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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 2022-01-11, 10:50 based on data in:

        Change sample names:

        General Statistics

        Showing 53/53 rows and 10/19 columns.
        Sample Name% DuplicationGC content% PF% Adapter% Dups% MappedM Total seqsGenome coverageM Genome readsM MT genome reads
        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
        GSM1483751
        84.3%
        39.9%
        97.5%
        1.0%
        92.1%
        100.0%
        0.8
        0.0 X
        0.9
        0.0
        GSM1483752
        89.2%
        40.6%
        98.4%
        96.1%
        100.0%
        3.3
        0.1 X
        3.4
        0.5
        GSM1483753
        87.7%
        40.3%
        97.5%
        1.3%
        94.2%
        100.0%
        1.4
        0.0 X
        1.5
        0.2
        GSM1483754
        88.9%
        41.8%
        98.5%
        95.9%
        100.0%
        2.4
        0.1 X
        2.7
        0.2
        GSM1483755
        88.3%
        39.7%
        98.4%
        95.0%
        100.0%
        2.3
        0.1 X
        2.5
        0.2
        GSM1483756
        89.3%
        42.4%
        98.1%
        0.7%
        95.2%
        100.0%
        0.6
        0.0 X
        0.7
        0.0
        GSM1483757
        87.6%
        40.5%
        98.3%
        94.4%
        100.0%
        1.4
        0.0 X
        1.5
        0.2
        GSM1483758
        89.6%
        40.2%
        98.1%
        0.7%
        96.0%
        100.0%
        3.6
        0.1 X
        4.0
        0.2
        GSM1483759
        88.8%
        38.9%
        98.2%
        0.5%
        94.8%
        100.0%
        2.2
        0.1 X
        2.4
        0.1
        GSM1483760
        84.9%
        40.7%
        96.9%
        1.0%
        93.8%
        100.0%
        1.4
        0.0 X
        1.6
        0.1
        GSM1483761
        84.6%
        39.9%
        94.6%
        3.7%
        93.9%
        100.0%
        2.1
        0.1 X
        2.4
        0.1
        GSM1483762
        85.1%
        39.6%
        97.5%
        0.8%
        93.2%
        100.0%
        2.2
        0.1 X
        2.5
        0.1
        GSM1483763
        83.6%
        40.3%
        96.4%
        5.6%
        92.6%
        100.0%
        1.4
        0.0 X
        1.6
        0.1
        GSM1483764
        86.4%
        41.4%
        97.6%
        0.8%
        94.0%
        100.0%
        1.2
        0.0 X
        1.4
        0.1
        GSM1483765
        86.7%
        40.8%
        98.1%
        0.7%
        93.9%
        100.0%
        2.1
        0.1 X
        2.5
        0.1
        GSM1483766
        84.5%
        40.6%
        97.5%
        0.8%
        93.8%
        100.0%
        2.8
        0.1 X
        3.1
        0.2
        GSM1483767
        85.1%
        40.7%
        98.0%
        92.7%
        100.0%
        1.4
        0.0 X
        1.5
        0.1
        GSM1483768
        90.6%
        41.5%
        98.5%
        0.4%
        95.9%
        100.0%
        1.6
        0.0 X
        1.8
        0.1
        GSM1483769
        91.1%
        41.0%
        98.3%
        1.0%
        95.1%
        100.0%
        0.9
        0.0 X
        1.0
        0.1
        GSM1483770
        90.3%
        40.2%
        98.9%
        92.7%
        100.0%
        0.0
        0.0 X
        0.0
        0.0
        GSM1483771
        89.9%
        40.5%
        98.3%
        1.0%
        95.0%
        100.0%
        1.8
        0.1 X
        2.0
        0.2
        GSM1483772
        89.4%
        40.8%
        97.6%
        1.5%
        95.6%
        100.0%
        2.0
        0.1 X
        2.3
        0.2
        GSM1483773
        89.7%
        40.3%
        98.6%
        95.7%
        100.0%
        2.6
        0.1 X
        3.0
        0.2
        GSM1483774
        89.8%
        40.5%
        98.7%
        95.4%
        100.0%
        2.3
        0.1 X
        2.7
        0.2
        GSM1483775
        89.4%
        40.4%
