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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_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 v0.9.5, 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
        alignment
        Date
        September 22, 2022
        Project
        ghe_2022
        Contact E-mail
        yourmail@here.com

        Report generated on 2022-09-23, 01:01 based on data in:

        Change sample names:

        Show/Hide samples:

        General Statistics

        Showing 54/54 rows and 13/28 columns.
        Sample Name% DuplicationGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqsGenome coverageM Genome readsM MT genome reads
        DMSO_1
        5.1%
        48.9%
        100.0%
        0.0%
        215 bp
        19.7%
        98.5%
        104.0
        83.6%
        81.1
        1.3 X
        98.8
        3.7
        DMSO_2
        24.9%
        51.0%
        100.0%
        0.0%
        195 bp
        37.4%
        98.6%
        143.2
        84.9%
        109.0
        1.8 X
        137.0
        4.3
        DMSO_3
        3.3%
        48.4%
        100.0%
        0.0%
        190 bp
        15.4%
        98.6%
        49.7
        85.3%
        39.4
        0.6 X
        47.3
        1.7
        HSC_D12_DMSO_1_bio
        2.9%
        49.0%
        97.1%
        0.0%
        HSC_D12_DMSO_1_tech
        3.7%
        48.7%
        97.4%
        0.0%
        HSC_D12_DMSO_2_bio
        10.7%
        51.2%
        96.8%
        0.1%
        HSC_D12_DMSO_2_tech
        18.0%
        50.9%
        96.9%
        0.1%
        HSC_D12_DMSO_3_bio
        2.6%
        48.4%
        96.4%
        0.1%
        HSC_D12_DMSO_3_tech
        1.7%
        48.4%
        96.5%
        0.1%
        HSC_D12_RU_1uM_1_bio
        2.5%
        50.3%
        97.2%
        0.0%
        HSC_D12_RU_1uM_1_tech
        4.3%
        47.9%
        96.0%
        0.6%
        HSC_D12_RU_1uM_2_bio
        2.2%
        50.3%
        96.5%
        0.1%
        HSC_D12_RU_1uM_2_tech
        2.0%
        48.4%
        97.1%
        0.9%
        HSC_D12_RU_1uM_3_bio
        2.9%
        50.4%
        96.7%
        0.1%
        HSC_D12_RU_1uM_3_tech
        2.7%
        46.4%
        97.8%
        0.2%
        HSC_D12_RU_1uM_plus_TA_100nM_1_bio
        2.0%
        48.7%
        96.9%
        0.1%
        HSC_D12_RU_1uM_plus_TA_100nM_1_tech
        3.5%
        44.2%
        97.1%
        0.1%
        HSC_D12_RU_1uM_plus_TA_100nM_2_bio
        4.3%
        51.4%
        95.6%
        0.1%
        HSC_D12_RU_1uM_plus_TA_100nM_2_tech
        3.1%
        45.2%
        96.3%
        0.1%
        HSC_D12_RU_1uM_plus_TA_100nM_3_bio
        2.5%
        50.0%
        97.3%
        0.1%
        HSC_D12_RU_1uM_plus_TA_100nM_3_tech
        3.5%
        45.3%
        96.2%
        0.2%
        HSC_D12_RU_1uM_plus_TA_1uM_1_bio
        3.2%
        48.8%
        96.3%
        0.1%
        HSC_D12_RU_1uM_plus_TA_1uM_1_tech
        3.9%
        48.5%
        96.7%
        0.1%
        HSC_D12_RU_1uM_plus_TA_1uM_2_bio
        2.6%
        50.0%
        97.9%
        0.1%
        HSC_D12_RU_1uM_plus_TA_1uM_2_tech
        4.8%
        49.8%
        97.7%
        0.1%
        HSC_D12_RU_1uM_plus_TA_1uM_3_bio
        2.9%
        51.1%
        97.7%
        0.1%
        HSC_D12_RU_1uM_plus_TA_1uM_3_tech
        2.9%
        50.9%
        97.2%
        0.1%
        HSC_D12_TA_100nM_1_bio
        1.7%
        51.3%
        97.7%
        0.1%
        HSC_D12_TA_100nM_1_tech
        2.8%
        51.2%
        97.8%
        0.1%
        HSC_D12_TA_100nM_2_bio
        2.6%
        50.7%
        95.6%
        0.1%
        HSC_D12_TA_100nM_2_tech
        4.2%
