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        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 1.17

        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.

        This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-10-04, 22:01 EDT based on data in: /scratch4/eande106/Mike/12/a664a65ab509c2728bd241a4b9919a


        General Statistics

        Showing 90/90 rows and 19/28 columns.
        Sample Name% Dups5'-3' biasM AlignedError rateM Non-PrimaryM Reads Mapped% Mapped% Proper PairsM Total seqsM Reads Mapped% AlignedM Aligned% Dups% GCM Seqs% BP Trimmed% Dups% GCM Seqs
        SRX298280
        44.2%
        1.83
        18.5
        0.54%
        7.2
        37.0
        100.0%
        99.8%
        37.0
        44.2
        80.4%
        17.0
        SRX298280_1
        74.4%
        51%
        21.5
        2.6%
        73.5%
        51%
        21.2
        SRX298280_2
        71.7%
        51%
        21.5
        6.1%
        71.2%
        50%
        21.2
        SRX298281
        37.6%
        1.85
        13.5
        0.53%
        4.9
        27.1
        100.0%
        99.8%
        27.1
        31.9
        80.5%
        12.5
        SRX298281_1
        71.0%
        50%
        15.8
        2.7%
        70.2%
        50%
        15.5
        SRX298281_2
        68.3%
        50%
        15.8
        6.0%
        68.1%
        50%
        15.5
        SRX298282
        55.3%
        1.78
        24.7
        0.54%
        9.6
        49.4
        100.0%
        99.8%
        49.4
        59.1
        80.1%
        22.7
        SRX298282_1
        78.2%
        51%
        28.9
        2.8%
        77.4%
        51%
        28.3
        SRX298282_2
        75.6%
        51%
        28.9
        6.2%
        75.1%
        51%
        28.3
        SRX298283
        37.8%
        1.90
        18.4
        0.51%
        4.2
        36.9
        100.0%
        99.8%
        36.9
        41.1
        77.1%
        17.7
        SRX298283_1
        72.9%
        46%
        23.4
        2.8%
        72.1%
        46%
        23.0
        SRX298283_2
        70.6%
        46%
        23.4
        5.7%
        70.0%
        46%
        23.0
        SRX298284
        41.6%
        1.89
        28.3
        0.52%
        7.5
        56.8
        100.0%
        99.8%
        56.8
        64.3
        78.7%
        27.1
        SRX298284_1
        75.4%
        47%
        35.1
        2.9%
        74.4%
        47%
        34.5
        SRX298284_2
        72.3%
        47%
        35.1
        6.6%
        71.4%
        47%
        34.5
        SRX298285
        34.9%
        1.87
        25.3
        0.52%
        9.3
        50.7
        100.0%
        99.8%
        50.7
        60.0
        77.9%
        23.7
        SRX298285_1
        73.3%
        48%
        31.1
        2.7%
        72.2%
        48%
        30.5
        SRX298285_2
        69.8%
        48%
        31.1
        6.7%
        69.0%
        48%
        30.5
        SRX298286
        40.6%
        2.33
        47.0
        0.47%
        10.0
        94.2
        100.0%
        99.9%
        94.2
        104.2
        84.5%
        45.2
        SRX298286_1
        77.2%
        48%
        54.7
        2.8%
        76.1%
        48%
        53.6
        SRX298286_2
        73.1%
        48%
        54.7
        6.9%
        72.4%
        48%
        53.6
        SRX298287
        39.6%
        1.96
        27.9
        0.50%
        10.2
        55.9
        100.0%
        99.8%
        55.9
        66.1
        77.6%
        25.7
        SRX298287_1
        74.3%
        47%
        33.8
        2.8%
        73.3%
        47%
        33.2
        SRX298287_2
        71.2%
        47%
        33.8
        6.2%
        70.3%
        47%
        33.2
        SRX298288
        28.4%
        1.84
        15.5
        0.53%
        3.5
        31.0
        100.0%
        99.8%
        31.0
        34.5
        81.1%
        14.9
        SRX298288_1
        67.1%
        47%
        18.8
        3.0%
        66.4%
        47%
        18.4
        SRX298288_2
        64.8%
        47%
        18.8
        6.0%
        64.3%
        47%
