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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.

        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 2022-12-14, 15:06 based on data in: /nobackup/tomato_genome/alt_splicing/SRP100604/work/b6/673300f5dd18c87d09f9cd43bd2d30


        General Statistics

        Showing 28/28 rows and 16/19 columns.
        Sample NameM Reads MappeddupInt% Proper PairsError rateM Non-PrimaryM Reads Mapped% Mapped% Proper PairsM Total seqs% AlignedM Aligned% BP Trimmed% Dups% GCLengthM Seqs
        SRR5279858
        54.7
        0.00%
        67.4%
        0.24%
        14.8
        39.9
        100.0%
        100.0%
        39.9
        89.1%
        18.4
        SRR5279858_1
        0.6%
        63.0%
        44%
        149 bp
        20.7
        SRR5279858_2
        0.9%
        61.4%
        44%
        148 bp
        20.7
        SRR5279875
        69.4
        0.00%
        51.6%
        0.24%
        28.2
        41.3
        100.0%
        100.0%
        41.3
        83.9%
        17.9
        SRR5279875_1
        0.7%
        68.0%
        45%
        148 bp
        21.4
        SRR5279875_2
        1.0%
        67.6%
        45%
        148 bp
        21.4
        SRR5279883
        77.7
        0.00%
        42.9%
        0.21%
        37.9
        39.8
        100.0%
        100.0%
        39.8
        80.4%
        16.7
        SRR5279883_1
        0.7%
        72.9%
        46%
        148 bp
        20.8
        SRR5279883_2
        1.0%
        71.9%
        46%
        148 bp
        20.8
        SRR5280323
        66.1
        0.00%
        52.2%
        0.25%
        26.0
        40.2
        100.0%
        99.9%
        40.2
        82.2%
        17.3
        SRR5280323_1
        0.7%
        72.5%
        46%
        148 bp
        21.0
        SRR5280323_2
        1.0%
        71.9%
        46%
        148 bp
        21.0
        SRR5280370
        72.6
        0.00%
        51.1%
        0.21%
        30.3
        42.3
        100.0%
        100.0%
        42.3
        84.8%
        18.5
        SRR5280370_1
        0.5%
        64.3%
        44%
        149 bp
        21.9
        SRR5280370_2
        0.8%
        63.4%
        44%
        148 bp
        21.9
        SRR5280382
        58.0
        0.00%
        65.0%
        0.23%
        17.1
        40.9
        100.0%
        100.0%
        40.9
        88.7%
        18.9
        SRR5280382_1
        0.7%
        60.9%
        43%
        148 bp
        21.3
        SRR5280382_2
        1.0%
        59.8%
        44%
        148 bp
        21.3
        SRR5280383
        46.8
        0.00%
        69.8%
        0.20%
        11.9
        34.9
        100.0%
        100.0%
        34.9
        91.3%
        16.4
        SRR5280383_1
        0.6%
        56.0%
        43%
        149 bp
        17.9
        SRR5280383_2
        0.9%
        55.2%
        43%
        148 bp
        17.9
        SRR5280392
        57.3
        0.00%
        75.5%
        0.21%
        11.6
        45.7
        100.0%
        100.0%
        45.7
        92.4%
        21.6
        SRR5280392_1
        0.6%
        58.9%
        43%
        149 bp
        23.4
        SRR5280392_2
        0.9%
        57.8%
        43%
        148 bp
        23.4
        SRR5282476
        43.0
        0.00%
        0.0%
        0.28%
        33.9
        9.2
        100.0%
        0.0%
        9.2
        38.1%
        3.7
        52.6%
        63.1%
        44%
        25 bp
        9.8
        SRR5282478
        42.8
        0.00%
        0.0%
        0.61%
        33.8
        9.0
        100.0%
        0.0%
        9.0
        35.4%
        3.5
        53.4%
        70.9%
        43%
        24 bp
        9.9
        SRR5282480
        68.7
        0.00%
        0.0%
        0.29%
        55.8
        12.9
        100.0%
        0.0%
        12.9
        37.1%
        5.1
        49.1%
        66.2%
        45%
        27 bp
        13.8
        SRR5282481
        52.9
        0.00%
        0.0%
        0.30%
        43.4
        9.4
        100.0%
        0.0%
        9.4
        36.3%
        3.7
        50.6%
        67.1%
        45%
        26 bp
        10.1

        DupRadar

        provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.

        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.

