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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_GRCm38.p6_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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        Please remember to cite the tools that you use in your analysis.

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        About MultiQC

        This report was generated using MultiQC, version 1.14

        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 v1.2.4, 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
        November 24, 2025
        Project
        no_blacklist
        Contact E-mail
        none@provided.com

        Report generated on 2025-11-25, 16:00 CET based on data in:

        Change sample names:

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        General Statistics

        Showing 24/24 rows and 17/34 columns.
        Sample Name% DuplicationM Reads After FilteringGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqs% AssignedGenome coverageM Genome readsM MT genome readsNumber of PeaksTreatment Redundancy
        SS-NAM-1
        16.5%
        75.6
        51.2%
        100.0%
        0.0%
        77 bp
        21.3%
        99.4%
        75.6
        99.7%
        39.9
        54.1%
        1.3 X
        69.8
        5.4
        94475
        0.31
        SS-NAM-1_S5_L001
        11.5%
        38.7
        51.2%
        99.5%
        16.3%
        SS-NAM-1_S5_L002
        10.7%
        36.9
        51.2%
        99.4%
        17.9%
        SS-NAM-2
        18.9%
        78.8
        53.1%
        100.0%
        0.0%
        66 bp
        24.2%
        99.4%
        78.8
        99.4%
        42.8
        65.5%
        1.3 X
        73.6
        4.7
        106940
        0.38
        SS-NAM-2_S6_L001
        12.7%
        40.3
        53.1%
        99.2%
        21.8%
        SS-NAM-2_S6_L002
        12.0%
        38.5
        53.1%
        99.2%
        23.5%
        SS-NAM-3
        16.3%
        44.0
        49.1%
        100.0%
        0.0%
        136 bp
        20.5%
        99.4%
        44.0
        97.7%
        16.1
        39.1%
        0.7 X
        40.2
        3.5
        66471
        0.16
        SS-NAM-3_S7_L001
        11.5%
        23.6
        49.2%
        98.5%
        7.4%
        SS-NAM-3_S7_L002
        9.5%
        20.4
        48.9%
        98.4%
        8.6%
        SS-NAM-4
        18.1%
        93.4
        49.5%
        100.0%
        0.0%
        75 bp
        22.7%
        99.5%
        93.4
        99.7%
        51.1
        40.4%
        1.6 X
        87.4
        5.6
        95518
        0.24
        SS-NAM-4_S8_L001
        12.2%
        47.0
        49.5%
        99.2%
        15.4%
        SS-NAM-4_S8_L002
        11.7%
        46.5
        49.4%
        99.1%
        16.6%
        Tr-NAM-1
        26.9%
        111.8
        51.7%
        100.0%
        0.0%
        83 bp
        36.1%
        99.4%
        111.8
        99.7%
        46.6
        71.2%
        1.8 X
        97.2
        14.0
        110803
        0.44
        Tr-NAM-1_S12_L001
        19.9%
        58.5
        51.7%
        99.6%
        13.3%
        Tr-NAM-1_S12_L002
        18.3%
        53.3
        51.7%
        99.5%
        14.5%
        Tr-NAM-2
        39.1%
        126.3
        52.2%
        100.0%
        0.0%
        79 bp
        48.0%
        99.3%
        126.3
        99.2%
        42.2
        70.1%
        2.0 X
        110.8
        14.6
        106579
        0.43
        Tr-NAM-2_S13_L001
        27.8%
        65.4
        52.2%
        99.2%
        17.6%
        Tr-NAM-2_S13_L002
        26.3%
        61.0
        52.2%
        99.1%
        19.0%
        Tr-NAM-3
        30.2%
        130.0
        52.5%
        100.0%
        0.0%
        74 bp
        38.9%
        99.1%
        130.0
        99.3%
        54.1
        68.7%
        2.1 X
        115.3
        13.6
        113282
        0.44
        Tr-NAM-3_S14_L001
        21.6%
        67.7
        52.5%
        99.0%
        17.9%
        Tr-NAM-3_S14_L002
        20.1%
        62.3
        52.4%
        98.9%
        19.3%
        Tr-NAM-4
        29.0%
        157.6
        53.5%
        100.0%
        0.0%
        75 bp
        38.3%
        99.3%
        157.6
        99.6%
        67.3
        72.4%
        2.6 X
        143.8
        12.6
        120195
        0.49
        Tr-NAM-4_S15_L001
        20.3%
        80.2
        53.5%
        99.0%
        17.8%
        Tr-NAM-4_S15_L002
        19.4%
        77.3
        53.5%
        98.9%
        19.2%