        18.4
        SRX298289
        42.3%
        2.07
        13.5
        0.57%
        6.0
        27.1
        100.0%
        99.8%
        27.1
        33.1
        76.0%
        12.5
        SRX298289_1
        73.1%
        50%
        16.8
        3.2%
        72.5%
        50%
        16.4
        SRX298289_2
        70.9%
        50%
        16.8
        6.1%
        70.6%
        50%
        16.4
        SRX298290
        52.7%
        2.05
        15.0
        0.59%
        6.3
        30.0
        100.0%
        99.8%
        30.0
        36.3
        64.1%
        13.8
        SRX298290_1
        79.9%
        49%
        22.0
        3.1%
        79.3%
        49%
        21.5
        SRX298290_2
        77.4%
        49%
        22.0
        6.2%
        77.0%
        49%
        21.5
        SRX298291
        46.3%
        1.99
        16.3
        0.58%
        6.8
        32.6
        100.0%
        99.9%
        32.6
        39.4
        82.3%
        14.8
        SRX298291_1
        75.4%
        52%
        18.3
        2.3%
        74.7%
        52%
        18.0
        SRX298291_2
        72.5%
        52%
        18.3
        5.7%
        71.9%
        51%
        18.0
        SRX298292
        38.9%
        1.88
        13.4
        0.55%
        4.2
        26.8
        100.0%
        99.8%
        26.8
        31.0
        66.7%
        12.6
        SRX298292_1
        75.5%
        48%
        19.4
        3.1%
        74.7%
        48%
        18.9
        SRX298292_2
        72.5%
        48%
        19.4
        6.6%
        72.1%
        47%
        18.9
        SRX298293
        47.9%
        2.00
        14.8
        0.53%
        5.2
        29.6
        100.0%
        99.8%
        29.6
        34.8
        56.2%
        14.0
        SRX298293_1
        79.8%
        48%
        25.5
        3.1%
        79.0%
        48%
        24.9
        SRX298293_2
        76.7%
        48%
        25.5
        6.6%
        76.3%
        48%
        24.9
        SRX298294
        47.2%
        2.07
        23.5
        0.53%
        8.3
        47.1
        100.0%
        99.9%
        47.1
        55.4
        81.2%
        22.0
        SRX298294_1
        75.8%
        50%
        27.7
        2.7%
        75.0%
        50%
        27.1
        SRX298294_2
        72.9%
        50%
        27.7
        6.3%
        72.3%
        50%
        27.1
        SRX298295
        65.6%
        1.92
        43.4
        0.56%
        10.9
        87.0
        100.0%
        99.9%
        87.0
        97.8
        80.6%
        41.7
        SRX298295_1
        81.8%
        47%
        52.7
        2.3%
        80.9%
        47%
        51.8
        SRX298295_2
        79.0%
        47%
        52.7
        5.6%
        78.2%
        47%
        51.8
        SRX298296
        37.3%
        1.72
        13.7
        0.54%
        4.5
        27.4
        100.0%
        99.8%
        27.4
        31.9
        72.6%
        13.1
        SRX298296_1
        69.9%
        46%
        18.4
        2.8%
        69.2%
        46%
        18.0
        SRX298296_2
        67.6%
        46%
        18.4
        5.3%
        67.0%
        46%
        18.0
        SRX298297
        33.0%
        1.80
        22.9
        0.54%
        6.2
        45.8
        100.0%
        99.8%
        45.8
        52.0
        77.2%
        22.0
        SRX298297_1
        72.6%
        46%
        29.1
        2.7%
        72.0%
        46%
        28.5
        SRX298297_2
        70.0%
        46%
        29.1
        5.5%
        69.7%
        46%
        28.5
        SRX298298
        41.8%
        2.11
        12.9
        0.55%
        4.1
        25.8
        100.0%
        99.8%
        25.8
        29.9
        79.3%
        12.0
        SRX298298_1
        71.6%
        49%
        15.4
        3.0%
        70.8%
        49%
        15.1
        SRX298298_2
        69.0%
        49%
        15.4
        6.2%
        68.4%
        49%
        15.1
        SRX298299
        33.6%
        1.95
        12.4
        0.55%
        5.4
        24.8
        100.0%
        99.9%
        24.8
        30.2
        66.1%
        11.2
        SRX298299_1
