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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 13, 2022
        Project
        pt8
        Contact E-mail
        tessa.dewijs2@ru.nl

        Report generated on 2022-01-14, 23:45 based on data in:

        Change sample names:

        General Statistics

        Showing 41/41 rows and 11/21 columns.
        Sample Name% DuplicationGC content% PF% AdapterInsert Size% Dups% MappedM Total seqsGenome coverageM Genome readsM MT genome reads
        GSM3406869
        5.2%
        44.6%
        96.9%
        21.5%
        231 bp
        49.9%
        100.0%
        103.1
        6.0 X
        63.1
        43.7
        GSM3406870
        7.1%
        44.3%
        98.4%
        22.6%
        224 bp
        49.6%
        100.0%
        64.8
        3.7 X
        38.6
        27.9
        GSM3406871
        16.4%
        45.2%
        97.4%
        21.0%
        257 bp
        44.5%
        100.0%
        131.7
        10.3 X
        98.3
        37.3
        GSM3406872
        7.8%
        44.7%
        98.7%
        17.6%
        236 bp
        38.5%
        100.0%
        77.9
        5.4 X
        56.7
        23.5
        GSM3406873
        6.7%
        45.1%
        98.6%
        19.7%
        215 bp
        36.8%
        100.0%
        87.6
        6.4 X
        67.3
        23.7
        GSM3406874
        5.9%
        45.2%
        98.4%
        17.0%
        224 bp
        35.9%
        100.0%
        85.3
        6.3 X
        66.0
        22.7
        GSM3406875
        10.5%
        44.5%
        98.5%
        19.0%
        213 bp
        41.0%
        100.0%
        101.2
        8.0 X
        81.9
        27.6
        GSM3406876
        1.6%
        44.5%
        99.3%
        11.3%
        220 bp
        36.7%
        100.0%
        115.2
        7.9 X
        88.8
        33.8
        GSM3406877
        3.5%
        43.9%
        99.5%
        1.1%
        290 bp
        43.9%
        100.0%
        116.4
        8.2 X
        90.1
        35.7
        GSM3406878
        4.2%
        43.7%
        98.6%
        12.3%
        221 bp
        46.4%
        100.0%
        65.8
        4.2 X
        45.0
        26.0
        GSM4467018
        63.7%
        52.2%
        99.4%
        88.8%
        100.0%
        26.7
        2.1 X
        38.3
        2.7
        GSM4467019
        60.2%
        50.6%
        99.4%
        85.5%
        100.0%
        25.3
        1.7 X
        31.4
        1.6
        GSM4467020
        62.5%
        49.5%
        99.5%
        83.6%
        100.0%
        28.4
        1.8 X
        32.4
        1.8
        GSM4467021
        56.6%
        51.8%
        99.4%
        83.8%
        100.0%
        32.9
        2.7 X
        48.8
        1.8
        GSM4467022
        56.7%
        51.3%
        98.7%
        1.0%
        82.4%
        100.0%
        31.2
        2.3 X
        41.6
        1.5
        GSM4467023
        53.5%
        49.5%
        99.5%
        79.8%
        100.0%
        29.7
        2.1 X
        38.7
        1.5
        GSM4467024
        53.9%
        49.7%
        99.4%
        81.5%
        100.0%
        30.6
        2.3 X
        42.0
        1.6
        GSM4467025
        51.8%
        50.0%
        98.8%
        0.9%
        81.0%
        100.0%
        28.2
        2.1 X
        37.5
        1.6
        GSM4467026
        57.1%
        47.8%
        99.3%
        0.3%
        81.4%
        100.0%
        29.9
        2.0 X
        36.9
        1.6
        GSM4724672
        37.2%
        50.6%
        98.2%
        1.7%
        63.6%
        100.0%
        12.8
        1.2 X
        16.2
        0.3
        GSM4724673
        46.4%
        47.0%
        97.9%
        1.9%
        69.7%
        100.0%
        33.7
        2.5 X
        34.0
        1.0
        GSM4724674
        41.7%
        47.9%
        98.4%
        3.1%
        62.1%
        100.0%
        21.3
        1.6 X
        21.9
        0.3
        GSM4724675
        34.3%
        48.2%
        97.2%
        2.9%
        51.6%
        100.0%
        11.0
        0.9 X
        11.7
        0.2
        GSM4724676
        40.9%
        47.5%
        98.7%
        2.0%
        60.6%
        100.0%
        20.6
        1.5 X
        21.1
        0.5
        GSM4724677
        32.7%
        49.3%
        99.2%
        1.7%
        54.6%
        100.0%
        12.6
        1.1 X
        14.6
        0.3
        GSM4724678
        37.2%
        48.4%
        99.7%
        57.4%
        100.0%
        18.3
        1.4 X
        19.8
        0.3
        GSM4724679
        33.2%
        47.5%
        99.5%
        1.0%
        50.1%
        100.0%
        12.1
        0.9 X
        12.6
        0.3
        GSM4724680
        36.1%
        47.7%
        98.7%
        1.5%
        54.1%
        100.0%
        14.5
        1.1 X
        15.1
        0.3
        GSM4724681
        34.7%
        48.1%
        98.6%
        1.3%
        54.9%
        100.0%
        15.2
        1.2 X
        16.6
        0.2
        GSM5021045
        63.3%
        40.7%
        99.9%
        86.3%
        100.0%
        30.5
        2.1 X
        28.1
        7.3
        GSM5021046
        63.5%
        40.5%
        99.8%
        86.3%
        100.0%
        29.4
        2.0 X
        26.6
        7.5
        GSM5021047
        58.4%
        40.6%
        99.8%
        84.8%
        100.0%
        33.2
        2.3 X
        31.2
        7.8
        GSM5021048
        51.3%
        40.5%
        99.9%
        80.9%
        100.0%
        25.5
        1.7 X
        23.4
        6.2
        GSM5021053
        45.8%
        40.6%
        99.8%
        77.3%
        100.0%
        20.1
        1.4 X
        19.4
        4.2
        GSM5021054
        52.5%
        40.1%
        99.8%
        84.1%
        100.0%
        30.6
        2.0 X
        27.9
        7.1
        GSM5021055
        50.3%
        41.1%
        99.8%
        80.3%
        100.0%
        22.0
        1.5 X
        20.7
        4.5
        GSM5021056
        51.6%
        40.9%
        99.8%
        80.9%
        100.0%
        24.0
        1.7 X
        23.1
        4.8
        GSM5021061
        51.1%
        40.7%
        99.9%
        79.6%
        100.0%
        23.4
        1.7 X
        22.5
        4.5
        GSM5021062
        55.6%
        40.3%
        99.8%
        82.7%
        100.0%
        26.1
        1.9 X
        25.6
        4.7
        GSM5021063
        61.6%
        40.4%
        99.8%
        85.1%
        100.0%
        32.9
        2.5 X
        33.6
        5.6
        GSM5021064
        50.4%
        40.7%
        99.9%
        78.9%
        100.0%
        22.4
        1.7 X
        22.7
        3.7