        Workflow explanation

        Preprocessing of reads was done automatically by seq2science v1.2.4 using the atac-seq workflow. Paired-end reads were trimmed with fastp v0.23.2 with default options. Genome assembly GRCm38.p6 was downloaded with genomepy 0.16.3. Reads were aligned with bwa-mem2 v2.2.1 with options '-M'. Afterwards, duplicate reads were marked with Picard MarkDuplicates v3.0.0. General alignment statistics were collected by samtools stats v1.16. Before peak calling, paired-end info from reads was removed with seq2science so that both mates in a pair get used. 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 by khmer v3.0 by taking the number of unique k-mers in the assembly of the same length as the average read length for each sample. Deeptools v3.5.1 was used for the fingerprint, profile, correlation and dendrogram/heatmap plots, where the heatmap was made with options '--distanceBetweenBins 9000 --binSize 1000'. The fraction reads in peak score (frips) was calculated by featurecounts v1.6.4. A consensus set of summits was made with gimmemotifs.combine_peaks v0.18.1. The UCSC genome browser was used to visualize and inspect alignment. All summits were extended with 100 bp to get a consensus peakset. Finally, a count table from the consensus peakset was made with gimmemotifs.coverage_table. Differential accessibility analysis was performed using DESeq2 v1.34. To adjust for multiple testing the (default) Benjamini-Hochberg procedure was performed with an FDR cutoff of 0.1 (default is 0.1). Counts were log transformed using the (default) shrinkage estimator apeglm v1.16. Differential motif analysis on the consensus peakset was performed with gimme maelstrom v0.18.1. Quality control metrics were aggregated by MultiQC v1.14.

        Assembly stats

        Genome assembly GRCm38.p6 contains of 66 contigs, with a GC-content of 41.67%, and 2.86% consists of the letter N. The N50-L50 stats are 130694993-9 and the N75-L75 stats are 120421639-14.

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.

        Filtered Reads

        Filtering statistics of sampled reads.

        loading..

        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.DOI: 10.1093/bioinformatics/btp352.

        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.DOI: 10.1093/bioinformatics/btp352.

        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.DOI: 10.1093/nar/gkw257.

        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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        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.DOI: 10.1093/bioinformatics/btt656.

        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.

        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.

        Peaks per sample distribution (macs2)

        The distribution of peaks between samples. An upset plot is like a venn diagram, but is easier to read with many samples. This figure shows the overlap of peaks between conditions/samples. .

        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.

        DESeq2 - MA plot for contrast conditions_trained_naive

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for conditions_trained_naive

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        gimme maelstrom macs2 results

        Gimme maelstrom is a method to infer differential motifs between samples. It solves a system of linear equations, Ax=b. Where we solve for x, A the motif scores and b the count table. It combines the results of different methods that solve this problem, and its result is the table below. It can be used to find **differential** motifs between samples.