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        STAR_SALMON DESeq2 PCA plot

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

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        Preseq

        Preseq estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count. A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.

        Complexity curve

        Note that the x axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.

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        RSeQC

        RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.

        Read Distribution

        Read Distribution calculates how mapped reads are distributed over genome features.

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        Inner Distance

        Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

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        Read Duplication

        read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

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        Junction Annotation

        Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

           
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        Junction Saturation

        Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

        Click a line to see the data side by side (as in the original RSeQC plot).

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        Infer experiment

        Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

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        Bam Stat

        All numbers reported in millions.

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        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).

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        Alignment metrics

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

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        Samtools Flagstat

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

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        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.

           
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        STAR

        STAR is an ultrafast universal RNA-seq aligner.

        Alignment Scores

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        Cutadapt

        Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.

        Filtered Reads

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

        loading..

        Trimmed Sequence Lengths

        This plot shows the number of reads with certain lengths of adapter trimmed.

        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)

        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.

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        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.

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        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.

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        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.

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        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.

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        Sequence Length Distribution

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

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        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.

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        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.

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        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.

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        nf-core/rnaseq Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.9.5
        yaml 5.4.1
        DESEQ2_QC_STAR_SALMON bioconductor-deseq2 1.28.0
        r-base 4.0.3
        DUPRADAR bioconductor-dupradar 1.18.0
        r-base 4.0.2
        GET_CHROM_SIZES samtools 1.1
        GTF_GENE_FILTER python 3.8.3
        PRESEQ_LCEXTRAP preseq 3.1.1
        RSEM_PREPAREREFERENCE_TRANSCRIPTS rsem 1.3.1
        star 2.7.6a
        RSEQC_BAMSTAT rseqc 3.0.1
        RSEQC_INFEREXPERIMENT rseqc 3.0.1
        RSEQC_INNERDISTANCE rseqc 3.0.1
        RSEQC_JUNCTIONANNOTATION rseqc 3.0.1
        RSEQC_JUNCTIONSATURATION rseqc 3.0.1
        RSEQC_READDISTRIBUTION rseqc 3.0.1
        RSEQC_READDUPLICATION rseqc 3.0.1
        SALMON_QUANT salmon 1.5.2
        SALMON_SE_GENE bioconductor-summarizedexperiment 1.20.0
        r-base 4.0.3
        SALMON_TX2GENE python 3.8.3
        SALMON_TXIMPORT bioconductor-tximeta 1.8.0
        r-base 4.0.3
        SAMPLESHEET_CHECK python 3.8.3
        SAMTOOLS_FLAGSTAT samtools 1.13
        SAMTOOLS_IDXSTATS samtools 1.13
        SAMTOOLS_INDEX samtools 1.13
        SAMTOOLS_SORT samtools 1.13
        SAMTOOLS_STATS samtools 1.13
        STAR_ALIGN star 2.6.1d
        STAR_GENOMEGENERATE star 2.6.1d
        TRIMGALORE cutadapt 3.4
        trimgalore 0.6.7
        Workflow Nextflow 21.04.0
        nf-core/rnaseq 3.4

        nf-core/rnaseq Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        runName
        golden_feynman
        containerEngine
        singularity
        launchDir
        /nobackup/tomato_genome/alt_splicing/SRP100604
        workDir
        /nobackup/tomato_genome/alt_splicing/SRP100604/work
        projectDir
        /nobackup/tomato_genome/alt_splicing/SRP100604/nf-core-rnaseq-3.4/workflow
        userName
        mdavi258
        profile
        singularity
        configFiles
        /nobackup/tomato_genome/alt_splicing/SRP100604/nf-core-rnaseq-3.4/workflow/nextflow.config, /nobackup/tomato_genome/alt_splicing/SRP100604/tomato.config

        Input/output options

        input
        SRP100604.csv

        Reference genome options

        fasta
        S_lycopersicum_Jun_2022.fa
        gtf
        S_lycopersicum_Jun_2022.gtf
        gene_bed
        S_lycopersicum_Jun_2022.bed

        Read trimming options

        save_trimmed
        true

        Alignment options

        skip_markduplicates
        true

        Process skipping options

        skip_bigwig
        true
        skip_stringtie
        true
        skip_fastqc
        true
        skip_qualimap
        true
        skip_biotype_qc
        true

        Institutional config options

        custom_config_base
        /nobackup/tomato_genome/alt_splicing/SRP100604/nf-core-rnaseq-3.4/workflow/../configs/