        73.9%
        48%
        17.3
        2.4%
        73.2%
        48%
        17.0
        SRX298299_2
        71.3%
        48%
        17.3
        5.6%
        70.8%
        48%
        17.0
        SRX298300
        47.3%
        1.98
        26.3
        0.56%
        11.1
        52.6
        100.0%
        99.8%
        52.6
        63.7
        76.6%
        24.2
        SRX298300_1
        76.8%
        48%
        32.3
        2.9%
        76.0%
        48%
        31.6
        SRX298300_2
        73.9%
        48%
        32.3
        6.4%
        73.1%
        48%
        31.6
        SRX298301
        85.1%
        2.42
        10.9
        0.54%
        3.5
        21.8
        100.0%
        99.7%
        21.8
        25.3
        76.6%
        10.1
        SRX298301_1
        85.3%
        47%
        13.6
        3.7%
        84.4%
        47%
        13.2
        SRX298301_2
        81.3%
        47%
        13.6
        7.4%
        81.0%
        47%
        13.2
        SRX298302
        54.6%
        1.88
        40.0
        0.53%
        11.4
        80.2
        100.0%
        99.8%
        80.2
        91.6
        82.8%
        37.7
        SRX298302_1
        79.3%
        49%
        46.5
        2.7%
        78.4%
        49%
        45.5
        SRX298302_2
        75.8%
        49%
        46.5
        6.4%
        75.4%
        49%
        45.5
        SRX298303
        46.4%
        1.91
        38.3
        0.56%
        13.5
        76.8
        100.0%
        99.9%
        76.8
        90.3
        80.5%
        35.5
        SRX298303_1
        78.2%
        49%
        45.0
        2.7%
        77.3%
        49%
        44.1
        SRX298303_2
        75.0%
        49%
        45.0
        6.3%
        74.4%
        49%
        44.1
        SRX298304
        55.6%
        2.03
        22.1
        0.48%
        7.0
        44.2
        100.0%
        99.8%
        44.2
        51.3
        74.2%
        20.7
        SRX298304_1
        78.5%
        47%
        28.4
        3.2%
        77.6%
        47%
        27.8
        SRX298304_2
        75.9%
        47%
        28.4
        6.5%
        75.2%
        46%
        27.8
        SRX298305
        51.5%
        2.10
        28.6
        0.48%
        8.5
        57.3
        100.0%
        99.8%
        57.3
        65.8
        80.5%
        26.9
        SRX298305_1
        76.8%
        49%
        34.2
        3.1%
        75.8%
        49%
        33.4
        SRX298305_2
        73.7%
        49%
        34.2
        6.8%
        73.3%
        49%
        33.4
        SRX298306
        44.1%
        1.95
        37.6
        0.49%
        12.5
        75.4
        100.0%
        99.8%
        75.4
        87.9
        77.3%
        35.0
        SRX298306_1
        77.5%
        48%
        46.3
        3.0%
        76.5%
        48%
        45.3
        SRX298306_2
        74.3%
        48%
        46.3
        7.0%
        73.5%
        48%
        45.3
        SRX298307
        50.8%
        1.97
        27.6
        0.52%
        8.6
        55.3
        100.0%
        99.8%
        55.3
        63.9
        78.0%
        26.3
        SRX298307_1
        77.9%
        46%
        34.5
        2.9%
        77.0%
        46%
        33.8
        SRX298307_2
        74.8%
        46%
        34.5
        6.5%
        74.2%
        46%
        33.8
        SRX298308
        51.6%
        1.92
        21.4
        0.53%
        5.1
        43.0
        100.0%
        99.7%
        43.0
        48.1
        78.5%
        20.6
        SRX298308_1
        78.0%
        48%
        27.2
        3.0%
        77.1%
        48%
        26.2
        SRX298308_2
        72.8%
        47%
        27.2
        7.8%
        74.1%
        47%
        26.2
        SRX298309
        56.9%
        2.16
        23.0
        0.53%
        3.5
        46.2
        100.0%
        99.8%
        46.2
        49.7
        81.7%
        22.4
        SRX298309_1
        77.6%
        48%
        28.1
        2.8%
        76.7%
        48%
        27.5
        SRX298309_2
        73.9%
        48%
        28.1
        6.7%
        73.3%
        48%
        27.5