        50.5%
        95.5%
        0.1%
        HSC_D12_TA_100nM_3_bio
        5.5%
        50.1%
        96.4%
        0.2%
        HSC_D12_TA_100nM_3_tech
        4.6%
        49.9%
        96.4%
        0.2%
        HSC_D12_TA_1uM_1_bio
        2.1%
        50.2%
        97.1%
        0.1%
        HSC_D12_TA_1uM_1_tech
        3.6%
        50.2%
        97.3%
        0.2%
        HSC_D12_TA_1uM_2_bio
        2.4%
        51.5%
        97.4%
        0.0%
        HSC_D12_TA_1uM_2_tech
        3.4%
        51.4%
        97.3%
        0.0%
        HSC_D12_TA_1uM_3_bio
        3.1%
        49.2%
        98.3%
        0.1%
        HSC_D12_TA_1uM_3_tech
        4.6%
        49.1%
        98.2%
        0.1%
        RU_1uM_1
        3.7%
        48.9%
        100.0%
        0.0%
        177 bp
        21.2%
        98.2%
        73.9
        85.1%
        52.7
        0.9 X
        68.8
        3.7
        RU_1uM_2
        2.3%
        49.3%
        100.0%
        0.0%
        187 bp
        17.3%
        98.6%
        85.5
        85.1%
        63.2
        1.1 X
        81.2
        3.1
        RU_1uM_3
        3.0%
        48.3%
        100.0%
        0.0%
        189 bp
        16.5%
        98.6%
        76.7
        85.2%
        60.4
        0.9 X
        72.6
        3.1
        RU_1uM_plus_TA_100nM_1
        3.2%
        45.8%
        100.0%
        0.0%
        193 bp
        15.7%
        98.6%
        89.0
        84.4%
        73.3
        1.1 X
        83.8
        3.9
        RU_1uM_plus_TA_100nM_2
        3.9%
        48.5%
        100.0%
        0.0%
        194 bp
        16.3%
        98.6%
        82.8
        84.6%
        66.0
        1.0 X
        78.7
        3.0
        RU_1uM_plus_TA_100nM_3
        3.2%
        47.8%
        100.0%
        0.0%
        191 bp
        16.6%
        98.6%
        83.2
        84.5%
        66.2
        1.0 X
        78.7
        3.4
        RU_1uM_plus_TA_1uM_1
        5.7%
        48.6%
        100.0%
        0.0%
        210 bp
        18.4%
        98.5%
        83.7
        83.0%
        65.3
        1.0 X
        79.4
        3.1
        RU_1uM_plus_TA_1uM_2
        6.1%
        49.9%
        100.0%
        0.0%
        208 bp
        20.2%
        98.6%
        106.7
        83.7%
        82.8
        1.3 X
        101.3
        3.8
        RU_1uM_plus_TA_1uM_3
        4.6%
        51.0%
        100.0%
        0.0%
        194 bp
        20.5%
        98.6%
        92.5
        84.2%
        70.3
        1.1 X
        87.6
        3.7
        TA_100nM_1
        3.2%
        51.2%
        100.0%
        0.0%
        161 bp
        22.3%
        98.7%
        137.0
        85.2%
        102.4
        1.7 X
        129.9
        5.4
        TA_100nM_2
        5.5%
        50.5%
        100.0%
        0.0%
        211 bp
        20.3%
        98.6%
        97.1
        84.3%
        73.5
        1.2 X
        92.9
        2.8
        TA_100nM_3
        8.5%
        50.0%
        100.0%
        0.0%
        166 bp
        21.6%
        98.7%
        73.4
        86.3%
        56.6
        0.9 X
        69.4
        3.1
        TA_1uM_1
        4.4%
        50.2%
        100.0%
        0.0%
        182 bp
        22.7%
        98.6%
        138.1
        84.5%
        104.4
        1.7 X
        131.5
        4.7
        TA_1uM_2
        4.6%
        51.4%
        100.0%
        0.0%
        213 bp
        19.2%
        98.6%
        106.2
        84.7%
        81.2
        1.3 X
        101.7
        3.1
        TA_1uM_3
        6.1%
        49.1%
        100.0%
        0.0%
        215 bp
        21.4%
        98.6%
        94.3
        84.4%
        73.3
        1.2 X
        89.2
        3.7

