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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_GRCz11_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 1.11

        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

        These samples were run by seq2science v0.6.1, a tool for easy preprocessing of NGS data.

        Take a look at our docs for info about how to use this report to the fullest.

        Workflow
        rna-seq
        Date
        January 10, 2022
        Project
        tdewijs
        Contact E-mail
        tessa.dewijs2@ru.nl

        Report generated on 2022-01-10, 17:41 based on data in:

        Change sample names:

        General Statistics

        Showing 45/45 rows and 10/19 columns.
        Sample Name% DuplicationGC content% PF% Adapter% Dups% MappedM Total seqsGenome coverageM Genome readsM MT genome reads
        DRR032730
        30.5%
        46.0%
        100.0%
        55.6%
        100.0%
        36.3
        2.9 X
        39.9
        0.5
        DRR032731
        31.0%
        46.3%
        100.0%
        56.1%
        100.0%
        36.0
        2.9 X
        39.3
        0.6
        DRR032732
        30.7%
        46.1%
        100.0%
        56.0%
        100.0%
        36.1
        2.9 X
        39.8
        0.5
        DRR032733
        44.7%
        47.3%
        100.0%
        70.9%
        100.0%
        40.7
        3.0 X
        40.8
        0.8
        DRR032734
        44.0%
        47.3%
        100.0%
        70.1%
        100.0%
        36.1
        2.6 X
        36.0
        0.9
        DRR032735
        36.3%
        45.9%
        100.0%
        62.1%
        100.0%
        41.7
        3.4 X
        46.2
        0.4
        DRR032736
        34.0%
        46.0%
        100.0%
        58.0%
        100.0%
        30.3
        2.4 X
        33.5
        0.4
        DRR032737
        38.5%
        46.9%
        100.0%
        61.5%
        100.0%
        38.0
        2.8 X
        38.3
        0.5
        DRR032738
        36.6%
        46.8%
        100.0%
        61.0%
        100.0%
        36.1
        2.6 X
        36.4
        0.5
        DRR032739
        28.7%
        46.4%
        100.0%
        56.0%
        100.0%
        38.7
        3.1 X
        42.5
        0.8
        DRR032740
        28.2%
        46.5%
        100.0%
        55.8%
        100.0%
        36.1
        2.9 X
        39.4
        0.7
        DRR032744
        24.8%
        45.8%
        100.0%
        49.5%
        100.0%
        34.4
        2.7 X
        37.5
        0.8
        DRR032745
        25.8%
        46.4%
        100.0%
        51.5%
        100.0%
        37.7
        2.9 X
        40.5
        0.9
        DRR032746
        31.0%
        46.1%
        100.0%
        56.6%
        100.0%
        34.0
        2.8 X
        38.1
        0.5
        DRR032747
        32.2%
        46.1%
        100.0%
        57.3%
        100.0%
        35.7
        2.9 X
        40.0
        0.5
        DRR032750
        32.6%
        45.9%
        100.0%
        60.5%
        100.0%
        36.4
        3.0 X
        41.8
        0.3
        DRR032751
        31.5%
        45.8%
        100.0%
        58.3%
        100.0%
        31.3
        2.6 X
        36.2
        0.3
        DRR032752
        40.5%
        46.9%
        100.0%
        64.4%
        100.0%
        36.0
        2.6 X
        36.0
        0.7
        DRR032753
        40.5%
        47.0%
        100.0%
        64.7%
        100.0%
        36.4
        2.7 X
        36.4
        0.7
        DRR032754
        31.2%
        45.7%
        100.0%
        58.2%
        100.0%
        32.8
        2.8 X
        38.1
        0.4
        DRR032755
        31.7%
        45.9%
        100.0%
        58.8%
        100.0%
        34.3
        2.9 X
        39.7
        0.4
        DRR032756
        33.6%
        45.7%
        100.0%
        61.8%
        100.0%
        43.6
        3.7 X
        50.3
        0.5
        DRR032757
        34.4%
        45.8%
        100.0%
        60.0%
        100.0%
        39.4
        3.3 X
        45.4
        0.4
        DRR032758
        27.9%
        46.5%
        100.0%
        55.2%
        100.0%
        34.5
        2.7 X
        37.6
        0.8
        DRR032759
        28.3%
        46.1%
        100.0%
        54.9%
        100.0%
        36.5
        2.9 X
        39.8
        0.8
        DRR032760
        30.2%
        46.5%
        100.0%
        57.0%
        100.0%
        37.5
        3.0 X
        41.1
        0.6
        DRR032761
        30.3%
        46.5%
        100.0%
        56.3%
        100.0%
        35.8
        2.8 X
        39.0
        0.6
        DRR032762
        30.8%
        46.3%
        100.0%
        57.2%
        100.0%
        38.0
        3.0 X
        41.4
        0.7
        DRR032763
        34.4%
        45.8%
        100.0%
        61.3%
        100.0%
        37.0
        3.0 X
        41.8
        0.4
        DRR032764
        34.9%
        46.1%
        100.0%
        61.1%
        100.0%
        35.2
        2.9 X
        39.4
        0.3
        GSM1483736
        89.3%
        40.6%
        96.9%
        2.6%
        95.6%
        100.0%
        0.9
        0.0 X
        0.9
        0.2
        GSM1483737
        87.7%
        41.3%
        96.5%
        4.5%
        94.7%
        100.0%
        0.6
        0.0 X
        0.5
        0.2
        GSM1483738
        90.1%
        40.3%
        98.7%
        95.7%
        100.0%
        2.3
        0.1 X
        2.3
        0.4
        GSM1483739
        89.1%
        41.3%
        98.6%
        95.6%
        100.0%
        1.7
        0.0 X
        1.4
        0.5
        GSM1483740
        90.0%
        40.7%
        96.9%
        2.5%
        95.2%
        100.0%
        0.6
        0.0 X
        0.6
        0.1
        GSM1483741
        88.0%
        39.8%
        97.2%
        2.8%
        94.3%
        100.0%
        1.6
        0.0 X
        1.7
        0.3
        GSM1483742
        89.9%
        40.0%
        97.5%
        2.0%
        95.1%
        100.0%
        1.1
        0.0 X
        1.2
        0.2
        GSM1483743
        89.1%
        41.8%
        98.7%
        94.9%
        100.0%
        0.3
        0.0 X
        0.4
        0.1
        GSM1483744
        85.0%
        40.2%
        96.5%
        1.4%
        92.2%
        100.0%
        0.4
        0.0 X
        0.4
        0.1
        GSM1483745
        82.7%
        40.9%
        96.7%
        1.1%
        91.7%
        100.0%
        0.5
        0.0 X
        0.6
        0.1
        GSM1483746
        84.2%
        40.9%
        97.1%
        0.7%
        92.1%
        100.0%
        0.4
        0.0 X
        0.5
        0.1
        GSM1483747
        85.0%
        40.0%
        97.6%
        0.7%
        92.8%
        100.0%
        0.7
        0.0 X
        0.8
        0.1
        GSM1483748
        85.5%
        39.5%
        97.2%
        0.6%
        93.2%
        100.0%
        1.2
        0.0 X
        1.4
        0.1
        GSM1483749
        84.2%
        39.6%
        97.9%
        0.8%
        90.4%
        100.0%
        0.2
        0.0 X
        0.3
        0.0
        GSM1483750
        85.7%
        38.4%
        97.6%
        0.7%
        92.6%
        100.0%
        1.5
        0.0 X
        1.7
        0.1