          factors motif information z-score SS-NAM-1 z-score SS-NAM-2 z-score SS-NAM-3 z-score SS-NAM-4 z-score Tr-NAM-1 z-score Tr-NAM-2 z-score Tr-NAM-3 z-score Tr-NAM-4 % with motif corr SS-NAM-1 corr SS-NAM-2 corr SS-NAM-3 corr SS-NAM-4 corr Tr-NAM-1 corr Tr-NAM-2 corr Tr-NAM-3 corr Tr-NAM-4
        GM.5.0.C2H2_ZF_Homeodomain.0001
        ZEB1
        		 logos/GM_5_0_C2H2_ZF_Homeodomain_0001.png 1.75 2.30 3.82 2.12 -4.04 -3.04 -3.89 -4.05 0.27 0.01 0.01 0.03 0.03 -0.03 -0.03 -0.02 -0.06
        GM.5.0.C2H2_ZF.0010
        SCRT1,SCRT2
        		 logos/GM_5_0_C2H2_ZF_0010.png 0.34 0.90 2.05 2.49 -3.16 -1.25 -0.74 -3.16 0.62 0.01 0.01 -0.00 -0.00 -0.00 -0.00 0.01 -0.01
        GM.5.0.C2H2_ZF.0281
        ZNF784,ZFP784
        		 logos/GM_5_0_C2H2_ZF_0281.png 0.25 1.14 2.61 2.01 -0.66 -1.54 -1.57 -3.12 0.61 0.01 0.02 0.01 0.01 -0.02 -0.01 -0.01 -0.03
        GM.5.0.Mixed.0104
        BDP1,BRF1,BRF2
        		 logos/GM_5_0_Mixed_0104.png -0.68 0.01 -1.51 -1.69 3.29 1.68 0.28 0.44 0.45 -0.01 -0.01 -0.02 -0.02 0.04 0.03 0.02 -0.01
        GM.5.0.C2H2_ZF.0129
        ZFP410,ZNF410
        		 logos/GM_5_0_C2H2_ZF_0129.png 1.30 -0.55 3.18 1.63 -3.79 -0.34 -2.03 -2.00 0.60 0.00 -0.01 0.03 0.03 -0.03 -0.02 -0.02 -0.02
        GM.5.0.C2H2_ZF.0095
        ZKSCAN3,ZKSCAN4,ZNF306
        		 logos/GM_5_0_C2H2_ZF_0095.png 0.62 -0.44 1.65 1.93 -3.20 -0.97 -1.59 -1.80 1.03 0.00 -0.01 0.02 0.02 -0.02 -0.01 -0.01 -0.01
        GM.5.0.C2H2_ZF.0171
        ZNF274
        		 logos/GM_5_0_C2H2_ZF_0171.png 0.37 -2.20 3.39 3.29 -2.22 -2.55 -3.56 -2.84 0.52 0.00 -0.01 0.03 0.04 -0.03 -0.02 -0.03 -0.03
        GM.5.0.Unknown.0065
        ZFP57
        		 logos/GM_5_0_Unknown_0065.png -0.72 0.20 2.33 3.03 -1.88 -1.76 -1.28 0.67 0.00 -0.00 -0.00 -0.00 0.00 0.00 0.00 -0.00 0.01
        GM.5.0.Unknown.0122
        PBX
        		 logos/GM_5_0_Unknown_0122.png 0.81 0.58 0.99 0.41 -1.77 0.55 -3.21 -0.74 0.78 0.00 0.00 0.01 0.00 -0.01 -0.00 -0.01 -0.00
        GM.5.0.Rel.0008
        NFKB1
        		 logos/GM_5_0_Rel_0008.png 2.26 1.44 1.98 1.69 -2.21 -2.62 -3.33 1.63 0.97 0.01 0.01 0.00 0.01 -0.02 -0.02 -0.02 0.02
        GM.5.0.C2H2_ZF.0127
        GLI1,GLI3,GLI,GLI2
        		 logos/GM_5_0_C2H2_ZF_0127.png -0.20 -1.19 0.93 2.38 -2.09 -1.68 -1.72 -3.22 1.02 -0.00 -0.01 0.03 0.03 -0.04 -0.03 -0.02 -0.01
        GM.5.0.p53.0005
        TP53,TP63,TP73,TRP63,TRP53,(...)