        98.3%
        0.7%
        95.2%
        100.0%
        1.7
        0.1 X
        2.1
        0.1
        GSM1483776
        84.3%
        40.7%
        97.0%
        0.6%
        94.1%
        100.0%
        2.5
        0.1 X
        2.8
        0.1
        GSM1483777
        81.4%
        39.7%
        96.7%
        0.5%
        93.6%
        100.0%
        4.6
        0.1 X
        5.4
        0.2
        GSM1483778
        84.1%
        40.4%
        96.9%
        0.8%
        93.3%
        100.0%
        2.1
        0.1 X
        2.4
        0.1
        GSM1483779
        83.6%
        40.1%
        97.0%
        1.0%
        94.5%
        100.0%
        4.3
        0.1 X
        4.9
        0.2
        GSM1483780
        83.6%
        40.6%
        96.6%
        0.7%
        94.5%
        100.0%
        3.4
        0.1 X
        3.8
        0.2
        GSM1483781
        83.3%
        41.3%
        97.2%
        0.7%
        93.1%
        100.0%
        1.9
        0.1 X
        2.2
        0.1
        GSM1483782
        83.3%
        41.2%
        97.0%
        0.7%
        93.9%
        100.0%
        1.7
        0.0 X
        1.9
        0.1
        GSM1483783
        82.6%
        40.9%
        96.4%
        5.4%
        92.7%
        100.0%
        1.8
        0.1 X
        2.0
        0.1
        GSM1483784
        87.8%
        40.2%
        97.7%
        0.6%
        96.6%
        100.0%
        7.4
        0.2 X
        8.3
        0.4
        GSM1483785
        87.4%
        40.3%
        98.0%
        0.6%
        95.1%
        100.0%
        3.3
        0.1 X
        3.7
        0.2
        GSM1483786
        88.6%
        39.0%
        97.9%
        0.5%
        96.4%
        100.0%
        7.4
        0.2 X
        8.6
        0.3
        GSM1483787
        87.3%
        39.4%
        97.7%
        0.6%
        95.7%
        100.0%
        5.5
        0.2 X
        6.3
        0.3
        GSM1483788
        88.5%
        41.4%
        98.1%
        96.5%
        100.0%
        4.9
        0.1 X
        5.8
        0.3
        GSM1483789
        84.6%
        41.1%
        97.1%
        0.4%
        93.8%
        100.0%
        1.7
        0.0 X
        1.9
        0.2
        GSM1483790
        82.7%
        40.4%
        97.1%
        0.7%
        93.2%
        100.0%
        3.8
        0.1 X
        4.3
        0.3
        GSM1483791
        84.0%
        41.3%
        97.1%
        0.6%
        93.6%
        100.0%
        2.5
        0.1 X
        2.8
        0.2
        GSM1483792
        82.6%
        40.9%
        96.9%
        0.8%
        94.9%
        100.0%
        6.3
        0.2 X
        7.2
        0.6
        GSM1483793
        85.1%
        40.2%
        97.3%
        0.6%
        93.9%
        100.0%
        2.3
        0.1 X
        2.6
        0.2
        GSM1483794
        82.6%
        39.7%
        97.2%
        0.7%
        93.0%
        100.0%
        3.2
        0.1 X
        3.7
        0.2
        GSM1483795
        85.4%
        39.8%
        97.6%
        0.6%
        92.4%
        100.0%
        1.2
        0.0 X
        1.5
        0.1
        GSM1483796
        84.4%
        40.8%
        97.5%
        0.7%
        94.4%
        100.0%
        2.9
        0.1 X
        3.4
        0.2
        GSM1483797
        89.2%
        41.2%
        98.2%
        0.3%
        96.2%
        100.0%
        4.6
        0.1 X
        5.2
        0.3
        GSM1483798
        87.8%
        41.0%
        98.0%
        0.6%
        95.2%
        100.0%
        3.6
        0.1 X
        4.1
        0.3
        GSM1483799
        89.0%
        41.6%
        97.3%
        2.1%
        96.5%
        100.0%
        6.3
        0.2 X
        7.1
        0.7
        GSM1483800
        89.9%
        41.7%
        98.7%
        95.3%
        100.0%
        1.3
        0.0 X
        1.5
        0.1
        GSM1483801
        90.1%
        40.9%
        98.5%
        0.4%
        94.1%
        100.0%
        0.1
        0.0 X
        0.2
        0.0

