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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_HmiaM1_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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        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 (08138c8)

        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 v1.0.0, 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
        June 21, 2023
        Project
        rna
        Contact E-mail
        yourmail@here.com

        Report generated on 2023-06-22, 08:51 CEST based on data in:

        Change sample names:

        General Statistics

        Showing 41/41 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
        SRX5260851
        35.8%
        67.0
        38.3%
        98.1%
        11.2%
        179 bp
        63.9%
        100.0%
        58.5
        100.0%
        54.4
        6.6 X
        65.2
        0.0
        SRX5260856
        59.5%
        29.8
        39.7%
        99.9%
        0.2%
        63.9%
        100.0%
        29.1
        0.0%
        26.4
        1.4 X
        33.7
        0.0
        SRX5260857
        59.7%
        25.4
        39.9%
        99.9%
        0.3%
        64.1%
        100.0%
        24.9
        0.0%
        22.1
        1.3 X
        29.7
        0.0
        SRX5260858
        59.5%
        27.0
        39.5%
        99.9%
        0.4%
        64.9%
        100.0%
        26.4
        0.0%
        24.0
        1.3 X
        30.3
        0.0
        SRX5260859
        56.3%
        23.7
        39.2%
        99.9%
        0.2%
        61.8%
        100.0%
        23.2
        0.0%
        20.5
        1.2 X
        28.0
        0.0
        SRX5260860
        58.5%
        26.3
        39.7%
        100.0%
        0.6%
        63.9%
        100.0%
        25.7
        0.0%
        23.4
        1.2 X
        29.5
        0.0
        SRX5260861
        58.9%
        28.6
        39.5%
        99.9%
        0.3%
        65.2%
        100.0%
        28.1
        0.0%
        25.5
        1.4 X
        32.2
        0.0
        SRX5260862
        56.7%
        27.1
        39.4%
        99.9%
        0.3%
        62.3%
        100.0%
        26.6
        0.0%
        24.1
        1.3 X
        30.6
        0.0
        SRX5260863
        57.3%
        27.3
        39.5%
        100.0%
        0.6%
        63.0%
        100.0%
        26.8
        0.0%
        24.3
        1.3 X
        30.8
        0.0
        SRX5260864
        56.6%
        24.7
        39.5%
        99.9%
        0.4%
        61.8%
        100.0%
        24.2
        0.0%
        21.8
        1.2 X
        28.2
        0.0
        SRX5260865
        55.9%
        26.2
        39.1%
        100.0%
        62.2%
        100.0%
        25.6
        0.0%
        23.0
        1.3 X
        30.1
        0.0
        SRX5260866
        45.2%
        16.8
        40.0%
        99.6%
        56.2%
        100.0%
        16.4
        0.0%
        15.1
        1.5 X
        18.5
        0.0
        SRX5260867
        60.8%
        45.7
        40.0%
        99.5%
        0.2%
        72.5%
        100.0%
        44.7
        0.0%
        40.9
        4.0 X
        50.7
        0.0
        SRX5260870
        44.9%
        65.9
        38.9%
        98.2%
        8.3%
        180 bp
        74.4%
        100.0%
        60.6
        100.0%
        55.9
        7.1 X
        69.0
        0.0
        SRX5260871
        35.5%
        54.7
        38.5%
        98.3%
        6.1%
        188 bp
        59.6%
        100.0%
        50.0
        100.0%
        46.7
        5.7 X
        55.3
        0.0
        SRX5260872
        48.8%
        19.2
        39.9%
        99.4%
        60.1%
        100.0%
        18.7
        0.0%
        17.2
        1.7 X
        21.2
        0.0
        SRX5260874
        27.7%
        62.7
        38.0%
        98.1%
        13.2%
        145 bp
        48.8%
        100.0%
        55.0
        100.0%
        51.1
        6.3 X
        61.7
        0.0
        SRX5260879
        55.0%
        22.5
        39.8%
        99.9%
        0.6%
        60.3%
        100.0%
        22.0
        0.0%
        20.0
        1.1 X
        25.5
        0.0
        SRX5260880
        59.4%
        30.8
        39.5%
        99.9%
        0.6%
        63.8%
        100.0%
        30.1
        0.0%
        27.1
        1.5 X
        35.1
        0.0
        SRX5260881
        56.8%
        27.1
        39.7%
        99.9%
        0.2%
        62.7%
        100.0%
        26.5
        0.0%
        24.2
        1.3 X
        30.4
        0.0
        SRX5260882
        55.2%
        22.0
        39.6%
        99.9%
        0.2%
        59.3%
        100.0%
        21.5
        0.0%
        19.4
        1.1 X
        25.2