        Workflow explanation

        Oh no! Something went wrong... Please let us know: https://github.com/vanheeringen-lab/seq2science/issues

        Assembly stats

        Genome assembly GRCz11 contains of 993 contigs, with a GC-content of 36.65%, and 0.34% consists of the letter N. The N50-L50 stats are 54304671-11 and the N75-L75 stats are 48040578-18. The genome annotation contains 30954 genes.

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...)

        Filtered Reads

        Filtering statistics of sampled reads.

        loading..

        Duplication Rates

        Duplication rates of sampled reads.

        loading..

        Sequence Quality

        Average sequencing quality over each base of all reads.

        loading..

        GC Content

        Average GC content over each base of all reads.

        loading..

        N content

        Average N content over each base of all reads.

        loading..

        Picard

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

        Mark Duplicates

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

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

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

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

        SamTools pre-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        The pre-sieve statistics are quality metrics measured before applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, read length filtering, and tn5 shift.

        Percent Mapped

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

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

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

        loading..

        Alignment metrics

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

        loading..

        deepTools

        deepTools is a suite of tools to process and analyze deep sequencing data.

        PCA plot

        PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads

        loading..

        Fingerprint plot

        Signal fingerprint according to plotFingerprint

        loading..

        Strandedness

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

        Sequencing strandedness was inferred for the following samples, and was called if 60% of the sampled reads were explained by either sense (forward) or antisense (reverse).

        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).

        loading..

        deepTools - Spearman correlation heatmap of reads in bins across the genome

        Spearman correlation plot generated by deeptools. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.

        deepTools - Pearson correlation heatmap of reads in bins across the genome

        Pearson correlation plot generated by deeptools. Pearson correlation is a parametric (lots of assumptions, e.g. normality and homoscedasticity) method, and assesses the linearity of the relationship.

        dupRadar

        Figures generated by [dupRadar](https://bioconductor.riken.jp/packages/3.4/bioc/vignettes/dupRadar/inst/doc/dupRadar.html#plotting-and-interpretation). Click the link for help with interpretation.

        DESeq2 - Sample distance cluster heatmap of counts

        Euclidean distance between samples, based on variance stabilizing transformed counts (RNA: expressed genes, ChIP: bound regions, ATAC: accessible regions). Gives us an overview of similarities and dissimilarities between samples.

        DESeq2 - Spearman correlation cluster heatmap of counts

        Correlation cluster heatmap based on variance stabilizing transformed counts. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.