        STAR_SALMON DESeq2 PCA plot

        PCA plot between samples in the experiment. These values are calculated using DESeq2 in the deseq2_qc.r script.

        loading..

        STAR_SALMON DESeq2 sample similarity

        is generated from clustering by Euclidean distances between DESeq2 rlog values for each sample in the deseq2_qc.r script.

        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..

        QualiMap

        QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.DOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503.

        Genomic origin of reads

        Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

        The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

        loading..

        Gene Coverage Profile

        Mean distribution of coverage depth across the length of all mapped transcripts.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

        QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

        The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

        loading..

        Samtools

        Version: 1.17

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        Percent Mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        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).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        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..

        XY counts

        loading..

        Mapped reads per contig

        The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

           
        loading..

        STAR

        STAR is an ultrafast universal RNA-seq aligner.DOI: 10.1093/bioinformatics/bts635.

        Alignment Scores

        loading..

        FastQC (raw)

        Version: 0.12.1

        FastQC (raw) This section of the report shows FastQC results before adapter trimming.

        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 (100bp).

        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 by sample

        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 overrepresented.

        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 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.

        loading..

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceSamplesOccurrences% of all reads
        GTCCTTTCGTACTAAAATATCACAATTTTTTAAAGATAGAAACCAACCTG
        46
        2174568
        0.1261%
        CTCGTCTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAATTCTAT
        41
        1704213
        0.0989%
        CCCCAATAAAATATTTTTATTTATTAAAATTTAATTAATCTATATAATTA
        29
        1102994
        0.0640%
        CTTGACCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAA
        29
        1308427
        0.0759%
        CAACCATTCATTCCAGCCTTCAATTAAAAGACTAATGATTATGCTACCTT
        15
        555011
        0.0322%
        CTTGACCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAG
        15
        701951
        0.0407%
        CTTCTCGTCTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAATTC
        14
        466285
        0.0270%
        CAACAATTCCCAAGATGAAGTTCCTGATCATCCTTGCCCTGGCTGTGGCC
        11
        313253
        0.0182%
        GTTCCTGCTAACTCTCTCCGCGGCTTTGGCCCTGGTGGCCGCCTCCCCGA
        9
        208100
        0.0121%
        GTTCAAGACACCAAACCTCCGAAATGAAACTACCAATCCTTCTGATAGCC
        8
        272882
        0.0158%
        GTAACGTTCAAGACACCAAACCTCCGAAATGAAACTACCAATCCTTCTGA
        8
        234756
        0.0136%
        CCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAACTACT
        8
        271634
        0.0158%
        CAAAAACATGTCTTTTTGAATTATATATAAAGTCTAACCTGCCCACTGAA
        5
        147727
        0.0086%
        TGAAGATCGTGATCCTCTTGTCCGCCGTGGTCTGCGCCCTGGGAGGCACC
        4
        123765
        0.0072%
        AGAGAATCGAACTCAAACTCTCCAGTTGTGTAGCAACCAGAGAGCCACCG
        4
        261009
        0.0151%
        CTTTCATCGTTCTGGTTGCCCTGGCCTGTGCCGCCCCAGCTTTCGGTCGC
        4
        91139
        0.0053%
        CCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAGCTACT
        3
        94217
        0.0055%
        CTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAATTCTATAAAAA
        2
        57150
        0.0033%
        GTCTTCTCGTCTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAAT
        2
        57223
        0.0033%
        CTTTGAGCTAGTGAAGTGAGACGATGATGAGGGAAAACAGCGACACAAAT
        2
        79739
        0.0046%