        0.0
        SRX5260883
        53.6%
        22.5
        39.3%
        99.9%
        0.2%
        58.0%
        100.0%
        22.0
        0.0%
        19.9
        1.1 X
        25.6
        0.0
        SRX5260884
        56.7%
        25.4
        39.4%
        99.9%
        0.6%
        61.1%
        100.0%
        24.9
        0.0%
        22.5
        1.2 X
        28.8
        0.0
        SRX5260885
        56.8%
        27.7
        39.8%
        100.0%
        62.3%
        100.0%
        27.1
        0.0%
        24.6
        1.3 X
        31.1
        0.0
        SRX5260886
        57.1%
        27.2
        39.4%
        99.9%
        0.3%
        61.7%
        100.0%
        26.6
        0.0%
        23.9
        1.3 X
        31.1
        0.0
        SRX5260887
        62.5%
        28.6
        39.4%
        99.0%
        1.2%
        66.0%
        100.0%
        27.9
        0.0%
        25.4
        1.4 X
        32.2
        0.0
        SRX5260888
        57.3%
        26.4
        39.4%
        99.9%
        0.2%
        61.6%
        100.0%
        25.9
        0.0%
        23.4
        1.3 X
        30.0
        0.0
        SRX5260889
        57.4%
        26.8
        39.5%
        99.9%
        0.6%
        61.8%
        100.0%
        26.2
        0.0%
        23.8
        1.3 X
        30.2
        0.0
        SRX5260890
        57.7%
        30.0
        39.4%
        99.9%
        0.3%
        63.9%
        100.0%
        29.4
        0.0%
        26.7
        1.4 X
        33.8
        0.0
        SRX5260891
        57.2%
        29.0
        39.5%
        100.0%
        0.3%
        62.6%
        100.0%
        28.3
        0.0%
        25.8
        1.4 X
        32.7
        0.0
        SRX5260892
        57.0%
        27.7
        39.3%
        99.9%
        0.3%
        62.5%
        100.0%
        27.1
        0.0%
        24.5
        1.3 X
        31.4
        0.0
        SRX5260893
        59.5%
        28.5
        39.6%
        99.9%
        0.3%
        65.1%
        100.0%
        27.9
        0.0%
        25.2
        1.4 X
        32.4
        0.0
        SRX5260894
        56.5%
        29.3
        39.6%
        100.0%
        63.0%
        100.0%
        28.6
        0.0%
        26.1
        1.4 X
        32.8
        0.0
        SRX5260895
        51.4%
        24.1
        39.9%
        99.5%
        62.8%
        100.0%
        23.6
        0.0%
        21.6
        2.1 X
        26.6
        0.0
        SRX5260896
        58.5%
        26.4
        39.5%
        100.0%
        64.0%
        100.0%
        25.9
        0.0%
        23.6
        1.2 X
        29.5
        0.0
        SRX5260897
        56.8%
        25.3
        39.6%
        100.0%
        62.2%
        100.0%
        24.8
        0.0%
        22.5
        1.2 X
        28.6
        0.0
        SRX5260898
        57.6%
        35.5
        40.0%
        99.5%
        0.2%
        69.5%
        100.0%
        34.8
        0.0%
        31.9
        3.1 X
        39.1
        0.0
        SRX5260899
        51.5%
        26.0
        39.9%
        99.6%
        63.6%
        100.0%
        25.4
        0.0%
        23.3
        2.3 X
        28.8
        0.0
        SRX5260903
        28.2%
        59.2
        38.3%
        98.3%
        8.2%
        167 bp
        50.6%
        100.0%
        51.7
        100.0%
        48.1
        6.0 X
        57.9
        0.0
        SRX5260906
        52.8%
        21.9
        39.3%
        99.9%
        0.6%
        57.3%
        100.0%
        21.4
        0.0%
        19.1
        1.1 X
        25.4
        0.0
        SRX5260907
        55.2%
        23.5
        39.5%
        99.9%
        0.3%
        59.6%
        100.0%
        22.9
        0.0%
        20.5
        1.1 X
        27.2
        0.0

        Workflow explanation

        Preprocessing of reads was done automatically by seq2science v1.0.0 using the rna-seq workflow. Public samples were downloaded from the Sequence Read Archive with help of the ncbi e-utilities and pysradb. Genome assembly danRer10 was downloaded with genomepy 0.15.0. Paired-end reads were trimmed with fastp v0.23.2 with default options. The UCSC genome browser was used to visualize and inspect alignment. Single-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. Differential gene expression analysis was performed using DESeq2 v1.34. 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 v1.16. 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 HmiaM1 contains of 18347 contigs, with a GC-content of 31.81%, and 11.85% consists of the letter N. The N50-L50 stats are 1044515-275 and the N75-L75 stats are 501601-598. The genome annotation contains 50 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.

        loading..

