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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
        rna-seq
        Date
        October 11, 2022
        Project
        metta
        Contact E-mail
        slrinzema@science.ru.nl

        Report generated on 2022-10-11, 22:55 based on data in:

        Change sample names:

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        General Statistics

        Showing 23/23 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
        BoneMarrow
        18.4%
        49.2%
        99.9%
        1.0%
        66.5%
        100.0%
        20.4
        0.0%
        12.0
        1.7 X
        70.3
        1.9
        CRISPR
        82.8%
        50.8%
        100.0%
        0.0%
        164 bp
        92.1%
        100.0%
        329.7
        100.0%
        294.9
        13.4 X
        417.4
        46.4
        CordBlood
        30.3%
        51.7%
        100.0%
        0.0%
        306 bp
        59.5%
        100.0%
        110.3
        100.0%
        100.9
        5.8 X
        119.9
        20.6
        DMSO_1
        3.7%
        48.7%
        100.0%
        0.0%
        222 bp
        16.5%
        100.0%
        52.2
        100.0%
        45.7
        1.0 X
        73.0
        2.0
        DMSO_2
        18.3%
        50.9%
        100.0%
        0.0%
        212 bp
        32.2%
        100.0%
        90.8
        100.0%
        78.5
        1.8 X
        131.0
        3.1
        DMSO_3
        1.7%
        48.4%
        100.0%
        0.0%
        196 bp
        10.2%
        100.0%
        13.7
        100.0%
        12.2
        0.2 X
        17.8
        0.5
        GSM5034742
        26.1%
        51.8%
        99.1%
        1.2%
        GSM5034743
        32.4%
        51.5%
        99.1%
        1.2%
        GSM5739833
        78.9%
        50.6%
        99.0%
        11.7%
        GSM5739838
        85.1%
        51.8%
        98.8%
        9.6%
        GSM5739842
        85.6%
        49.9%
        98.7%
        11.4%
        GSM5772725
        19.0%
        49.2%
        99.3%
        13.7%
        GSM5772761
        27.5%
        47.3%
        98.5%
        12.2%
        GSM5772762
        22.6%
        50.4%
        99.0%
        23.4%
        GSM5772763
        25.9%
        46.6%
        98.3%
        10.5%
        GSM5772764
        16.2%
        48.8%
        98.7%
        17.1%
        GSM6133152
        37.3%
        49.9%
        97.5%
        5.5%
        GSM6133153
        40.9%
        48.8%
        98.5%
        3.0%
        HSCD12_DMSO_1tec
        3.7%
        48.7%
        97.4%
        0.0%
        HSCD12_DMSO_2tec
        18.0%
        50.9%
        96.9%
        0.1%
        HSCD12_DMSO_3tec
        1.7%
        48.4%
        96.5%
        0.1%
        Neutrophil
        25.3%
        49.2%
        100.0%
        74.8%
        100.0%
        81.8
        0.0%
        59.2
        5.0 X
        218.8
        1.1
        Skin
        46.4%
        49.3%
        100.0%
        70.7%
        100.0%
        32.4
        0.0%
        29.4
        1.0 X
        39.1
        0.6

        Workflow explanation

        Assembly stats

        Genome assembly GRCh38 contains of 194 contigs, with a GC-content of 40.87%, and 4.88% consists of the letter N. The N50-L50 stats are 145138636-9 and the N75-L75 stats are 114364328-14. The genome annotation contains 60645 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 technical_replicates descriptivate_name
        HSCD12_DMSO_1tec GRCh38 DMSO_1 HSCD12_DMSO_1tec
        HSCD12_DMSO_2tec GRCh38 DMSO_2 HSCD12_DMSO_2tec
        HSCD12_DMSO_3tec GRCh38 DMSO_3 HSCD12_DMSO_3tec
        GSM5739838 GRCh38 CRISPR CRISPR_1
        GSM5739833 GRCh38 CRISPR CRISPR_2
        GSM5739842 GRCh38 CRISPR CRISPR_3
        GSM5034742 GRCh38 CordBlood CordBlood_1
        GSM5034743 GRCh38 CordBlood CordBlood_2
        GSM6133153 GRCh38 Skin Skin_1
        GSM6133152 GRCh38 Skin Skin_2
        GSM5772725 GRCh38 BoneMarrow BoneMarrow_1
        GSM5772761 GRCh38 Neutrophil Neutrophil_1
        GSM5772762 GRCh38 Neutrophil Neutrophil_2
        GSM5772763 GRCh38 Neutrophil Neutrophil_3
        GSM5772764 GRCh38 Neutrophil Neutrophil_4

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

# pipeline file locations
result_dir: ./  # where to store results
genome_dir: ../genome  # where to look for or download the genomes
fastq_dir: ../fastqs_all  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: slrinzema@science.ru.nl

# 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)
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:
#  - 'stage_2_1'

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

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