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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_Smed_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 (c62b2c1)

        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 v0.7.1, 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
        February 22, 2022
        Project
        2022-01-jordi
        Contact E-mail
        siebrenf@science.ru.nl

        Report generated on 2022-02-22, 15:05 based on data in:

        Change sample names:

        General Statistics

        Showing 28/28 rows and 12/26 columns.
        Sample Name% DuplicationGC content% PF% Adapter% Dups% MappedM Total seqs% Proper PairsM Total seqsGenome coverageM Genome readsM MT genome reads
        GSM2187933
        51.9%
        41.4%
        97.4%
        2.4%
        86.9%
        100.0%
        14.3
        0.0%
        7.0
        2.1 X
        31.5
        0.8
        GSM2187934
        47.3%
        41.4%
        93.5%
        6.3%
        85.1%
        100.0%
        16.2
        0.0%
        8.1
        2.3 X
        35.7
        0.9
        GSM2187935
        53.4%
        39.3%
        98.9%
        0.9%
        85.6%
        100.0%
        15.9
        0.0%
        9.3
        2.0 X
        30.1
        1.2
        GSM2187936
        54.3%
        39.7%
        98.8%
        1.2%
        89.0%
        100.0%
        16.3
        0.0%
        8.7
        2.1 X
        32.9
        0.9
        GSM2187937
        53.4%
        42.0%
        99.0%
        1.8%
        90.4%
        100.0%
        16.0
        0.0%
        7.1
        2.4 X
        37.3
        0.9
        GSM2187938
        51.3%
        42.0%
        96.4%
        3.5%
        88.1%
        100.0%
        16.4
        0.0%
        7.5
        2.5 X
        38.4
        1.0
        GSM2187939
        56.4%
        39.1%
        93.2%
        6.8%
        89.1%
        100.0%
        15.9
        0.0%
        8.7
        2.1 X
        32.0
        0.5
        GSM2187940
        49.1%
        39.7%
        97.2%
        2.6%
        86.5%
        100.0%
        21.2
        0.0%
        12.0
        2.7 X
        41.7
        1.6
        GSM2187941
        55.0%
        40.9%
        97.1%
        2.8%
        91.0%
        100.0%
        20.4
        0.0%
        9.4
        3.1 X
        46.9
        0.9
        GSM2187942
        51.5%
        40.5%
        99.3%
        1.2%
        89.1%
        100.0%
        16.2
        0.0%
        7.9
        2.3 X
        35.6
        0.8
        GSM2187943
        51.1%
        42.1%
        98.9%
        1.9%
        91.2%
        100.0%
        21.6
        0.0%
        9.1
        3.3 X
        51.4
        0.9
        GSM2187944
        46.3%
        41.1%
        98.2%
        1.6%
        87.0%
        100.0%
        23.4
        0.0%
        11.6
        3.4 X
        51.2
        1.3
        GSM2187945
        48.7%
        39.1%
        99.8%
        85.2%
        100.0%
        14.4
        0.0%
        7.8
        1.9 X
        28.9
        0.8
        GSM2187946
        44.9%
        38.9%
        98.7%
        1.2%
        78.6%
        100.0%
        16.8
        0.0%
        10.3
        2.0 X
        30.4
        1.2
        GSM2187947
        43.3%
        38.6%
        99.4%
        1.1%
        76.9%
        100.0%
        17.4
        0.0%
        11.2
        2.0 X
        30.2
        1.3
        GSM2187948
        43.6%
        39.0%
        99.1%
        0.8%
        81.1%
        100.0%
        18.9
        0.0%
        11.5
        2.3 X
        34.6
        1.1
        GSM2187949
        40.4%
        38.0%
        99.4%
        0.8%
        68.0%
        100.0%
        12.3
        0.0%
        8.7
        1.3 X
        19.3
        1.1
        GSM2187950
        42.1%
        38.0%
        99.4%
        0.9%
        66.3%
        100.0%
        17.0
        0.0%
        12.8
        1.6 X
        24.8
        1.4
        GSM2187951
        44.0%
        38.2%
        99.2%
        0.8%
        73.8%
        100.0%
        24.4
        0.0%
        16.9
        2.6 X
        39.8
        1.7
        GSM2187952
        40.1%
        38.0%
        99.4%
        0.8%
        70.2%
        100.0%
        24.4
        0.0%
        18.0
        2.4 X
        36.5
        1.9
        GSM2187953
        44.3%
        37.5%
        99.4%
        0.8%
        70.5%
        100.0%
        19.2
        0.0%
        14.1
        1.9 X
        28.7
        1.3
        GSM2187954
        40.7%
        37.9%
        98.9%
        1.0%
        62.2%
        100.0%
        14.3
        0.0%
        11.2
        1.3 X
        19.9
        1.2
        GSM2187955
        40.7%
        39.0%
        99.4%
        0.7%
        70.7%
        100.0%
        17.3
        0.0%
        11.5
        2.0 X
        29.9
        1.2
        GSM2187956
        39.4%
        37.4%
        99.2%
        0.8%
        64.4%
        100.0%
        21.5
        0.0%
        17.3
        1.9 X
        28.9
        1.6
        GSM2187957
        42.3%
        37.4%
        97.9%
        2.1%
        60.5%
        100.0%
        15.3
        0.0%
        12.4
        1.3 X
        20.2
        1.3
        GSM2187958
        42.4%
        37.3%
        99.4%
        0.9%
        60.0%
        100.0%
        15.8
        0.0%
        13.1
        1.3 X
        20.2
        1.3
        GSM2187959
        41.4%
        37.3%
        99.4%
        0.9%
        60.0%
        100.0%
        17.0
        0.0%
        14.3
        1.4 X
        21.3
        1.5
        GSM2187960
        38.4%
        37.2%
        99.4%
        0.9%
        60.7%
        100.0%
        18.3
        0.0%
        15.3
        1.5 X
        22.8
        1.6

