size due to factors such as alignment clipping, alignment errors, or structural variation or splicing in a gap between reads from the same fragment.

        loading..

        GC content distribution

        Each solid line represents the distribution of GC content of mapped reads for a given sample.

        GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).

        QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).

        loading..

        Picard

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

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        loading..

        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.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        loading..

        Alignment metrics

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

        loading..

        Samtools Flagstat

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

        loading..

        FastQ Screen

        FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.

        Mapped Reads

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        Sequence Length Distribution

        All samples have sequences of a single length (150bp).

        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.

        loading..

        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.

        38 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.

        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.

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        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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