P1234: test_ngi_project

Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in test_ngi_project_multiqc_data when this report was generated.


        Choose Plots

        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

        Save Settings

        You can save the toolbox settings for this report to the browser.


        Load Settings

        Choose a saved report profile from the dropdown box below:

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.21

        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/MultiQC/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

        P1234: test_ngi_project
        This is an example project. All identifying data has been removed.

        This report has been generated by the NGI-RNAseq analysis pipeline. Some additional functionality is added to the report using the MultiQC_NGI plugin.
        Contact E-mail:
        phil.ewels@scilifelab.se
        Application Type:
        RNA-seq
        Library Method:
        TruSeq Stranded mRNA
        Sequencing Platform:
        HiSeq 2500 High Output V4
        Sequencing Setup:
        2x125
        Reference Genome:
        hg19

        Report generated on 2024-02-28, 14:39 CET based on data in: /Users/ewels/GitHub/MultiQC/website/public/examples/ngi-rna


        General Statistics

        Showing 22/22 rows and 6/9 columns.
        Sample Name% AlignedM Aligned% BP Trimmed% Dups% GCM Seqs
        P1234_1001
        68.2%
        22.8M
        10.3%
        71.3%
        49%
        33.7M
        P1234_1002
        67.9%
        20.9M
        10.7%
        68.8%
        50%
        31.1M
        P1234_1003
        64.7%
        21.7M
        11.0%
        70.9%
        51%
        33.7M
        P1234_1004
        55.2%
        17.0M
        13.2%
        73.2%
        52%
        31.2M
        P1234_1005
        53.0%
        17.7M
        15.9%
        75.6%
        51%
        33.8M
        P1234_1006
        52.7%
        16.1M
        14.1%
        73.8%
        52%
        30.8M
        P1234_1007
        33.0%
        7.0M
        32.0%
        80.5%
        52%
        21.8M
        P1234_1008
        27.5%
        4.3M
        44.2%
        80.6%
        50%
        16.7M
        P1234_1009
        52.3%
        10.5M
        20.9%
        64.2%
        46%
        20.5M
        P1234_1010
        44.2%
        4.9M
        24.9%
        80.7%
        48%
        11.4M
        P1234_1011
        42.2%
        11.6M
        27.1%
        44.2%
        49%
        28.0M
        P1234_1012
        40.6%
        10.6M
        27.1%
        45.2%
        51%
        26.6M
        P1234_1013
        60.8%
        35.6M
        14.0%
        51.6%
        46%
        59.0M
        P1234_1014
        64.7%
        30.1M
        12.2%
        49.2%
        46%
        46.9M
        P1234_1015
        48.0%
        6.3M
        23.5%
        44.0%
        48%
        13.2M
        P1234_1016
        46.4%
        12.9M
        25.7%
        54.7%
        46%
        28.2M
        P1234_1017
        39.9%
        13.4M
        28.9%
        52.3%
        45%
        34.1M
        P1234_1018
        40.3%
        12.2M
        27.9%
        52.0%
        46%
        30.8M
        P1234_1019
        34.1%
        5.6M
        32.8%
        74.4%
        51%
        16.9M
        P1234_1020
        34.1%
        5.3M
        31.3%
        75.6%
        51%
        15.8M
        P1234_1021
        42.7%
        12.0M
        24.0%
        42.7%
        46%
        28.3M
        P1234_1022
        59.3%
        0.0M
        15.3%
        0.0%
        51%
        0.0M

        edgeR: Sample Similarity

        is generated from normalised gene counts through edgeR. Euclidean distances between log2 normalised CPM values are then calculated and clustered.

        Created with MultiQC

        MDS Plot

        show relatedness between samples in a project. These values are calculated using edgeR in the edgeR_heatmap_MDS.r script.

        Created with MultiQC

        STAR

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

        Alignment Scores

        Created with MultiQC

        Cutadapt

        Version: 1.9.1

        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.

        Created with MultiQC

        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.