        DESeq2 - Pearson correlation cluster heatmap of counts

        Correlation cluster heatmap based on variance stabilizing transformed counts. Pearson correlation is a parametric (lots of assumptions, e.g. normality and homoscedasticity) method, and assesses the linearity of the relationship.

        Samples & Config

        The samples file used for this run:

        sample assembly stage _trep descriptive_name
        DRR032733 GRCz11 2cell DRR032733 0.75hpf_A1
        DRR032734 GRCz11 2cell DRR032734 0.75hpf_A2
        DRR032752 GRCz11 8cell DRR032752 1.25hpf_A3
        DRR032753 GRCz11 8cell DRR032753 1.25hpf_A4
        DRR032737 GRCz11 32cell DRR032737 1.75hpf_A5
        DRR032738 GRCz11 32cell DRR032738 1.75hpf_A6
        DRR032735 GRCz11 30epiboly DRR032735 4.67hpf_A7
        DRR032736 GRCz11 30epiboly DRR032736 4.67hpf_A8
        DRR032763 GRCz11 shield DRR032763 6hpf_A9
        DRR032764 GRCz11 shield DRR032764 6hpf_A10
        DRR032750 GRCz11 75epiboly DRR032750 8hpf_A11
        DRR032751 GRCz11 75epiboly DRR032751 8hpf_A12
        DRR032754 GRCz11 90epiboly DRR032754 9hpf_A13
        DRR032755 GRCz11 90epiboly DRR032755 9hpf_A14
        DRR032756 GRCz11 bud DRR032756 10hpf_A15
        DRR032757 GRCz11 bud DRR032757 10hpf_A16
        DRR032746 GRCz11 6somite DRR032746 12hpf_A17
        DRR032747 GRCz11 6somite DRR032747 12hpf_A18
        DRR032730 GRCz11 14somite DRR032730 16hpf_A19
        DRR032731 GRCz11 14somite DRR032731 16hpf_A20
        DRR032732 GRCz11 14somite DRR032732 16hpf_A21
        DRR032760 GRCz11 prim5-6 DRR032760 24hpf_A22
        DRR032761 GRCz11 prim5-6 DRR032761 24hpf_A23
        DRR032762 GRCz11 prim5-6 DRR032762 24hpf_A24
        DRR032758 GRCz11 prim25 DRR032758 36hpf_A25
        DRR032759 GRCz11 prim25 DRR032759 36hpf_A26
        DRR032739 GRCz11 48hpf DRR032739 48hpf_A27
        DRR032740 GRCz11 48hpf DRR032740 48hpf_A28
        DRR032744 GRCz11 60hpf DRR032744 60hpf_A29
        DRR032745 GRCz11 60hpf DRR032745 60hpf_A30
        GSM1483736 GRCz11 1-cell GSM1483736 0.67hpf_B1
        GSM1483737 GRCz11 8-cell GSM1483737 1.33hpf_B2
        GSM1483738 GRCz11 64-cell GSM1483738 2hpf_B3
        GSM1483739 GRCz11 256-cell GSM1483739 2.67hpf_B4
        GSM1483740 GRCz11 High GSM1483740 3.33hpf_B5
        GSM1483741 GRCz11 Sphere GSM1483741 4hpf_B6
        GSM1483742 GRCz11 30pc-epiboly GSM1483742 4.67hpf_B7
        GSM1483743 GRCz11 50pc-epiboly GSM1483743 5.33hpf_B8
        GSM1483744 GRCz11 Shield GSM1483744 6hpf_B9
        GSM1483745 GRCz11 Shield GSM1483745 6.67hpf_B10
        GSM1483746 GRCz11 Shield GSM1483746 7.33hpf_B11
        GSM1483747 GRCz11 75pc-epiboly GSM1483747 8hpf_B12
        GSM1483748 GRCz11 75pc-epiboly GSM1483748 8.67hpf_B13
        GSM1483749 GRCz11 90pc-epiboly GSM1483749 9.33hpf_B14
        GSM1483750 GRCz11 Bud GSM1483750 10hpf_B15

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples_pt1.tsv

# pipeline file locations
result_dir: ./results_Dre/pt1  # where to store results
genome_dir: ./genomes  # where to look for or download the genomes
fastq_dir: ./fastq  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: tessa.dewijs2@ru.nl

# produce a UCSC trackhub?
create_trackhub: true

# how to handle replicates
technical_replicates: merge    # change to "keep" to not combine them

# which trimmer to use
trimmer: fastp

# which quantifier to use
quantifier: salmon  # or salmon or featurecounts

# which aligner to use (not used for the gene counts matrix if the quantifier is Salmon)
aligner: star

# filtering after alignment (not used for the gene counts matrix if the quantifier is Salmon)
remove_blacklist: true
min_mapping_quality: 255  # (only keep uniquely mapped reads from STAR alignments)
only_primary_align: true
remove_dups: false # keep duplicates (check dupRadar in the MultiQC)

## differential gene expression analysis
#contrasts:
#  - 'stage_2_1'

        MultiQC v1.11 - Written by Phil Ewels, available on GitHub.

        This report uses HighCharts, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.