        Workflow explanation

        Preprocessing of reads was done automatically with workflow tool seq2science v0.6.1. Public samples were downloaded from the Sequence Read Archive with help of the ncbi e-utilities and pysradb. Genome assembly GRCz11 was downloaded with genomepy 0.11.0. Single-end reads were trimmed with fastp v0.20.1 with default options. Paired-end reads were trimmed with fastp v0.20.1 with default options. Reads were aligned with STAR v2.7.6a with default options. Transcript abundances were quantified with Salmon v1.5.2 with options '--seqBias --gcBias --validateMappings --recoverOrphans'. Afterwards, duplicate reads were marked with Picard MarkDuplicates v2.23.8. General alignment statistics were collected by samtools stats v1.14. Mapped reads were removed if they did not have a minimum mapping quality of 255, were a (secondary) multimapper or aligned inside the ENCODE blacklist.Afterwards samples were downsampled to -1 reads. Transcript abundance estimations were aggregated and converted to gene counts using tximeta v1.10.0. Sample sequencing strandedness was inferred using RSeQC v4.0.0 in order to improve quantification accuracy. Deeptools v3.5.0 was used for the fingerprint, profile, correlation and dendrogram/heatmap plots, where the heatmap was made with options '--distanceBetweenBins 9000 --binSize 1000'. The UCSC genome browser was used to visualize and inspect alignment. RNA-seq read duplication types were analyzed using dupRadar v1.20.0. Quality control metrics were aggregated by MultiQC v1.11.

