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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 1.9

        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 v0.4.3, 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.

        Contact E-mail
        Jsmits@science.ru.nl
        Workflow
        RNAseq
        Date
        February 11, 2021

        Report generated on 2021-02-12, 16:05 based on data in:

        Change sample names:

        General Statistics

        Showing 11/11 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
        GSM4728059
        15.1%
        54.5%
        98.6%
        4.6%
        429 bp
        35.5%
        100.0%
        54.7
        100.0%
        34.5
        2.7 X
        56.1
        1.1
        GSM4728060
        11.7%
        55.6%
        98.6%
        2.1%
        367 bp
        34.8%
        100.0%
        51.3
        100.0%
        32.6
        2.6 X
        53.3
        1.3
        GSM915329
        19.0%
        46.6%
        93.3%
        0.2%
        249 bp
        56.2%
        100.0%
        393.4
        100.0%
        164.3
        11.3 X
        346.9
        86.2
        GSM958733
        4.5%
        50.7%
        69.5%
        1.3%
        185 bp
        33.4%
        100.0%
        256.2
        100.0%
        159.2
        7.4 X
        290.4
        16.9
        PKC19_RNA_1
        2.0%
        55.2%
        96.9%
        0.0%
        312 bp
        19.3%
        100.0%
        36.0
        100.0%
        22.6
        0.9 X
        63.2
        0.5
        PKC19_RNA_2
        2.2%
        57.9%
        96.2%
        0.0%
        334 bp
        23.2%
        100.0%
        39.7
        100.0%
        21.6
        1.2 X
        85.9
        0.4
        PKC19_RNA_3JQ
        6.0%
        51.5%
        96.7%
        0.2%
        177 bp
        35.6%
        100.0%
        61.3
        100.0%
        28.3
        1.4 X
        101.3
        0.1
        PRIM_RNA_1
        3.9%
        55.7%
        96.1%
        0.0%
        360 bp
        22.2%
        100.0%
        31.0
        100.0%
        18.6
        0.7 X
        56.8
        0.3
        PRIM_RNA_2
        2.1%
        55.9%
        96.0%
        0.0%
        389 bp
        19.7%
        100.0%
        31.1
        100.0%
        19.0
        0.7 X
        58.7
        0.4
        SRR5499067
        6.0%
        51.6%
        96.8%
        0.2%
        177 bp
        35.6%
        100.0%
        61.3
        100.0%
        28.3
        1.4 X
        101.3
        0.1
        SRR5499071
        14.2%
        33.3%
        94.8%
        3.4%
        121 bp
        46.5%
        100.0%
        43.7
        100.0%
        16.0
        1.3 X
        95.2
        0.1

        Sample clustering

        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, 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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        Pileup (BAM) Spearman correlation

        Spearman correlation plot generated by deeptools. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.

        Pileup (BAM) Pearson correlation

        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.

        Samples & Config

        The samples file used for this run:

        sample assembly descriptive_name cell_type
        SRR5499071 hg38 KC_1 KC
        SRR5499067 hg38 KC_2 KC
        PKC19_RNA_3JQ hg38 KC_3 KC
        PKC19_RNA_1 hg38 KC_4 KC
        PKC19_RNA_2 hg38 KC_5 KC
        PRIM_RNA_1 hg38 LSC_RNA_1 LSC
        PRIM_RNA_2 hg38 LSC_RNA_2 LSC
        GSM4728059 hg38 LSC_ouyang_1 LSC
        GSM4728060 hg38 LSC_ouyang_2 LSC
        GSM915329 hg38 H1_ESC_1 ESC
        GSM958733 hg38 H1_ESC_2 ESC

        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: ../genomes  # where to look for or download the genomes
fastq_dir: ../fastq_dir  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: Jsmits@science.ru.nl
ignore_strandedness: True

# produce a UCSC trackhub?
create_trackhub: False

# 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

##### aligner and filter options are not used for the gene counts matrix if the quantifier is Salmon

# which aligner to use
aligner: star

# filtering after alignment
remove_blacklist: True
min_mapping_quality: 255  # (only keep uniquely mapped reads from STAR alignments)
only_primary_align: True

##### differential gene expression analysis (optional) #####

#deseq2:
#  multiple_testing_procedure: BH
#  alpha_value: 0.1
#  shrinkage_estimator: apeglm

#contrasts:
#  - 'celltype_KC_LSC'

        MultiQC v1.9 - Written by Phil Ewels, available on GitHub.

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