        		 logos/GM_5_0_p53_0005.png -0.51 -2.26 1.92 3.12 -2.19 -1.13 -0.74 -3.02 0.92 -0.01 -0.02 0.01 0.02 -0.01 -0.01 -0.00 -0.01
        GM.5.0.C2H2_ZF.0126
        ZFX,ZFY,ZFY1,ZFA,ZFP711,(...)
        		 logos/GM_5_0_C2H2_ZF_0126.png -0.98 -2.82 2.82 2.03 -2.86 -2.27 -3.36 -0.48 0.77 -0.00 -0.01 0.01 -0.00 -0.01 -0.00 -0.00 0.02
        GM.5.0.Homeodomain.0029
        ONECUT1,CUX2,CUX1,ONECUT3,ONECUT2,(...)
        		 logos/GM_5_0_Homeodomain_0029.png 2.40 2.09 3.00 0.21 -2.30 -3.29 -2.38 -1.97 0.75 0.02 0.03 0.01 -0.00 -0.03 -0.02 -0.02 0.01
        GM.5.0.Mixed.0096
        POU2F2
        		 logos/GM_5_0_Mixed_0096.png -3.03 2.00 -1.02 -1.64 0.29 0.81 0.73 0.52 1.10 0.00 0.02 -0.02 -0.03 0.01 0.01 0.01 0.03
        GM.5.0.C2H2_ZF.0107
        ZNF524,ZFP524
        		 logos/GM_5_0_C2H2_ZF_0107.png 0.88 0.46 0.90 1.28 -1.29 -1.17 -2.50 -3.37 0.87 0.01 0.01 0.02 0.02 -0.03 -0.01 -0.02 -0.02
        GM.5.0.Homeodomain_POU.0024
        CCDC6,POU3F1,POU3F2
        		 logos/GM_5_0_Homeodomain_POU_0024.png 0.66 -0.03 1.14 -0.38 0.43 -3.01 -0.16 1.13 1.13 0.02 0.02 -0.01 -0.02 -0.00 -0.02 -0.00 0.02
        GM.5.0.Nuclear_receptor.0099
        HNF4A,HNF4G
        		 logos/GM_5_0_Nuclear_receptor_0099.png 0.01 -3.48 1.76 0.47 -0.66 -0.33 1.21 0.05 1.05 -0.00 -0.01 0.00 0.00 -0.00 -0.00 0.01 0.00
        GM.5.0.Myb_SANT.0016
        MYB
        		 logos/GM_5_0_Myb_SANT_0016.png -0.99 -0.07 -1.80 -1.68 0.95 2.08 3.45 1.61 1.18 -0.00 0.01 -0.01 -0.03 0.01 0.01 0.02 0.02
        GM.5.0.Homeodomain.0037
        HOMEZ
        		 logos/GM_5_0_Homeodomain_0037.png -1.47 2.21 -3.03 -2.59 1.16 1.69 1.82 0.94 0.80 0.01 0.03 -0.03 -0.04 0.01 0.00 0.01 0.03
        GM.5.0.Homeodomain.0140
        DMRT2,DMRT1,DMRTC2,DMRTA1,DMRT3,(...)
        		 logos/GM_5_0_Homeodomain_0140.png -0.59 1.77 2.21 3.20 -2.19 -2.51 -2.15 -3.09 0.79 0.01 0.01 0.04 0.05 -0.05 -0.04 -0.05 -0.04
        GM.5.0.C2H2_ZF_Homeodomain.0002
        ZEB1,TBX3
        		 logos/GM_5_0_C2H2_ZF_Homeodomain_0002.png 3.19 3.14 2.69 2.73 -3.48 -3.30 -4.05 -4.20 0.00 0.04 0.04 0.03 0.03 -0.05 -0.04 -0.03 -0.06
        GM.5.0.SMAD.0002
        NFIC,NFIA,NFIX,NFIB,NFYA
        		 logos/GM_5_0_SMAD_0002.png 3.29 -2.44 2.50 1.32 -2.96 -3.46 -1.99 3.24 0.00 0.01 -0.01 0.01 -0.01 -0.01 -0.02 -0.01 0.03
        GM.5.0.Mixed.0022
        BCLAF1,YY1,NFE2,ELF1,SIN3A,(...)
        		 logos/GM_5_0_Mixed_0022.png 3.46 1.76 -0.53 -1.87 0.58 -0.01 0.46 0.66 1.10 0.04 0.04 -0.02 -0.04 -0.01 -0.01 -0.01 0.05
