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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.p14_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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        Tool Citations

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

        To help with this, you can download publication details of the tools mentioned in this report:

        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 v1.2.0, 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
        November 30, 2023
        Project
        20231129_CH0008
        Contact E-mail
        none@provided.com

        Report generated on 2023-11-30, 11:58 CET based on data in:

        Change sample names:

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        General Statistics

        Showing 19/19 rows and 14/29 columns.
        Sample Name% DuplicationM Reads After FilteringGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqsGenome coverageM Genome readsM MT genome reads
        44084_CH_TP5375H_0_1
        29.1%
        9.8
        52.8%
        98.5%
        0.1%
        44085_CH_TP5375H_0_2
        25.8%
        8.8
        52.0%
        98.2%
        0.1%
        44086_CH_TP5375H_0_3
        28.4%
        7.9
        49.6%
        99.3%
        0.1%
        44087_CH_TP5375H_120_7
        30.0%
        7.3
        52.9%
        98.3%
        0.0%
        44088_CH_TP5375H_120_8
        29.4%
        8.3
        52.2%
        98.5%
        0.1%
        44089_CH_TP5375H_120_9
        30.7%
        9.3
        53.9%
        99.2%
        0.0%
        44090_CH_TP5375H_1000_13
        29.4%
        10.5
        52.7%
        98.3%
        0.1%
        44091_CH_TP5375H_1000_14
        28.5%
        6.4
        52.2%
        98.0%
        0.1%
        44092_CH_TP5375H_1000_15
        33.0%
        8.7
        52.2%
        99.1%
        0.0%
        44093_CH_MYC_0_19
        27.3%
        8.0
        50.8%
        97.7%
        0.1%
        44094_CH_MYC_0_20
        28.4%
        7.5
        50.5%
        98.7%
        0.1%
        44095_CH_MYC_0_21
        34.9%
        10.5
        55.8%
        98.9%
        0.3%
        44096_CH_MYC_120_25
        27.7%
        8.2
        49.7%
        98.3%
        0.1%
        44097_CH_MYC_120_26
        28.9%
        7.3
        50.9%
        98.0%
        0.2%
        MYC_0
        32.7%
        26.0
        52.7%
        100.0%
        0.0%
        354 bp
        46.9%
        100.0%
        25.1
        100.0%
        19.1
        1.0 X
        54.5
        0.4
        MYC_120
        29.5%
        15.5
        50.2%
        100.0%
        0.0%
        315 bp
        42.7%
        100.0%
        14.8
        100.0%
        12.7
        0.4 X
        23.2
        0.3
        TP5375H_0
        29.6%
        26.5
        51.6%
        100.0%
        0.0%
        362 bp
        44.1%
        100.0%
        25.7
        100.0%
        21.7
        0.8 X
        42.2
        0.4
        TP5375H_1000
        32.3%
        25.6
        52.4%
        100.0%
        0.0%
        400 bp
        47.6%
        100.0%
        24.4
        100.0%
        20.3
        0.8 X
        41.1
        0.3
        TP5375H_120
        32.2%
        24.9
        53.0%
        100.0%
        0.0%
        337 bp
        46.6%
        100.0%
        24.1
        100.0%
        19.9
        0.8 X
        41.6
        0.4

        Workflow explanation

        Preprocessing of reads was done automatically by seq2science v1.2.0 using the rna-seq workflow. Genome assembly GRCh38.p14 was downloaded with genomepy 0.16.1. Paired-end reads were trimmed with fastp v0.23.2 with default options. Reads were aligned with STAR v2.7.10b with default options. Afterwards, duplicate reads were marked with Picard MarkDuplicates v3.0.0. General alignment statistics were collected by samtools stats v1.16. Sample sequencing strandedness was inferred using RSeQC v5.0.1 in order to improve quantification accuracy. 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'. Read counting and summarizing to gene-level was performed on filtered bam using HTSeq-count v2.0.2. RNA-seq read duplication types were analyzed using dupRadar v1.28.0. The UCSC genome browser was used to visualize and inspect alignment. TPM normalized gene counts were generated using genomepy based on longest transcript lengths. Quality control metrics were aggregated by MultiQC v1.14.

        Assembly stats

        Genome assembly GRCh38.p14 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 40874 genes.

        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.

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        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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        Strandedness

        Strandedness package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.DOI: 10.1093/bioinformatics/bts356.

        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).

        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.

        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
        44084_CH_TP53_R175H_0_1 GRCh38.p14 TP53_R175H_0
        44085_CH_TP53_R175H_0_2 GRCh38.p14 TP53_R175H_0
        44086_CH_TP53_R175H_0_3 GRCh38.p14 TP53_R175H_0
        44087_CH_TP53_R175H_120_7 GRCh38.p14 TP53_R175H_120
        44088_CH_TP53_R175H_120_8 GRCh38.p14 TP53_R175H_120
        44089_CH_TP53_R175H_120_9 GRCh38.p14 TP53_R175H_120
        44090_CH_TP53_R175H_1000_13 GRCh38.p14 TP53_R175H_1000
        44091_CH_TP53_R175H_1000_14 GRCh38.p14 TP53_R175H_1000
        44092_CH_TP53_R175H_1000_15 GRCh38.p14 TP53_R175H_1000
        44093_CH_MYC_0_19 GRCh38.p14 MYC_0
        44094_CH_MYC_0_20 GRCh38.p14 MYC_0
        44095_CH_MYC_0_21 GRCh38.p14 MYC_0
        44096_CH_MYC_120_25 GRCh38.p14 MYC_120
        44097_CH_MYC_120_26 GRCh38.p14 MYC_120

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

# pipeline file locations
result_dir:  /ceph/rimlsfnwi/data/moldevbio/yu/Cyriel/seq2science/runs/20231129_CH0008/results  # where to store results
genome_dir:  /ceph/rimlsfnwi/data/moldevbio/yu/Cyriel/seq2science/genomes  # where to look for or download the genomes
fastq_dir:  /ceph/rimlsfnwi/data/moldevbio/yu/Cyriel/seq2science/runs/20231129_CH0008/2023-11-20_AAF23JGM5 # where to look for or download the fastqs


# contact info for multiqc report and trackhub
# email: cyriel.huijer@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

##### 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
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

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

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