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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_Xenopus_tropicalis_v9.1_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 (631b25f)

        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 v0.5.6, 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
        atac-seq
        Date
        October 28, 2021
        Project
        xt_atac
        Contact E-mail
        yourmail@here.com

        Report generated on 2021-10-29, 01:07 based on data in:

        Change sample names:

        General Statistics

        Showing 11/11 rows and 16/33 columns.
        Sample Name% DuplicationGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqs% AssignedGenome coverageM Genome readsM MT genome readsNumber of PeaksTreatment Redundancy
        Xt_stage105_rep2
        69.0%
        46.8%
        94.5%
        7.7%
        81 bp
        85.2%
        96.7%
        185.8
        98.9%
        5.9
        20.2%
        3.2 X
        112.0
        67.7
        30701
        0.04
        Xt_stage105_rep3
        14.2%
        46.6%
        98.3%
        1.2%
        119 bp
        44.2%
        94.6%
        40.7
        98.7%
        7.3
        30.7%
        0.6 X
        21.4
        17.0
        49137
        0.07
        Xt_stage12_rep1
        17.0%
        45.5%
        87.6%
        28.9%
        126 bp
        61.7%
        95.2%
        128.5
        98.0%
        14.3
        23.9%
        5.4 X
        113.9
        8.6
        66917
        0.07
        Xt_stage12_rep2
        15.6%
        45.1%
        83.6%
        27.8%
        67 bp
        85.8%
        94.2%
        296.8
        99.0%
        14.7
        18.3%
        4.2 X
        94.2
        185.5
        45551
        0.05
        Xt_stage16_rep1
        3.4%
        46.0%
        95.3%
        9.0%
        12 bp
        57.3%
        34.0%
        34.6
        99.6%
        2.5
        8.7%
        0.2 X
        7.1
        4.6
        6017
        0.10
        Xt_stage16_rep2
        2.0%
        44.1%
        91.2%
        7.4%
        34 bp
        50.0%
        10.8%
        139.6
        99.2%
        3.9
        6.4%
        0.3 X
        10.6
        4.4
        11953
        0.11
        Xt_stage26_rep1
        16.1%
        49.1%
        97.4%
        5.7%
        63 bp
        19.1%
        95.3%
        66.2
        99.5%
        29.3
        28.7%
        1.6 X
        60.2
        2.9
        96715
        0.10
        Xt_stage26_rep2
        7.1%
        48.2%
        98.3%
        5.5%
        81 bp
        12.9%
        95.5%
        57.3
        99.4%
        27.5
        30.4%
        1.3 X
        51.3
        3.4
        69419
        0.11
        Xt_stage34_rep1
        2.7%
        49.7%
        97.1%
        6.9%
        62 bp
        7.5%
        94.4%
        19.7
        99.3%
        10.1
        26.9%
        0.5 X
        17.9
        0.7
        35273
        0.08
        Xt_stage34_rep2
        3.2%
        47.9%
        97.7%
        3.5%
        75 bp
        9.0%
        96.1%
        116.0
        99.3%
        50.3
        12.1%
        2.8 X
        106.3
        5.1
        73425
        0.07
        Xt_stage9_rep2
        52.7%
        46.9%
        95.4%
        6.3%
        114 bp
        89.3%
        90.4%
        281.1
        95.9%
        2.5
        5.8%
        5.2 X
        181.4
        72.7
        7028
        0.02