        GM.5.0.AT_hook.0004
        HMGA1,IRF7
        		 logos/GM_5_0_AT_hook_0004.png 1.81 1.41 0.67 -3.38 -0.80 -0.93 0.69 0.50 1.09 0.02 0.03 -0.02 -0.05 0.01 -0.00 0.01 0.05
        GM.5.0.Homeodomain.0189
        SIX4,SIX1,SIX2
        		 logos/GM_5_0_Homeodomain_0189.png 0.64 0.01 -2.65 -3.18 2.22 2.82 2.11 1.36 1.22 -0.01 0.01 -0.04 -0.05 0.05 0.04 0.04 0.03
        GM.5.0.C2H2_ZF.0286
        TERF2,SP9,SP3
        		 logos/GM_5_0_C2H2_ZF_0286.png -1.43 0.28 -1.75 -1.48 3.23 2.47 1.38 -0.83 4.32 -0.00 0.01 -0.01 -0.00 0.00 0.01 -0.00 -0.01
        GM.5.0.Unknown.0198
        		logos/GM_5_0_Unknown_0198.png 3.20 -1.73 1.08 -0.39 -1.29 -2.81 -1.73 2.13 1.14 0.01 -0.01 0.00 -0.01 -0.01 -0.02 -0.01 0.03
        GM.5.0.bZIP.0079
        FOS
        		 logos/GM_5_0_bZIP_0079.png -2.49 -0.88 -1.32 0.05 1.81 2.80 1.50 -3.19 1.12 -0.02 -0.02 -0.01 0.00 0.03 0.03 0.02 -0.03
        GM.5.0.bZIP.0092
        BATF
        		 logos/GM_5_0_bZIP_0092.png -3.07 -1.11 -1.59 -2.71 2.50 2.05 3.14 -0.46 1.11 -0.02 -0.01 -0.02 -0.03 0.03 0.02 0.03 0.02
        GM.5.0.STAT.0007
        STAT3,STAT1,STAT5A,STAT5B,BCL6B,(...)
        		 logos/GM_5_0_STAT_0007.png -2.60 -1.01 -0.66 -1.58 2.62 3.25 2.61 0.99 1.54 0.00 0.02 -0.03 -0.05 0.03 0.02 0.03 0.03
        GM.5.0.Rel.0010
        NFAT5,NFATC2,NFATC1,NFATC3,NFATC4,(...)
        		 logos/GM_5_0_Rel_0010.png 3.17 2.96 0.80 0.58 -2.09 -3.96 -3.04 -2.02 1.59 0.02 0.03 -0.02 -0.04 0.01 -0.01 0.01 0.04
        GM.5.0.C2H2_ZF.0020
        EGR1,EGR4,EGR2,EGR3,RREB1,(...)
        		 logos/GM_5_0_C2H2_ZF_0020.png -3.41 -0.42 3.99 3.42 -2.67 -3.27 -3.62 2.18 1.49 0.00 0.01 0.03 0.02 -0.05 -0.04 -0.03 0.01
        GM.5.0.bHLH.0006
        NEUROD1,NEUROG2,NEUROD2,ATOH1,OLIG2,(...)
        		 logos/GM_5_0_bHLH_0006.png 2.66 3.41 2.14 3.69 -2.78 -3.42 -4.17 2.30 1.54 0.01 0.01 0.00 0.00 -0.01 -0.02 -0.02 0.02
        GM.5.0.IRF.0010
        IRF5,IRF6,IRF2,IRF4
        		 logos/GM_5_0_IRF_0010.png -2.93 0.28 -2.71 -2.22 0.37 0.61 1.05 3.33 1.04 -0.00 0.02 -0.03 -0.04 0.02 0.01 0.02 0.06
        GM.5.0.Unknown.0181
        ZNF317
        		 logos/GM_5_0_Unknown_0181.png -1.95 0.67 -1.12 -0.59 1.74 2.46 3.33 -0.87 1.52 -0.01 -0.00 -0.02 -0.02 0.04 0.02 0.03 0.01
        GM.5.0.C2H2_ZF.0196
        REST,BCL3,SIN3A
        		 logos/GM_5_0_C2H2_ZF_0196.png 1.69 -0.28 2.13 3.04 -2.38 -1.66 -2.46 -2.21 1.62 0.01 -0.01 0.01 0.01 -0.01 -0.01 -0.01 -0.00
