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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_hg38_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 v0.5.4, 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
        rna-seq
        Date
        August 30, 2021
        Project
        ghe_2021
        Contact E-mail
        yourmail@here.com

        Report generated on 2021-08-30, 15:00 based on data in:

        Change sample names:

        General Statistics

        Showing 12/12 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
        NP_Day10_supernatant_DMSO_A72_S3
        17.4%
        49.8%
        95.9%
        0.0%
        237 bp
        22.7%
        100.0%
        39.5
        100.0%
        33.5
        0.9 X
        63.7
        0.9
        NP_Day10_supernatant_TA_A71_S7
        18.5%
        51.1%
        95.0%
        0.0%
        238 bp
        23.7%
        100.0%
        40.6
        100.0%
        28.1
        1.5 X
        107.5
        0.7
        NP_Day12_adherent_DMSO_A61_S9
        7.6%
        49.3%
        97.5%
        0.0%
        255 bp
        14.0%
        100.0%
        61.8
        100.0%
        55.2
        1.2 X
        85.9
        1.4
        NP_Day12_adherent_TA_A60_S4
        9.6%
        47.6%
        96.6%
        0.1%
        271 bp
        14.6%
        100.0%
        40.7
        100.0%
        37.1
        0.7 X
        51.8
        1.3
        NP_Day12_supernatant_DMSO_A74_S8
        11.8%
        48.9%
        97.5%
        0.0%
        246 bp
        18.0%
        100.0%
        44.7
        100.0%
        39.5
        0.9 X
        63.7
        1.4
        NP_Day12_supernatant_TA_A73_S10
        6.6%
        48.1%
        97.0%
        0.0%
        253 bp
        12.8%
        100.0%
        38.9
        100.0%
        34.6
        0.7 X
        53.7
        1.1
        NP_Day19_adherent_DMSO_A65_S2
        27.5%
        51.7%
        95.7%
        0.0%
        223 bp
        33.5%
        100.0%
        42.8
        100.0%
        31.7
        1.4 X
        100.5
        0.7
        NP_Day19_adherent_TA_A64_S1
        32.0%
        50.5%
        96.2%
        0.1%
        194 bp
        37.4%
        100.0%
        36.1
        100.0%
        32.8
        0.6 X
        46.5
        0.4
        NP_Day19_supernatant_DMSO_A63_S6
        5.3%
        48.9%
        96.8%
        0.1%
        209 bp
        9.7%
        100.0%
        45.0
        100.0%
        40.6
        0.8 X
        60.1
        0.8
        NP_Day19_supernatant_TA_A62_S5
        10.0%
        49.7%
        96.8%
        0.1%
        246 bp
        14.1%
        100.0%
        41.8
        100.0%
        36.7
        0.8 X
        62.3
        0.8
        NP_Day1_adherentcells_DMSO_A70_S14
        4.5%
        51.2%
        95.7%
        0.0%
        260 bp
        9.1%
        100.0%
        47.9
        100.0%
        34.5
        1.6 X
        118.6
        0.9
        NP_Day1_adherentcells_TA_A66_S11
        7.3%
        49.9%
        96.5%
        0.0%
        250 bp
        11.8%
        100.0%
        46.4
        100.0%
        39.5
        1.0 X
        75.3
        0.7

        Workflow explanation

        Preprocessing of reads was done automatically with workflow tool seq2science v0.5.4. Paired-end reads were trimmed with fastp v0.20.1 with default options. Genome assembly hg38 was downloaded with genomepy 0.9.3. Reads were aligned with STAR v2.7.6a with default options. 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. General alignment statistics were collected by samtools stats v1.11. Afterwards, duplicate reads were marked with Picard MarkDuplicates v2.23.8. 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'. Read counting and summarizing to gene-level was performed on filtered bam using HTSeq-count v0.12.4. RNA-seq read duplication types were analyzed using dupRadar v1.20.0. The UCSC genome browser was used to visualize and inspect alignment. Differential gene expression analysis was performed using DESeq2 v1.30.1. 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 v1.12.0. 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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        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).

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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.

        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 old_sample_name assembly day fraction compound batch descriptive_name
        NP_Day1_adherentcells_TA_A66_S11 NP_Day1_adherentcells_TA hg38 1 adherent TA adherent_TA Day1_adherentcells_TA
        NP_Day1_adherentcells_DMSO_A70_S14 NP_Day1_adherentcells_DMSO hg38 1 adherent DMSO adherent_DMSO Day1_adherentcells_DMSO
        NP_Day10_supernatant_TA_A71_S7 NP_Day10_supernatant_TA hg38 10 supernatant TA supernatant_TA Day10_supernatant_TA
        NP_Day10_supernatant_DMSO_A72_S3 NP_Day10_supernatant_DMSO hg38 10 supernatant DMSO supernatant_DMSO Day10_supernatant_DMSO
        NP_Day12_supernatant_TA_A73_S10 NP_Day12_supernatant_TA hg38 12 supernatant TA supernatant_TA Day12_supernatant_TA
        NP_Day12_supernatant_DMSO_A74_S8 NP_Day12_supernatant_DMSO hg38 12 supernatant DMSO supernatant_DMSO Day12_supernatant_DMSO
        NP_Day12_adherent_TA_A60_S4 NP_Day12_adherent_TA hg38 12 adherent TA adherent_TA Day12_adherent_TA
        NP_Day12_adherent_DMSO_A61_S9 NP_Day12_adherent_DMSO hg38 12 adherent DMSO adherent_DMSO Day12_adherent_DMSO
        NP_Day19_supernatant_TA_A62_S5 NP_Day19_supernatant_TA hg38 19 supernatant TA supernatant_TA Day19_supernatant_TA
        NP_Day19_supernatant_DMSO_A63_S6 NP_Day19_supernatant_DMSO hg38 19 supernatant DMSO supernatant_DMSO Day19_supernatant_DMSO
        NP_Day19_adherent_TA_A64_S1 NP_Day19_adherent_TA hg38 19 adherent TA adherent_TA Day19_adherent_TA
        NP_Day19_adherent_DMSO_A65_S2 NP_Day19_adherent_DMSO hg38 19 adherent DMSO adherent_DMSO Day19_adherent_DMSO

        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/moldevbio/heeringen/siebrenf/genomes/
fastq_dir: /ceph/rimlsfnwi/data/moldevbio/heeringen/siebrenf/ghe_2021/fastq/


# 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 quantifier to use
quantifier: htseq  # 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)
markduplicates: -Xms4G -Xmx6G MAX_FILE_HANDLES_FOR_READ_ENDS_MAP=999  # keep duplicates (check dupRadar in the MultiQC)
remove_blacklist: true
min_mapping_quality: 255  # (only keep uniquely mapped reads from STAR alignments)
only_primary_align: true

# differential gene expression analysis
contrasts:
  - 'batch+day_19_12'

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

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