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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 11, 2022
        Project
        pt3
        Contact E-mail
        tessa.dewijs2@ru.nl

        Report generated on 2022-01-12, 11:57 based on data in:

        Change sample names:

        General Statistics

        Showing 53/53 rows and 11/21 columns.
        Sample Name% DuplicationGC content% PF% AdapterInsert Size% Dups% MappedM Total seqsGenome coverageM Genome readsM MT genome reads
        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
        GSM1483802
        89.3%
        40.2%
        99.0%
        93.1%
        100.0%
        0.3
        0.0 X
        0.4
        0.0
        GSM1483803
        89.7%
        40.0%
        98.1%
        1.4%
        96.4%
        100.0%
        7.4
        0.2 X
        8.4
        0.4
        GSM1483804
        85.6%
        40.6%
        97.4%
        0.4%
        95.4%
        100.0%
        4.6
        0.1 X
        5.3
        0.2
        GSM1483805
        84.6%
        41.5%
        97.2%
        0.6%
        94.6%
        100.0%
        2.6
        0.1 X
        2.9
        0.2
        GSM1483806
        83.7%
        41.6%
        97.5%
        0.7%
        93.3%
        100.0%
        2.7
        0.1 X
        3.1
        0.2
        GSM1483807
        82.2%
        41.5%
        97.1%
        94.4%
        100.0%
        4.5
        0.1 X
        5.2
        0.2
        GSM1483808
        82.8%
        40.3%
        97.3%
        0.7%
        93.5%
        100.0%
        3.7
        0.1 X
        4.3
        0.3
        GSM1483809
        85.0%
        40.2%
        97.1%
        0.7%
        94.4%
        100.0%
        2.4
        0.1 X
        2.7
        0.1
        GSM1483810
        83.0%
        34.2%
        97.7%
        86.6%
        100.0%
        0.1
        0.0 X
        0.1
        0.0
        GSM1483811
        84.0%
        40.6%
        97.1%
        0.8%
        94.1%
        100.0%
        3.0
        0.1 X
        3.3
        0.2
        GSM1483812
        84.4%
        39.6%
        97.8%
        0.6%
        92.9%
        100.0%
        2.4
        0.1 X
        2.7
        0.1
        GSM1483813
        87.3%
        40.5%
        97.7%
        0.6%
        96.2%
        100.0%
        5.6
        0.2 X
        6.4
        0.3
        GSM1483814
        88.4%
        40.5%
        96.3%
        2.2%
        95.9%
        100.0%
        4.3
        0.1 X
        5.0
        0.3
        GSM1483815
        62.3%
        42.4%
        89.2%
        83.1%
        100.0%
        0.0
        0.0 X
        0.0
        0.0
        GSM1483816
        89.6%
        40.2%
        98.4%
        0.6%
        95.9%
        100.0%
        4.0
        0.1 X
        4.7
        0.3
        GSM1483817
        90.4%
        42.7%
        98.4%
        0.7%
        96.5%
        100.0%
        2.5
        0.1 X
        2.8
        0.1
        GSM1483818
        91.0%
        41.0%
        98.6%
        0.7%
        96.2%
        100.0%
        3.8
        0.1 X
        4.2
        0.3
        GSM1483819
        90.1%
        41.9%
        98.7%
        96.5%
        100.0%
        3.8
        0.1 X
        4.2
        0.4
        GSM1483820
        90.1%
        40.0%
        98.3%
        0.7%
        95.6%
        100.0%
        3.7
        0.1 X
        4.1
        0.3
        GSM1483821
        86.7%
        41.2%
        97.5%
        1.0%
        94.2%
        100.0%
        0.9
        0.0 X
        1.0
        0.1
        GSM1483822
        86.7%
        40.9%
        98.3%
        0.7%
        94.5%
        100.0%
        2.1
        0.1 X
        2.4
        0.1
        GSM1483823
        84.5%
        39.4%
        97.5%
        0.8%
        93.7%
        100.0%
        4.2
        0.1 X
        4.8
        0.3
        GSM1483824
        84.6%
        39.6%
        97.8%
        93.3%
        100.0%
        2.4
        0.1 X
        2.8
        0.2
        GSM1483825
        85.6%
        40.6%
        97.1%
        0.9%
        94.4%
        100.0%
        3.5
        0.1 X
        4.2
        0.3
        GSM1483826
        86.4%
        41.1%
        98.2%
        1.0%