        GM.5.0.C2H2_ZF.0182
        EGR3,NPAS1,NPAS3,EGR1,ZFP3,(...)
        		 logos/GM_5_0_C2H2_ZF_0182.png 0.67 1.97 2.30 1.80 -2.88 -2.33 -3.07 1.83 1.64 0.00 -0.00 0.03 0.04 -0.05 -0.04 -0.03 -0.01
        GM.5.0.Unknown.0009
        TAF1,NRF1,TCF12,GABPA
        		 logos/GM_5_0_Unknown_0009.png 1.13 1.37 -3.20 -2.70 1.97 0.88 1.40 3.02 2.26 0.00 0.04 -0.07 -0.07 0.05 0.04 0.04 0.06
        GM.5.0.STAT.0023
        STAT1,RBPJ,IKZF1,IKZF4,IKZF2
        		 logos/GM_5_0_STAT_0023.png -1.47 0.48 -2.26 -2.53 4.20 3.51 2.43 1.30 2.53 -0.00 0.03 -0.06 -0.06 0.05 0.04 0.04 0.05
        GM.5.0.Unknown.0001
        HES1,HES2,HES4,RAD21,SMC3
        		 logos/GM_5_0_Unknown_0001.png 3.21 2.04 -3.16 -1.25 0.20 2.77 2.61 1.42 1.95 0.04 0.03 -0.01 -0.02 -0.02 -0.01 -0.01 0.02
        GM.5.0.Runt.0003
        RUNX2,RUNX3,RUNX1,ENSG00000250096,CBFB,(...)
        		 logos/GM_5_0_Runt_0003.png -4.14 -3.08 -1.53 -3.42 3.59 3.16 3.94 2.09 2.23 -0.04 -0.04 -0.00 -0.02 0.03 0.04 0.04 0.01
        GM.5.0.bZIP.0021
        BATF3,JUND
        		 logos/GM_5_0_bZIP_0021.png -2.65 -2.27 -1.67 -1.75 2.50 2.36 3.15 3.30 1.84 -0.02 -0.01 -0.03 -0.03 0.04 0.03 0.03 0.04
        GM.5.0.C2H2_ZF.0178
        PRDM1
        		 logos/GM_5_0_C2H2_ZF_0178.png -2.19 2.97 -3.18 -1.26 2.37 1.48 2.40 0.97 2.31 -0.01 0.01 -0.03 -0.03 0.03 0.02 0.03 0.02
        GM.5.0.Mixed.0029
        PAX3,PAX5,PAX8,PAX1,PAX9
        		 logos/GM_5_0_Mixed_0029.png -0.81 -2.40 -3.51 -1.57 1.86 2.24 2.74 1.89 1.74 -0.02 -0.02 -0.03 -0.02 0.04 0.04 0.04 0.00
        GM.5.0.Unknown.0068
        ZBTB14,E2F1,ZBTB33
        		 logos/GM_5_0_Unknown_0068.png 2.55 3.85 -0.60 -0.79 -1.31 0.90 0.30 0.70 1.98 0.04 0.05 -0.02 -0.03 -0.02 -0.01 -0.01 0.03
        GM.5.0.Unknown.0200
        SP5,SP9,SP8,SP3,TAF1
        		 logos/GM_5_0_Unknown_0200.png -1.41 -0.81 -1.78 -2.58 3.80 3.34 2.35 1.63 1.71 0.00 0.03 -0.06 -0.07 0.05 0.05 0.05 0.05
        GM.5.0.Unknown.0085
        		logos/GM_5_0_Unknown_0085.png 3.02 2.87 -2.21 -1.23 -1.17 -0.24 0.19 1.52 2.56 0.02 0.02 -0.01 -0.02 -0.01 -0.01 -0.00 0.02
        GM.5.0.GATA.0004
        GATA3,GATA5,GATA6,GATA1,GATA4,(...)
        		 logos/GM_5_0_GATA_0004.png 2.80 2.47 1.72 1.37 -2.72 -4.31 -3.18 -2.43 0.00 0.02 0.02 0.01 0.01 -0.03 -0.03 -0.03 -0.01
        GM.5.0.Rel.0006
        NFKB1,NFKB2,RELA,NFKB,REL,(...)
        		 logos/GM_5_0_Rel_0006.png 1.77 2.35 -2.69 2.97 -2.19 -2.10 -3.02 3.84 2.57 0.01 0.02 -0.02 -0.01 0.00 -0.00 -0.01 0.04