        Workflow explanation

        Preprocessing of reads was done automatically with workflow tool seq2science v0.7.1. Public samples were downloaded from the Sequence Read Archive with help of the ncbi e-utilities and pysradb. Genome assembly Smed was downloaded with genomepy 0.11.1. Single-end reads were trimmed with fastp v0.20.1 with default options. The UCSC genome browser was used to visualize and inspect alignment. Reads were aligned with STAR v2.7.6a with default options. Afterwards, duplicate reads were marked with Picard MarkDuplicates v2.23.8. General alignment statistics were collected by samtools stats v1.14. 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. 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.

        Assembly stats

        Genome assembly Smed contains of 5 contigs, with a GC-content of 29.60%, and 0.01% consists of the letter N. The N50-L50 stats are 265042666-2 and the N75-L75 stats are 265042666-2. The genome annotation contains 29082 genes.

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...)

        Filtered Reads

        Filtering statistics of sampled reads.

        loading..

        Duplication Rates

        Duplication rates 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.

        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
        loading..

        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).

        loading..

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        loading..

        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).

        loading..

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        loading..

        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

        loading..

        Fingerprint plot

        Signal fingerprint according to plotFingerprint

        loading..

        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).

        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.

        DESeq2 - MA plot for contrast stage_14_2

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for stage_14_2

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        Samples & Config

        The samples file used for this run:

        sample assembly stage descriptive_name
        GSM2187933 Smed 2 p2_1
        GSM2187934 Smed 2 p2_2
        GSM2187935 Smed 2 p2_3
        GSM2187936 Smed 2 p2_4
        GSM2187937 Smed 3 e3_1
        GSM2187938 Smed 3 e3_2
        GSM2187939 Smed 3 e3_3
        GSM2187940 Smed 3 e3_4
        GSM2187941 Smed 4 e4_1
        GSM2187942 Smed 4 e4_2
        GSM2187943 Smed 4 e4_3
        GSM2187944 Smed 4 e4_4
        GSM2187945 Smed 6 e6_1
        GSM2187946 Smed 6 e6_2
        GSM2187947 Smed 6 e6_3
        GSM2187948 Smed 6 e6_4
        GSM2187949 Smed 9 e9_1
        GSM2187950 Smed 9 e9_2
        GSM2187951 Smed 9 e9_3
        GSM2187952 Smed 9 e9_4
        GSM2187953 Smed 10 e10_1
        GSM2187954 Smed 10 e10_2
        GSM2187955 Smed 10 e10_3
        GSM2187956 Smed 10 e10_4
        GSM2187957 Smed 14 e14_1
        GSM2187958 Smed 14 e14_2
        GSM2187959 Smed 14 e14_3
        GSM2187960 Smed 14 e14_4

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

# pipeline file locations
result_dir: ./s2s_rna  # where to store results
genome_dir: /bank/genomes  # where to look for or download the genomes
# fastq_dir: ./s2s_rna/fastq  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: siebrenf@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
contrasts:
  - 'stage_14_2'

        MultiQC v1.11 (c62b2c1) - Written by Phil Ewels, available on GitHub.

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