        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..

        Insert Sizes

        Insert size estimation 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.

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        loading..

        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
        GSM4724672 GRCz11 2-cell GSM4724672 0.75hpf_J3
        GSM4724673 GRCz11 64-cell GSM4724673 2hpf_J4
        GSM4724674 GRCz11 64-cell GSM4724674 2hpf_J5
        GSM4724675 GRCz11 64-cell GSM4724675 2hpf_J6
        GSM4724676 GRCz11 1000-cell GSM4724676 3hpf_J7
        GSM4724677 GRCz11 1000-cell GSM4724677 3hpf_J8
        GSM4724678 GRCz11 1000-cell GSM4724678 3hpf_J9
        GSM4724679 GRCz11 sphere GSM4724679 4hpf_J10
        GSM4724680 GRCz11 sphere GSM4724680 4hpf_J11
        GSM4724681 GRCz11 sphere GSM4724681 4hpf_J12
        GSM5021045 GRCz11 12hpf GSM5021045 12hpf_K1
        GSM5021046 GRCz11 12hpf GSM5021046 12hpf_K2
        GSM5021047 GRCz11 12hpf GSM5021047 12hpf_K3
        GSM5021048 GRCz11 12hpf GSM5021048 12hpf_K4
        GSM5021053 GRCz11 18hpf GSM5021053 18hpf_K5
        GSM5021054 GRCz11 18hpf GSM5021054 18hpf_K6
        GSM5021055 GRCz11 18hpf GSM5021055 18hpf_K7
        GSM5021056 GRCz11 18hpf GSM5021056 18hpf_K8
        GSM5021061 GRCz11 24hpf GSM5021061 24hpf_K9
        GSM5021062 GRCz11 24hpf GSM5021062 24hpf_K10
        GSM5021063 GRCz11 24hpf GSM5021063 24hpf_K11
        GSM5021064 GRCz11 24hpf GSM5021064 24hpf_K12
        GSM4467018 GRCz11 0h GSM4467018 0hpf_L1
        GSM4467019 GRCz11 1h GSM4467019 1hpf_L2
        GSM4467020 GRCz11 2h GSM4467020 2hpf_L3
        GSM4467021 GRCz11 3h GSM4467021 3hpf_L4
        GSM4467022 GRCz11 4h GSM4467022 4hpf_L5
        GSM4467023 GRCz11 5h GSM4467023 5hpf_L6
        GSM4467024 GRCz11 6h GSM4467024 6hpf_L7
        GSM4467025 GRCz11 7h GSM4467025 7hpf_L8
        GSM4467026 GRCz11 8h GSM4467026 8hpf_L9
        GSM3406869 GRCz11 0hpf GSM3406869 0hpf_M1
        GSM3406870 GRCz11 0hpf GSM3406870 0hpf_M2
        GSM3406871 GRCz11 2hpf GSM3406871 2hpf_M3
        GSM3406872 GRCz11 2hpf GSM3406872 2hpf_M4
        GSM3406873 GRCz11 3hpf GSM3406873 3hpf_M5
        GSM3406874 GRCz11 3hpf GSM3406874 3hpf_M6
        GSM3406875 GRCz11 4hpf GSM3406875 4hpf_M7
        GSM3406876 GRCz11 4hpf GSM3406876 4hpf_M8
        GSM3406877 GRCz11 6hpf GSM3406877 6hpf_M9
        GSM3406878 GRCz11 6hpf GSM3406878 6hpf_M10

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples_pt8.tsv

# pipeline file locations
result_dir: /bank/tdewijs/results_Dre/pt8  # where to store results
genome_dir: /bank/tdewijs/genomes  # where to look for or download the genomes
fastq_dir: /bank/tdewijs/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.