        GM.5.0.MADS_box.0014
        MEF2A,MYEF2,MEF2C,MEF2D,AC002126.6,(...)
        		 logos/GM_5_0_MADS_box_0014.png -0.32 -2.88 0.52 1.03 3.23 0.05 -0.74 1.68 2.06 0.01 0.00 -0.01 -0.02 0.01 -0.00 0.00 0.04
        GM.5.0.bZIP.0052
        CEBPB,CEBPA,CEBPD,DBP,CEBPE,(...)
        		 logos/GM_5_0_bZIP_0052.png -3.90 -4.14 -2.92 -3.01 3.05 3.77 4.18 1.75 2.82 -0.05 -0.05 -0.02 -0.07 0.07 0.07 0.09 0.03
        GM.5.0.bZIP.0086
        MAF,NFE2L2,GABP
        		 logos/GM_5_0_bZIP_0086.png -1.41 -0.63 -3.02 -1.95 2.89 2.70 2.84 2.41 3.11 -0.01 0.01 -0.04 -0.04 0.04 0.04 0.04 0.02
        GM.5.0.IRF.0006
        IRF1,IRF9,IRF2,IRF7,IRF3,(...)
        		 logos/GM_5_0_IRF_0006.png -0.41 2.19 -2.24 1.24 1.91 -2.58 -1.55 3.20 2.77 -0.00 0.04 -0.07 -0.06 0.04 0.03 0.03 0.08
        GM.5.0.bZIP.0037
        MAFK,MAFF,MAFG,MAF,MAFA,(...)
        		 logos/GM_5_0_bZIP_0037.png 0.00 1.01 -2.73 -1.28 2.62 3.35 2.22 -1.53 2.52 -0.02 -0.02 -0.03 -0.04 0.08 0.05 0.05 -0.01
        GM.5.0.Mixed.0051
        FEV,ETV2,ERF,ETS2,ENSMUSG00000044690,(...)
        		 logos/GM_5_0_Mixed_0051.png -3.17 0.17 -3.61 -2.43 4.15 3.72 3.59 -0.41 3.75 -0.02 0.02 -0.09 -0.08 0.10 0.08 0.08 0.04
        GM.5.0.C2H2_ZF.0243
        ZBTB7A,RXRA
        		 logos/GM_5_0_C2H2_ZF_0243.png 2.76 2.77 -0.43 0.01 -1.08 -3.18 -0.79 -0.55 2.84 0.07 0.06 -0.01 -0.01 -0.05 -0.04 -0.04 0.01
        GM.5.0.Mixed.0079
        CTCF,HIC1,CTCFL,RAD21,SMC3
        		 logos/GM_5_0_Mixed_0079.png 2.75 2.65 -3.25 -2.80 -1.53 -0.34 0.93 1.97 3.56 0.05 0.05 -0.02 -0.03 -0.03 -0.02 -0.02 0.03
        GM.5.0.C2H2_ZF.0062
        IRF4,BCL11A,IRF8,BCL11B,BCL3,(...)
        		 logos/GM_5_0_C2H2_ZF_0062.png 2.05 2.64 -3.52 1.45 -3.16 -2.35 -4.25 3.47 5.48 -0.01 0.03 -0.07 -0.05 0.05 0.04 0.03 0.07
        GM.5.0.bZIP.0013
        NFE2,FOS,JUN,JUND,FOSB,(...)
        		 logos/GM_5_0_bZIP_0013.png -3.05 -3.65 -3.13 -2.75 5.08 4.46 4.48 0.33 6.49 -0.04 -0.04 -0.05 -0.05 0.12 0.08 0.09 -0.02
        GM.5.0.Ets.0015
        SPIB,SPI1,SPIC,ETS2,FEV,(...)
        		 logos/GM_5_0_Ets_0015.png 3.13 3.47 -5.18 -4.42 4.40 4.26 3.36 -3.65 10.95 -0.01 0.04 -0.11 -0.09 0.10 0.09 0.08 0.04
        GM.5.0.C2H2_ZF.0023
        CTCFL,CTCF,RAD21,MYC,MAX,(...)
        		 logos/GM_5_0_C2H2_ZF_0023.png 4.86 3.79 -2.65 -3.66 -4.05 -1.33 -4.07 3.32 7.70 0.10 0.08 -0.02 -0.03 -0.06 -0.05 -0.05 0.04

