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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_GRCh38.p13_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 1.9

        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 v0.4.3, 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.

        Contact E-mail
        Jsmits@science.ru.nl
        Workflow
        H3K27ac
        Date
        February 17, 2021

        Report generated on 2021-02-17, 17:26 based on data in:

        Change sample names:

        General Statistics

        Showing 8/8 rows and 15/32 columns.
        Sample Name% DuplicationGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqs% AssignedGenome coverageM Genome readsM MT genome readsTreatment Redundancy
        Chipseq_DOM_H3K27ac
        9.3%
        46.8%
        98.1%
        0.1%
        158 bp
        14.4%
        98.6%
        55.3
        99.2%
        44.4
        46.6%
        0.7 X
        54.5
        0.0
        0.00
        Chipseq_LSC_PRIM_H3K27ac
        7.6%
        44.9%
        97.9%
        0.1%
        180 bp
        12.1%
        97.1%
        62.9
        98.5%
        50.3
        37.2%
        0.8 X
        61.1
        0.0
        0.00
        GSM466732
        1.6%
        44.0%
        44.3%
        0.5%
        5.9%
        77.2%
        11.1
        0.0%
        6.7
        17.4%
        0.1 X
        8.5
        0.0
        0.00
        GSM4728063
        12.9%
        48.5%
        95.6%
        25.6%
        186 bp
        14.5%
        98.9%
        50.5
        97.9%
        40.9
        37.9%
        2.3 X
        50.1
        0.0
        0.00
        GSM4728064
        13.4%
        48.8%
        93.1%
        33.8%
        173 bp
        16.6%
        97.7%
        41.4
        98.1%
        32.4
        41.1%
        1.8 X
        40.6
        0.1
        0.00
        GSM663427
        0.0%
        46.3%
        95.5%
        3.0%
        91.0%
        17.0
        0.0%
        10.8
        13.2%
        0.1 X
        15.4
        0.0
        0.00
        GSM733718
        5.2%
        44.5%
        86.8%
        6.2%
        93.4%
        36.8
        0.0%
        25.0
        6.0%
        0.4 X
        34.4
        0.0
        0.00
        SRX663241
        11.1%
        44.1%
        99.1%
        0.5%
        11.6%
        99.2%
        55.6
        0.0%
        41.9
        31.4%
        0.7 X
        55.1
        0.0
        0.00

        fastp

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

        Filtered Reads

        Filtering statistics of sampled reads.

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        Duplication Rates

        Duplication rates of sampled reads.

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        Insert Sizes

        Insert size estimation of sampled reads.

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        Sequence Quality

        Average sequencing quality over each base of all reads.

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        GC Content

        Average GC content over each base of all reads.

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        N content

        Average N content over each base of all reads.

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        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.

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        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
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        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, 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).

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        Alignment metrics

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

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        SamTools post-sieve

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

        The post-sieve statistics are quality metrics measured after applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, 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).

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        Alignment metrics

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

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        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

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        Fingerprint plot

        Signal fingerprint according to plotFingerprint

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        Read Distribution Profile after Annotation

        Accumulated view of the distribution of sequence reads related to the closest annotated gene. All annotated genes have been normalized to the same size.

        • Green: -2.0Kb upstream of gene to TSS
        • Yellow: TSS to TES
        • Pink: TES to 0.5Kb downstream of gene
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        macs2_frips

        Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.

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        Pileup (BAM) Spearman correlation

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

        Pileup (BAM) Pearson correlation

        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.

        Samples & Config

        The samples file used for this run:

        sample assembly descriptive_name control
        Chipseq_DOM_H3K27ac GRCh38.p13 DOM_H3K27ac SRR5499084
        SRX663241 GRCh38.p13 HKC1_H3K27ac SRR1528616
        Chipseq_LSC_PRIM_H3K27ac GRCh38.p13 LSC_PRIM_H3K27ac PRIM_LSC_input
        GSM4728063 GRCh38.p13 LSC_H3K27ac_Ouyang_1 PRIM_LSC_input
        GSM4728064 GRCh38.p13 LSC_H3K27ac_Ouyang_2 PRIM_LSC_input
        GSM466732 GRCh38.p13 ESC1_H3K27ac_1
        GSM663427 GRCh38.p13 ESC1_H3K27ac_2
        GSM733718 GRCh38.p13 ESC1_H3K27ac_3

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

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

# contact info for multiqc report and trackhub
email: Jsmits@science.ru.nl

# produce a UCSC trackhub?
create_trackhub: True

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

# which trimmer to use
trimmer: fastp

# which aligner to use
aligner: bwa-mem

# filtering after alignment
remove_blacklist: True
min_mapping_quality: 30
only_primary_align: True

# peak caller
peak_caller:
  macs2:
    --keep-dup 1 --broad

        MultiQC v1.9 - Written by Phil Ewels, available on GitHub.

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