        Workflow explanation

        Preprocessing of reads was done automatically with workflow tool seq2science v0.5.6. Paired-end reads were trimmed with fastp v0.20.1 with default options. Genome assembly Xenopus_tropicalis_v9.1 was downloaded with genomepy 0.10.0. Reads were aligned with bwa-mem2 v2.2.1 with options '-M'. Afterwards, duplicate reads were marked with Picard MarkDuplicates v2.23.8. General alignment statistics were collected by samtools stats v1.11. Mapped reads were removed if they did not have a minimum mapping quality of 30, were a (secondary) multimapper, were a PCR/optical duplicate, aligned inside the ENCODE blacklist or had a template length longer than 150 bp and shorter than 0 bp and finally were tn5 bias shifted by seq2science. Peaks were called with macs2 v2.2.7 with options '--shift -100 --extsize 200 --nomodel --buffer-size 10000' in BAM mode. The effective genome size was estimated by taking the number of unique kmers in the assembly of the same length as the average read length for each sample. 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'. Narrowpeak files of biological replicates belonging to the same condition were merged with fisher's method in macs2. The fraction reads in peak score (frips) was calculated by featurecounts v1.6.4. The UCSC genome browser was used to visualize and inspect alignment. A consensus set of summits was made with gimmemotifs.combine_peaks v0.15.1. A peak feature distribution plot and peak localization plot relative to TSS were made with chipseeker. All summits were extended with 100 bp to get a consensus peakset. Finally, a count table from the consensus peakset with gimmemotifs. Quality control metrics were aggregated by MultiQC v1.11.

        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, 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).

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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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        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.

        Peak distributions (macs2)

        The distribution of read pileup around 20000 random peaks for each sample. This visualization is a quick and dirty way to check if your peaks look like what you would expect, and what the underlying distribution of different types of peaks is.

        Peak feature distribution (macs2)

        Figure generated by chipseeker

        Distribution of peak locations relative to TSS (macs2)

        Figure generated by chipseeker

        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 biological_replicates mpf hpf descriptive_name notes source
        Xt_stage9_rep2 Xenopus_tropicalis_v9.1 stage9 420 7 stage9_rep2 st9_rep2 Bright
        Xt_stage105_rep2 Xenopus_tropicalis_v9.1 stage10.5 660 11 stage105_rep2 st10.5_rep2 Bright
        Xt_stage105_rep3 Xenopus_tropicalis_v9.1 stage10.5 660 11 stage105_rep3 st10.5_rep3 Bright
        Xt_stage12_rep1 Xenopus_tropicalis_v9.1 stage12 795 13.25 stage12_rep1 st12_rep1 Bright
        Xt_stage12_rep2 Xenopus_tropicalis_v9.1 stage12 795 13.25 stage12_rep2 st12_rep2 Bright
        Xt_stage16_rep2 Xenopus_tropicalis_v9.1 stage16 1095 18.25 stage16_rep2 st16_rep2 Bright
        Xt_stage16_rep1 Xenopus_tropicalis_v9.1 stage16 1095 18.25 stage16_rep1 st16_rep1 Bright
        Xt_stage26_rep1 Xenopus_tropicalis_v9.1 stage26 1770 29.5 stage26_rep1 st26_rep1 Bright
        Xt_stage26_rep2 Xenopus_tropicalis_v9.1 stage26 1770 29.5 stage26_rep2 st26_rep2 Bright
        Xt_stage34_rep1 Xenopus_tropicalis_v9.1 stage34 2670 44.5 stage34_rep1 st34_rep1 Bright
        Xt_stage34_rep2 Xenopus_tropicalis_v9.1 stage34 2670 44.5 stage34_rep2 st34_rep2 Bright

        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: /bank/genomes  # where to look for or download the genomes
fastq_dir: /bank/raw/xenopus  # 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
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-mem2

# filtering after alignment
remove_blacklist: true
remove_mito: true
tn5_shift: true
min_mapping_quality: 30
only_primary_align: True
max_template_length: 150
remove_dups: true

# peak callers (supported peak callers are macs2, and genrich)
peak_caller:
  macs2:
      --shift -100 --extsize 200 --nomodel --buffer-size 10000
#  genrich:
#      -j -y -D -d 200 -q 0.05

## differential accessibility analysis
#contrasts:
#  - 'biological_replicates_adult_embryo'

        MultiQC v1.11 (631b25f) - Written by Phil Ewels, available on GitHub.

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