        Samples & Config

        The samples file used for this run:

        sample assembly technical_replicates _brep conditions
        SS-NAM-1_S5_L001 GRCm38.p6 SS-NAM-1 Naive-NAM-1 naive
        SS-NAM-1_S5_L002 GRCm38.p6 SS-NAM-1 Naive-NAM-1 naive
        SS-NAM-2_S6_L001 GRCm38.p6 SS-NAM-2 Naive-NAM-2 naive
        SS-NAM-2_S6_L002 GRCm38.p6 SS-NAM-2 Naive-NAM-2 naive
        SS-NAM-3_S7_L001 GRCm38.p6 SS-NAM-3 Naive-NAM-3 naive
        SS-NAM-3_S7_L002 GRCm38.p6 SS-NAM-3 Naive-NAM-3 naive
        SS-NAM-4_S8_L001 GRCm38.p6 SS-NAM-4 Naive-NAM-4 naive
        SS-NAM-4_S8_L002 GRCm38.p6 SS-NAM-4 Naive-NAM-4 naive
        Tr-NAM-1_S12_L001 GRCm38.p6 Tr-NAM-1 Trained-NAM-1 trained
        Tr-NAM-1_S12_L002 GRCm38.p6 Tr-NAM-1 Trained-NAM-1 trained
        Tr-NAM-2_S13_L001 GRCm38.p6 Tr-NAM-2 Trained-NAM-2 trained
        Tr-NAM-2_S13_L002 GRCm38.p6 Tr-NAM-2 Trained-NAM-2 trained
        Tr-NAM-3_S14_L001 GRCm38.p6 Tr-NAM-3 Trained-NAM-3 trained
        Tr-NAM-3_S14_L002 GRCm38.p6 Tr-NAM-3 Trained-NAM-3 trained
        Tr-NAM-4_S15_L001 GRCm38.p6 Tr-NAM-4 Trained-NAM-4 trained
        Tr-NAM-4_S15_L002 GRCm38.p6 Tr-NAM-4 Trained-NAM-4 trained

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

# pipeline file locations
result_dir: /scratch/siebrenf/yavor/no_blacklist/results  # where to store results
genome_dir: /scratch/siebrenf/genomes  # where to look for or download the genomes
fastq_dir: /scratch/siebrenf/yavor/fastq  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
# email: yavor@mhlangalab.org

# produce a UCSC trackhub?
create_trackhub: true
create_qc_report: 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: false # <---------------------------------------- 
remove_mito: true
tn5_shift: true
min_mapping_quality: 30
only_primary_align: true
max_template_length: 150
remove_dups: true

# should the final output be stored as cram files (instead of bam) to save storage?
store_as_cram: false

# macs2 ignores the mates in a paired sequencing sample. with this option enabled
# seq2science removes the mate information after alignment, so all reads are used
macs2_keep_mates: 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

# how much peak summits will be extended by (on each side) for the final count table
# (e.g. 100 means a 200 bp wide peak)
slop: 100

# whether or not to run gimme maelstrom to infer differential motifs
run_gimme_maelstrom: true
infer_motif2factors: false
motif2factors_reference: []

# differential accessibility analysis
# for explanation, see: https://vanheeringen-lab.github.io/seq2science/content/DESeq2.html
contrasts:
  - conditions_trained_naive

        MultiQC v1.14 - Written by Phil Ewels, available on GitHub.

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