        Created with MultiQC

        FastQC

        Version: 0.11.5

        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.

        Created with MultiQC

        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.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        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 dashed black line shows theoretical GC content: Human Transcriptome (UCSC hg38)

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        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.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (123bp).

        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.

        Created with MultiQC

        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.

        Created with MultiQC

        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
        GTCTACGGCCATACCACCCTGAACGCGCCCGATCTCGTCTGATCTCGGAA
        12
        5355475
        0.9038%
        CGACTCTTAGCGGTGGATCACTCGGCTCGTGCGTCGATGAAGAACGCAGC
        12
        1784487
        0.3011%
        ATCCAAGTACTAACCAGGCCCGACCCTGCTTAGCTTCCGAGATCAGACGA
        9
        700985
        0.1183%
        TACTAACCAGGCCCGACCCTGCTTAGCTTCCGAGATCAGACGAGATCGGG
        9
        743693
        0.1255%
        TGGTAACTTTTCTGACACCTCCTGCTTAAAACCCAAAAGGTCAGAAGGAT
        9
        610204
        0.1030%
        GACTCTTAGCGGTGGATCACTCGGCTCGTGCGTCGATGAAGAACGCAGCT
        8
        472573
        0.0797%
        CGCGACCTCAGATCAGACGTGGCGACCCGCTGAATTTAAGCATATTAGTC
        8
        434220
        0.0733%
        GCCGGGCGCGGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAG
        7
        394797
        0.0666%
        GTAGCCCCGGGAGGAACCCGGGGCCGCAAGTGCGTTCGAAGTGTCGATGA
        7
        290023
        0.0489%
        GTACTAACCAGGCCCGACCCTGCTTAGCTTCCGAGATCAGACGAGATCGG
        7
        355739
        0.0600%
        ACTAACCAGGCCCGACCCTGCTTAGCTTCCGAGATCAGACGAGATCGGGC
        7
        289418
        0.0488%
        TCTAGATAGTCAAGTTCGACCGTCTTCTCAGCGCTCCGCCAGGGCCGTGG
        7
        669223
        0.1129%
        GGTTTAGTGAGGCCCTCGGATCGGCCCCGCCGGGGTCGGCCCACGGCCCT
        6
        378238
        0.0638%
        GCGACCTCAGATCAGACGTGGCGACCCGCTGAATTTAAGCATATTAGTCA
        6
        201591
        0.0340%
        GTGGTAACTTTTCTGACACCTCCTGCTTAAAACCCAAAAGGTCAGAAGGA
        6
        318183
        0.0537%
        CATCCAAGTACTAACCAGGCCCGACCCTGCTTAGCTTCCGAGATCAGACG
        5
        252395
        0.0426%
        TCCTGCAATTCACATTAATTCTCGCAGCTAGCTGCGTTCTTCATCGACGC
        5
        292525
        0.0494%
        CTTAGGACACCTGCGTTACCGTTTGACAGGTGTACCGCCCCAGTCAAACT
        4
        137784
        0.0233%
        CTGTGGTAACTTTTCTGACACCTCCTGCTTAAAACCCAAAAGGTCAGAAG
        4
        146923
        0.0248%
        GGAGCAGAAGGGCAAAAGCTCGCTTGATCTTGATTTTCAGTACGAATACA
        4
        249076
        0.0420%

        Adapter Content

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

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

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

        From the FastQC Help:

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

        Created with MultiQC

        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.

        Created with MultiQC

        Software Versions

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

        SoftwareVersion
        Cutadapt1.9.1
        FastQC0.11.5

        NGI-RNAseq Software Versions

        are collected at run time from the software output.

        FastQC
        v0.11.5
        Trim Galore!
        v0.4.2
        STAR
        v2.5.3a
        StringTie
        v1.3.3
        Preseq
        v2.0.0
        featureCounts
        v1.5.1
        dupRadar
        v1.8.0
        Picard MarkDuplicates
        v2.0.1
        Nextflow
        v0.27.0