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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 1.11 (9fc53f9)

        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 v0.8.0, 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
        May 06, 2022
        Project
        hendrik_morsche
        Contact E-mail
        none@provided.com

        Report generated on 2022-05-06, 23:27 based on data in:

        Change sample names:

        General Statistics

        Showing 8/8 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
        White-Bloodcells-Morsche-MDL1-C-nonstimulated-0-16238
        11.2%
        51.7%
        98.2%
        0.1%
        203 bp
        22.7%
        100.0%
        39.3
        100.0%
        33.8
        0.8 X
        58.4
        0.5
        White-Bloodcells-Morsche-MDL2-C-nonstimulated-1-16239
        7.9%
        52.5%
        98.0%
        0.1%
        189 bp
        20.8%
        100.0%
        39.8
        100.0%
        33.7
        0.8 X
        60.6
        0.6
        White-Bloodcells-Morsche-MDL3-C-stimulated-0-16240
        17.3%
        52.4%
        97.8%
        0.1%
        190 bp
        29.3%
        100.0%
        43.0
        100.0%
        36.4
        0.9 X
        67.2
        0.4
        White-Bloodcells-Morsche-MDL4-C-stimulated-1-16241
        14.8%
        52.0%
        98.1%
        0.1%
        184 bp
        27.7%
        100.0%
        41.4
        100.0%
        35.2
        0.9 X
        62.5
        0.5
        White-Bloodcells-Morsche-MDL5-L-nonstimulated-0-16242
        22.2%
        51.0%
        98.0%
        0.1%
        190 bp
        31.2%
        100.0%
        45.3
        100.0%
        39.6
        0.9 X
        65.6
        0.4
        White-Bloodcells-Morsche-MDL6-L-nonstimulated-1-16243
        16.9%
        50.5%
        98.1%
        0.1%
        191 bp
        24.8%
        100.0%
        36.5
        100.0%
        32.2
        0.7 X
        51.7
        0.3
        White-Bloodcells-Morsche-MDL7-L-stimulated-0-16244
        6.0%
        50.4%
        97.6%
        0.1%
        194 bp
        15.7%
        100.0%
        32.1
        100.0%
        28.1
        0.6 X
        45.8
        0.4
        White-Bloodcells-Morsche-MDL8-L-stimulated-1-16245
        5.5%
        52.5%
        98.0%
        0.1%
        191 bp
        29.9%
        100.0%
        38.4
        100.0%
        26.1
        1.5 X
        105.6
        0.4

        Workflow explanation

        Assembly stats

        Genome assembly GRCh38.p13 contains of 194 contigs, with a GC-content of 40.86%, and 4.96% consists of the letter N. The N50-L50 stats are 145138636-9 and the N75-L75 stats are 114364328-14. The genome annotation contains 59653 genes.

        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 assembly descriptive_name additive stimulation incubation number 2v2 2v1a 2v1b 1av2 1bv2
        White-Bloodcells-Morsche-MDL1-C-nonstimulated-0-16238 GRCh38.p13 MDL1-CU0 Candida no 48h zero control control control control
        White-Bloodcells-Morsche-MDL2-C-nonstimulated-1-16239 GRCh38.p13 MDL2-CU1 Candida no 48h one treated treated treated treated
        White-Bloodcells-Morsche-MDL3-C-stimulated-0-16240 GRCh38.p13 MDL3-CS0 Candida yes 48h zero control.stim
        White-Bloodcells-Morsche-MDL4-C-stimulated-1-16241 GRCh38.p13 MDL4-CS1 Candida yes 48h one treated.stim
        White-Bloodcells-Morsche-MDL5-L-nonstimulated-0-16242 GRCh38.p13 MDL5-LU0 LPS no 24h zero control control control control
        White-Bloodcells-Morsche-MDL6-L-nonstimulated-1-16243 GRCh38.p13 MDL6-LU1 LPS no 24h one treated treated treated treated
        White-Bloodcells-Morsche-MDL7-L-stimulated-0-16244 GRCh38.p13 MDL7-LS0 LPS yes 24h zero control.stim
        White-Bloodcells-Morsche-MDL8-L-stimulated-1-16245 GRCh38.p13 MDL8-LS1 LPS yes 24h one treated.stim

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

# pipeline file locations
result_dir: ~/ceph/hendrik_morsche/2022  # where to store results
genome_dir: ~/ceph/genomes  # where to look for or download the genomes
fastq_dir: ~/ceph/hendrik_morsche/fastq  # where to look for or download the fastqs

# produce a UCSC trackhub?
create_trackhub: true
generate_qc_report: 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)
remove_blacklist: true
min_mapping_quality: 255  # (only keep uniquely mapped reads from STAR alignments)
only_primary_align: true
remove_dups: false # keep duplicates (check dupRadar in the MultiQC)

# differential gene expression analysis
# for explanation, see: https://vanheeringen-lab.github.io/seq2science/content/DESeq2.html
#contrasts:
  #- '2v2_treated_control'
  #- '1av2_treated_control'
  #- '2v1a_treated_control'
  #- 'incubation+2v2_treated_control'
  #- '2v1b_treated_control'
  #- '1bv2_treated_control'

        MultiQC v1.11 (9fc53f9) - Written by Phil Ewels, available on GitHub.

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