        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.

        loading..

        Cutadapt

        Version: 3.4

        Cutadapt is a tool to find and remove adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.DOI: 10.14806/ej.17.1.200.

        Filtered Reads

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

        loading..

        Trimmed Sequence Lengths (3')

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

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        loading..

        FastQC (trimmed)

        Version: 0.11.9

        FastQC (trimmed) This section of the report shows FastQC results after adapter trimming.

        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

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        loading..

        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 by sample

        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 overrepresented.

        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 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.

        loading..

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceSamplesOccurrences% of all reads
        GTCCTTTCGTACTAAAATATCACAATTTTTTAAAGATAGAAACCAACCTG
        46
        2154115
        0.1276%
        CTCGTCTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAATTCTAT
        42
        1708853
        0.1013%
        CCCCAATAAAATATTTTTATTTATTAAAATTTAATTAATCTATATAATTA
        29
        1079402
        0.0640%
        CTTGACCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAA
        28
        1227984
        0.0728%
        CAACCATTCATTCCAGCCTTCAATTAAAAGACTAATGATTATGCTACCTT
        18
        619718
        0.0367%
        CTTGACCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAG
        15
        670918
        0.0398%
        CTTCTCGTCTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAATTC
        14
        462886
        0.0274%
        CAACAATTCCCAAGATGAAGTTCCTGATCATCCTTGCCCTGGCTGTGGCC
        10
        285405
        0.0169%
        GTTCAAGACACCAAACCTCCGAAATGAAACTACCAATCCTTCTGATAGCC
        8
        263565
        0.0156%
        GTAACGTTCAAGACACCAAACCTCCGAAATGAAACTACCAATCCTTCTGA
        8
        226124
        0.0134%
        CCAAGATGAAACTGTTCGTATTCCTGGCCTTGGCCGTGGCCGCAACTACT
        8
        261277
        0.0155%
        GTTCCTGCTAACTCTCTCCGCGGCTTTGGCCCTGGTGGCCGCCTCCCCGA
        8
        186228
        0.0110%
        CAAAAACATGTCTTTTTGAATTATATATAAAGTCTAACCTGCCCACTGAA
        5
        146505
        0.0087%
        TGAAGATCGTGATCCTCTTGTCCGCCGTGGTCTGCGCCCTGGGAGGCACC
        4
        120276
        0.0071%
        AGAGAATCGAACTCAAACTCTCCAGTTGTGTAGCAACCAGAGAGCCACCG
        4
        254166
        0.0151%
        CTTTCATCGTTCTGGTTGCCCTGGCCTGTGCCGCCCCAGCTTTCGGTCGC
        4
        89100
        0.0053%
        GTCTTCTCGTCTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAAT
        2
        56789
        0.0034%
        CTTTTAAATAAATTTTAGCTTTTTGACTAAAAAATAAAATTCTATAAAAA
        2
        56613
        0.0034%
        ATCGAACTCAAACTCTCCAGTTGTGTAGCAACCAGAGAGCCACCGAGGAG
        2
        44758
        0.0027%
        CTTTGAGCTAGTGAAGTGAGACGATGATGAGGGAAAACAGCGACACAAAT
        2
        78494
        0.0047%

        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.

        No samples found with any adapter contamination > 0.1%

        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.

        loading..