        93.2%
        100.0%
        0.5
        0.0 X
        0.5
        0.0
        GSM1483827
        84.7%
        40.0%
        97.9%
        93.2%
        100.0%
        1.8
        0.1 X
        2.0
        0.1
        GSM1483828
        84.5%
        40.0%
        97.5%
        1.1%
        93.1%
        100.0%
        2.1
        0.1 X
        2.5
        0.1
        GSM1483829
        85.5%
        40.0%
        97.1%
        0.4%
        96.8%
        100.0%
        16.4
        0.5 X
        18.7
        0.9
        GSM1483830
        85.9%
        39.4%
        97.6%
        95.3%
        100.0%
        7.3
        0.2 X
        9.5
        0.5
        GSM1483831
        89.2%
        40.7%
        98.4%
        94.5%
        100.0%
        0.4
        0.0 X
        0.4
        0.0
        GSM1483832
        88.2%
        40.6%
        97.4%
        5.1%
        95.8%
        100.0%
        5.5
        0.2 X
        6.3
        0.4
        GSM1483833
        86.6%
        40.7%
        97.3%
        0.6%
        96.2%
        100.0%
        6.1
        0.2 X
        7.1
        0.4
        GSM1483834
        84.3%
        41.1%
        96.5%
        4.4%
        95.4%
        100.0%
        7.1
        0.2 X
        8.2
        0.5
        GSM1483835
        88.7%
        41.6%
        97.3%
        1.0%
        94.8%
        100.0%
        0.4
        0.0 X
        0.5
        0.0
        GSM1483836
        84.2%
        39.8%
        95.9%
        6.3%
        95.9%
        100.0%
        9.5
        0.3 X
        10.8
        0.6
        GSM1483837
        84.3%
        39.0%
        96.7%
        0.8%
        94.4%
        100.0%
        4.7
        0.1 X
        5.5
        0.3
        GSM1483838
        84.6%
        39.6%
        97.6%
        0.8%
        93.2%
        100.0%
        2.5
        0.1 X
        2.8
        0.1
        GSM1483839
        84.1%
        40.1%
        97.3%
        0.7%
        94.4%
        100.0%
        2.1
        0.1 X
        2.3
        0.1
        GSM1483840
        84.5%
        40.7%
        97.6%
        0.9%
        92.7%
        100.0%
        0.8
        0.0 X
        0.9
        0.1
        GSM1483841
        85.9%
        41.8%
        97.3%
        0.8%
        92.3%
        100.0%
        0.1
        0.0 X
        0.1
        0.0
        GSM2837568
        7.8%
        47.7%
        99.3%
        12.7%
        240 bp
        33.5%
        100.0%
        171.8
        16.5 X
        184.5
        4.3
        GSM2837569
        5.4%
        47.5%
        99.2%
        13.3%
        236 bp
        31.2%
        100.0%
        179.6
        16.7 X
        187.0
        4.6
        GSM2837570
        6.4%
        47.5%
        99.4%
        11.7%
        234 bp
        36.8%
        100.0%
        263.5
        24.4 X
        272.7
        6.8
        GSM2837571
        6.9%
        47.4%
        99.4%
        11.2%
        237 bp
        30.9%
        100.0%
        127.1
        11.8 X
        131.9
        3.5
        GSM2837572
        11.7%
        47.2%
        99.3%
        13.2%
        237 bp
        26.5%
        100.0%
        132.4
        11.8 X
        132.2
        4.3
        GSM2837573
        7.5%
        47.1%
        97.5%
        11.0%
        271 bp
        28.0%
        100.0%
        111.5
        10.8 X
        121.1
        1.7
        GSM2837574
        6.2%
        47.4%
        99.3%
        11.6%
        274 bp
        28.3%
        100.0%
        102.1
        9.9 X
        111.4
        1.4
        GSM2837575
        8.3%
        46.7%
        99.1%
        11.3%
        235 bp
        31.3%
        100.0%
        105.6
        10.2 X
        114.8
        2.3
        GSM2837576
        8.1%
        45.9%
        99.3%
        14.5%
        255 bp
        20.7%
        100.0%
        89.8
        8.7 X
        98.2
        2.3
        GSM2837577
        13.9%
        47.3%
        99.1%
        17.4%
        245 bp
        34.7%
        100.0%
        120.0
        11.3 X
        129.4
        1.8
        GSM2837578
        11.7%
        47.2%
        99.4%
        12.7%
        233 bp
        35.0%
        100.0%
        157.8
        15.3 X
        171.0
        3.8