        Workflow explanation

        Assembly stats

        Genome assembly GRCh38 contains of 455 contigs, with a GC-content of 40.99%, and 4.98% consists of the letter N. The N50-L50 stats are 145138636-9 and the N75-L75 stats are 107043718-15.

        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..

        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).

        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..

        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.

        Samples & Config

        The samples file used for this run:

        sample assembly technical_replicates descriptive_name
        HSC_D12_DMSO_1_bio GRCh38 DMSO_1 DMSO_1_bio
        HSC_D12_DMSO_1_tech GRCh38 DMSO_1 DMSO_1_tech
        HSC_D12_DMSO_2_bio GRCh38 DMSO_2 DMSO_2_bio
        HSC_D12_DMSO_2_tech GRCh38 DMSO_2 DMSO_2_tech
        HSC_D12_DMSO_3_bio GRCh38 DMSO_3 DMSO_3_bio
        HSC_D12_DMSO_3_tech GRCh38 DMSO_3 DMSO_3_tech
        HSC_D12_RU_1uM_plus_TA_1uM_1_bio GRCh38 RU_1uM_plus_TA_1uM_1 RU_1uM_plus_TA_1uM_1_bio
        HSC_D12_RU_1uM_plus_TA_1uM_1_tech GRCh38 RU_1uM_plus_TA_1uM_1 RU_1uM_plus_TA_1uM_1_tech
        HSC_D12_RU_1uM_plus_TA_1uM_2_bio GRCh38 RU_1uM_plus_TA_1uM_2 RU_1uM_plus_TA_1uM_2_bio
        HSC_D12_RU_1uM_plus_TA_1uM_2_tech GRCh38 RU_1uM_plus_TA_1uM_2 RU_1uM_plus_TA_1uM_2_tech
        HSC_D12_RU_1uM_plus_TA_1uM_3_bio GRCh38 RU_1uM_plus_TA_1uM_3 RU_1uM_plus_TA_1uM_3_bio
        HSC_D12_RU_1uM_plus_TA_1uM_3_tech GRCh38 RU_1uM_plus_TA_1uM_3 RU_1uM_plus_TA_1uM_3_tech
        HSC_D12_RU_1uM_plus_TA_100nM_1_bio GRCh38 RU_1uM_plus_TA_100nM_1 RU_1uM_plus_TA_100nM_1_bio
        HSC_D12_RU_1uM_plus_TA_100nM_1_tech GRCh38 RU_1uM_plus_TA_100nM_1 RU_1uM_plus_TA_100nM_1_tech
        HSC_D12_RU_1uM_plus_TA_100nM_2_bio GRCh38 RU_1uM_plus_TA_100nM_2 RU_1uM_plus_TA_100nM_2_bio
        HSC_D12_RU_1uM_plus_TA_100nM_2_tech GRCh38 RU_1uM_plus_TA_100nM_2 RU_1uM_plus_TA_100nM_2_tech
        HSC_D12_RU_1uM_plus_TA_100nM_3_bio GRCh38 RU_1uM_plus_TA_100nM_3 RU_1uM_plus_TA_100nM_3_bio
        HSC_D12_RU_1uM_plus_TA_100nM_3_tech GRCh38 RU_1uM_plus_TA_100nM_3 RU_1uM_plus_TA_100nM_3_tech
        HSC_D12_RU_1uM_1_bio GRCh38 RU_1uM_1 RU_1uM_1_bio
        HSC_D12_RU_1uM_1_tech GRCh38 RU_1uM_1 RU_1uM_1_tech
        HSC_D12_RU_1uM_2_bio GRCh38 RU_1uM_2 RU_1uM_2_bio
        HSC_D12_RU_1uM_2_tech GRCh38 RU_1uM_2 RU_1uM_2_tech
        HSC_D12_RU_1uM_3_bio GRCh38 RU_1uM_3 RU_1uM_3_bio
        HSC_D12_RU_1uM_3_tech GRCh38 RU_1uM_3 RU_1uM_3_tech
        HSC_D12_TA_100nM_1_bio GRCh38 TA_100nM_1 TA_100nM_1_bio
        HSC_D12_TA_100nM_1_tech GRCh38 TA_100nM_1 TA_100nM_1_tech
        HSC_D12_TA_100nM_2_bio GRCh38 TA_100nM_2 TA_100nM_2_bio
        HSC_D12_TA_100nM_2_tech GRCh38 TA_100nM_2 TA_100nM_2_tech
        HSC_D12_TA_100nM_3_bio GRCh38 TA_100nM_3 TA_100nM_3_bio
        HSC_D12_TA_100nM_3_tech GRCh38 TA_100nM_3 TA_100nM_3_tech
        HSC_D12_TA_1uM_1_bio GRCh38 TA_1uM_1 TA_1uM_1_bio
        HSC_D12_TA_1uM_1_tech GRCh38 TA_1uM_1 TA_1uM_1_tech
        HSC_D12_TA_1uM_2_bio GRCh38 TA_1uM_2 TA_1uM_2_bio
        HSC_D12_TA_1uM_2_tech GRCh38 TA_1uM_2 TA_1uM_2_tech
        HSC_D12_TA_1uM_3_bio GRCh38 TA_1uM_3 TA_1uM_3_bio
        HSC_D12_TA_1uM_3_tech GRCh38 TA_1uM_3 TA_1uM_3_tech

        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: /ceph/rimlsfnwi/data/genomes/  # where to look for or download the genomes
fastq_dir: ./fastqs  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: yourmail@here.com

# 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 aligner to use
aligner: bwa-mem2

# how to sort bam
bam_sorter:
  samtools:
    coordinate

# filtering after alignment
remove_blacklist: true
only_primary_align: true
min_mapping_quality: 30
remove_dups: false

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

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