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        Cutadapt3.4
        FastQC (raw)0.12.1
        FastQC (trimmed)0.11.9
        Samtools1.17

        nf-core/rnaseq Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/rnaseq v3.13.0 (doi: https://doi.org/10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.04.3 (Di Tommaso et al., 2017) with the following command:

        nextflow run -r 3.13.0 nf-core/rnaseq --input /vast/eande106/projects/Mike/qbb_rnaseq2/samplesheet.csv --outdir . --fasta /vast/eande106/projects/Mike/qbb_rnaseq2/genome/BDGP6.59.fa --gtf /vast/eande106/projects/Mike/qbb_rnaseq2/genome/BDGP6.59.gtf --star_index /vast/eande106/projects/Mike/qbb_rnaseq2/star_index --unstranded_threshold 0.5 --skip_bbsplit --skip_bigwig --skip_preseq --skip_dupradar --skip_rseqc --skip_biotype_qc --skip_pseudo_alignment -c /vast/eande106/projects/Mike/qbb_rnaseq2/jhu.config

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/rnaseq Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.12.0
        yaml 6.0.1
        CUSTOM_GETCHROMSIZES getchromsizes 1.16.1
        DESEQ2_QC_STAR_SALMON bioconductor-deseq2 1.28.0
        r-base 4.0.3
        FASTQC fastqc 0.12.1
        GTF2BED perl 5.26.2
        GTF_FILTER python 3.9.5
        MAKE_TRANSCRIPTS_FASTA rsem 1.3.1
        star 2.7.10a
        PICARD_MARKDUPLICATES picard 3.0.0
        QUALIMAP_RNASEQ qualimap 2.2.2-dev
        SALMON_QUANT salmon 1.10.1
        SAMTOOLS_FLAGSTAT samtools 1.17
        SAMTOOLS_IDXSTATS samtools 1.17
        SAMTOOLS_INDEX samtools 1.17
        SAMTOOLS_SORT samtools 1.17
        SAMTOOLS_STATS samtools 1.17
        SE_GENE bioconductor-summarizedexperiment 1.24.0
        r-base 4.1.1
        STAR_ALIGN gawk 5.1.0
        samtools 1.16.1
        star 2.7.9a
        STRINGTIE_STRINGTIE stringtie 2.2.1
        TRIMGALORE cutadapt 3.4
        trimgalore 0.6.7
        TX2GENE python 3.9.5
        TXIMPORT bioconductor-tximeta 1.12.0
        r-base 4.1.1
        Workflow Nextflow 24.04.3
        nf-core/rnaseq 3.13.0

        nf-core/rnaseq Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        revision
        3.13.0
        runName
        zen_stone
        containerEngine
        singularity
        launchDir
        /vast/eande106/projects/Mike/qbb_rnaseq2
        workDir
        /scratch4/eande106/Mike
        projectDir
        /home/msauria1/.nextflow/assets/nf-core/rnaseq
        userName
        msauria1
        profile
        standard
        configFiles
        N/A

        Input/output options

        input
        /vast/eande106/projects/Mike/qbb_rnaseq2/samplesheet.csv
        outdir
        .

        Reference genome options

        fasta
        /vast/eande106/projects/Mike/qbb_rnaseq2/genome/BDGP6.59.fa
        gtf
        /vast/eande106/projects/Mike/qbb_rnaseq2/genome/BDGP6.59.gtf
        star_index
        /vast/eande106/projects/Mike/qbb_rnaseq2/star_index

        Alignment options

        min_mapped_reads
        5

        Process skipping options

        skip_pseudo_alignment
        true
        skip_bigwig
        true
        skip_dupradar
        true
        skip_rseqc
        true
        skip_biotype_qc
        true

        Institutional config options

        config_profile_description
        JHU Rockfish
        config_profile_contact
        msauria1@jh.edu

        Max job request options

        max_cpus
        32
        max_memory
        127 GB
        max_time
        3d