        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. Afterwards, duplicate reads were marked with Picard MarkDuplicates v2.23.8. Transcript abundances were quantified with Salmon v1.5.2 with options '--seqBias --gcBias --validateMappings --recoverOrphans'. 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. General alignment statistics were collected by samtools stats v1.14. 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'. RNA-seq read duplication types were analyzed using dupRadar v1.20.0. The UCSC genome browser was used to visualize and inspect alignment. 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
        GSM1483800 GRCz11 High-pec GSM1483800 43.33hpf_B65
        GSM1483801 GRCz11 High-pec GSM1483801 44hpf_B66
        GSM1483802 GRCz11 High-pec GSM1483802 44.67hpf_B67
        GSM1483803 GRCz11 High-pec GSM1483803 45.33hpf_B68
        GSM1483804 GRCz11 High-pec GSM1483804 46hpf_B69
        GSM1483805 GRCz11 High-pec GSM1483805 46.67hpf_B70
        GSM1483806 GRCz11 High-pec GSM1483806 47.33hpf_B71
        GSM1483807 GRCz11 Long-pec GSM1483807 48hpf_B72
        GSM1483808 GRCz11 Long-pec GSM1483808 48.67hpf_B73
        GSM1483809 GRCz11 Long-pec GSM1483809 49.33hpf_B74
        GSM1483810 GRCz11 Long-pec GSM1483810 50hpf_B75
        GSM1483811 GRCz11 Long-pec GSM1483811 50.67hpf_B76
        GSM1483812 GRCz11 Long-pec GSM1483812 51.33hpf_B77
        GSM1483813 GRCz11 Long-pec GSM1483813 52hpf_B78
        GSM1483814 GRCz11 Long-pec GSM1483814 52.67hpf_B79
        GSM1483815 GRCz11 Long-pec GSM1483815 53.33hpf_B80
        GSM1483816 GRCz11 Long-pec GSM1483816 54hpf_B81
        GSM1483817 GRCz11 Long-pec GSM1483817 54.67hpf_B82
        GSM1483818 GRCz11 Long-pec GSM1483818 55.33hpf_B83
        GSM1483819 GRCz11 Long-pec GSM1483819 56hpf_B84
        GSM1483820 GRCz11 Long-pec GSM1483820 56.67hpf_B85
        GSM1483821 GRCz11 Long-pec GSM1483821 57.33hpf_B86
        GSM1483822 GRCz11 Long-pec GSM1483822 58hpf_B87
        GSM1483823 GRCz11 Long-pec GSM1483823 58.67hpf_B88
        GSM1483824 GRCz11 Long-pec GSM1483824 59.33hpf_B89
        GSM1483825 GRCz11 Pec-fin GSM1483825 60hpf_B90
        GSM1483826 GRCz11 Pec-fin GSM1483826 60.67hpf_B91
        GSM1483827 GRCz11 Pec-fin GSM1483827 61.33hpf_B92
        GSM1483828 GRCz11 Pec-fin GSM1483828 62hpf_B93
        GSM1483829 GRCz11 Pec-fin GSM1483829 62.67hpf_B94
        GSM1483830 GRCz11 Pec-fin GSM1483830 63.33hpf_B95
        GSM1483831 GRCz11 Pec-fin GSM1483831 64hpf_B96
        GSM1483832 GRCz11 Pec-fin GSM1483832 64.67hpf_B97
        GSM1483833 GRCz11 Pec-fin GSM1483833 65.33hpf_B98
        GSM1483834 GRCz11 Pec-fin GSM1483834 66hpf_B99
        GSM1483835 GRCz11 Pec-fin GSM1483835 66.67hpf_B100
        GSM1483836 GRCz11 Pec-fin GSM1483836 67.33hpf_B101
        GSM1483837 GRCz11 Pec-fin GSM1483837 68hpf_B102
        GSM1483838 GRCz11 Pec-fin GSM1483838 68.67hpf_B103
        GSM1483839 GRCz11 Pec-fin GSM1483839 69.33hpf_B104
        GSM1483840 GRCz11 Pec-fin GSM1483840 70hpf_B105
        GSM1483841 GRCz11 Pec-fin GSM1483841 70.67hpf_B106
        GSM2837568 GRCz11 5-9-somites GSM2837568 12hpf_C1
        GSM2837569 GRCz11 14-19-somites GSM2837569 16hpf_C2
        GSM2837572 GRCz11 64-cell GSM2837572 2hpf_C3
        GSM2837570 GRCz11 20-25-somites GSM2837570 20hpf_C4
        GSM2837571 GRCz11 Prim-5 GSM2837571 26hpf_C5
        GSM2837573 GRCz11 Prim-15 GSM2837573 30hpf_C6
        GSM2837574 GRCz11 Prim-25 GSM2837574 36hpf_C7
        GSM2837575 GRCz11 Long-pec GSM2837575 48hpf_C8
        GSM2837576 GRCz11 Shield GSM2837576 6hpf_C9
        GSM2837577 GRCz11 Protruding-mouth GSM2837577 72hpf_C10
        GSM2837578 GRCz11 75pc-epiboly GSM2837578 8hpf_C11

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples_pt3.tsv

# pipeline file locations
result_dir: .  # 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.