        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
        GSM1483749 GRCz11 90pc-epiboly GSM1483749 9.33hpf_B14
        GSM1483750 GRCz11 Bud GSM1483750 10hpf_B15
        GSM1483751 GRCz11 1-4-somites GSM1483751 10.67hpf_B16
        GSM1483752 GRCz11 1-4-somites GSM1483752 11.33hpf_B17
        GSM1483753 GRCz11 5-9-somites GSM1483753 12hpf_B18
        GSM1483754 GRCz11 5-9-somites GSM1483754 12.67hpf_B19
        GSM1483755 GRCz11 5-9-somites GSM1483755 13.33hpf_B20
        GSM1483756 GRCz11 10-13-somites GSM1483756 14hpf_B21
        GSM1483757 GRCz11 10-13-somites GSM1483757 14.67hpf_B22
        GSM1483758 GRCz11 10-13-somites GSM1483758 15.33hpf_B23
        GSM1483759 GRCz11 14-19-somites GSM1483759 16hpf_B24
        GSM1483760 GRCz11 14-19-somites GSM1483760 16.67hpf_B25
        GSM1483761 GRCz11 14-19-somites GSM1483761 17.33hpf_B26
        GSM1483762 GRCz11 14-19-somites GSM1483762 18hpf_B27
        GSM1483763 GRCz11 14-19-somites GSM1483763 18.67hpf_B28
        GSM1483764 GRCz11 20-25-somites GSM1483764 19.33hpf_B29
        GSM1483765 GRCz11 20-25-somites GSM1483765 20hpf_B30
        GSM1483766 GRCz11 20-25-somites GSM1483766 20.67hpf_B31
        GSM1483767 GRCz11 20-25-somites GSM1483767 21.33hpf_B32
        GSM1483768 GRCz11 26-somites GSM1483768 22hpf_B33
        GSM1483769 GRCz11 26-somites GSM1483769 22.67hpf_B34
        GSM1483770 GRCz11 26-somites GSM1483770 23.33hpf_B35
        GSM1483771 GRCz11 Prim-5 GSM1483771 24hpf_B36
        GSM1483772 GRCz11 Prim-5 GSM1483772 24.67hpf_B37
        GSM1483773 GRCz11 Prim-5 GSM1483773 25.33hpf_B38
        GSM1483774 GRCz11 Prim-5 GSM1483774 26hpf_B39
        GSM1483775 GRCz11 Prim-5 GSM1483775 26.67hpf_B40
        GSM1483776 GRCz11 Prim-5 GSM1483776 27.33hpf_B41
        GSM1483777 GRCz11 Prim-5 GSM1483777 28hpf_B42
        GSM1483778 GRCz11 Prim-5 GSM1483778 28.67hpf_B43
        GSM1483779 GRCz11 Prim-5 GSM1483779 29.33hpf_B44
        GSM1483780 GRCz11 Prim-15 GSM1483780 30hpf_B45
        GSM1483781 GRCz11 Prim-15 GSM1483781 30.67hpf_B46
        GSM1483782 GRCz11 Prim-15 GSM1483782 31.33hpf_B47
        GSM1483783 GRCz11 Prim-15 GSM1483783 32hpf_B48
        GSM1483784 GRCz11 Prim-15 GSM1483784 32.67hpf_B49
        GSM1483785 GRCz11 Prim-15 GSM1483785 33.33hpf_B50
        GSM1483786 GRCz11 Prim-15 GSM1483786 34hpf_B51
        GSM1483787 GRCz11 Prim-15 GSM1483787 34.67hpf_B52
        GSM1483788 GRCz11 Prim-15 GSM1483788 35.33hpf_B53
        GSM1483789 GRCz11 Prim-25 GSM1483789 36hpf_B54
        GSM1483790 GRCz11 Prim-25 GSM1483790 36.67hpf_B55
        GSM1483791 GRCz11 Prim-25 GSM1483791 37.33hpf_B56
        GSM1483792 GRCz11 Prim-25 GSM1483792 38hpf_B57
        GSM1483793 GRCz11 Prim-25 GSM1483793 38.67hpf_B58
        GSM1483794 GRCz11 Prim-25 GSM1483794 39.33hpf_B59
        GSM1483795 GRCz11 Prim-25 GSM1483795 40hpf_B60
        GSM1483796 GRCz11 Prim-25 GSM1483796 40.67hpf_B61
        GSM1483797 GRCz11 Prim-25 GSM1483797 41.33hpf_B62
        GSM1483798 GRCz11 High-pec GSM1483798 42hpf_B63
        GSM1483799 GRCz11 High-pec GSM1483799 42.67hpf_B64
        GSM1483800 GRCz11 High-pec GSM1483800 43.33hpf_B65
        GSM1483801 GRCz11 High-pec GSM1483801 44hpf_B66

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples_pt2.tsv

# pipeline file locations
result_dir: ./results_Dre/pt2  # 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.