        Sequence Quality

        Average sequencing quality over each base of all reads.

        loading..

        GC Content

        Average GC content over each base of all reads.

        loading..

        N content

        Average N content over each base of all reads.

        loading..

        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.

        loading..

        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.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).

        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.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.

        loading..

        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

        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.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.

        DESeq2 - MA plot for contrast deseq2_rnai_control

        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 deseq2_rnai_control

        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.

        DESeq2 - MA plot for contrast deseq2_6h_3h

        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 deseq2_6h_3h

        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.

        DESeq2 - MA plot for contrast deseq2_6h_0h

        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 deseq2_6h_0h

        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 _brep descriptive_name original_name deseq2
        SRX5260879 HmiaM1 head-0hpa head-0hpa_1 RNA-seq_of_Hofstenia_miamia_head_fragments_at_0_hours_post_amputation_replicate_1
        SRX5260906 HmiaM1 head-0hpa head-0hpa_2 RNA-seq_of_Hofstenia_miamia_head_fragments_at_0_hours_post_amputation_replicate_2
        SRX5260907 HmiaM1 head-0hpa head-0hpa_3 RNA-seq_of_Hofstenia_miamia_head_fragments_at_0_hours_post_amputation_replicate_3
        SRX5260859 HmiaM1 tail-0hpa tail-0hpa_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_0_hours_post_amputation_replicate_1 0h
        SRX5260858 HmiaM1 tail-0hpa tail-0hpa_2 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_0_hours_post_amputation_replicate_2 0h
        SRX5260861 HmiaM1 tail-0hpa tail-0hpa_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_0_hours_post_amputation_replicate_3 0h
        SRX5260882 HmiaM1 head-1hpa head-1hpa_1 RNA-seq_of_Hofstenia_miamia_head_fragments_at_1_hour_post_amputation_replicate_1
        SRX5260883 HmiaM1 head-1hpa head-1hpa_2 RNA-seq_of_Hofstenia_miamia_head_fragments_at_1_hour_post_amputation_replicate_2
        SRX5260884 HmiaM1 head-1hpa head-1hpa_3 RNA-seq_of_Hofstenia_miamia_head_fragments_at_1_hour_post_amputation_replicate_3
        SRX5260860 HmiaM1 tail-1hpa tail-1hpa_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_1_hour_post_amputation_replicate_1 1h
        SRX5260863 HmiaM1 tail-1hpa tail-1hpa_2 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_1_hour_post_amputation_replicate_2 1h
        SRX5260862 HmiaM1 tail-1hpa tail-1hpa_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_1_hour_post_amputation_replicate_3 1h
        SRX5260885 HmiaM1 head-3hpa head-3hpa_1 RNA-seq_of_Hofstenia_miamia_head_fragments_at_3_hours_post_amputation_replicate_1
        SRX5260886 HmiaM1 head-3hpa head-3hpa_2 RNA-seq_of_Hofstenia_miamia_head_fragments_at_3_hours_post_amputation_replicate_2
        SRX5260887 HmiaM1 head-3hpa head-3hpa_3 RNA-seq_of_Hofstenia_miamia_head_fragments_at_3_hours_post_amputation_replicate_3
        SRX5260865 HmiaM1 tail-3hpa tail-3hpa_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_3_hours_post_amputation_replicate_1 3h
        SRX5260864 HmiaM1 tail-3hpa tail-3hpa_2 RNA-seq_of_Hofstenia_miamia_tail_fragmens_at_3_hours_post_amputation_replicate_2 3h
        SRX5260892 HmiaM1 tail-3hpa tail-3hpa_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_3_hours_post_amputation_replicate_3 3h
        SRX5260888 HmiaM1 head-6hpa head-6hpa_1 RNA-seq_of_Hofstenia_miamia_head_fragmenst_at_6_hours_post_amputation_replicate_1
        SRX5260889 HmiaM1 head-6hpa head-6hpa_2 RNA-seq_of_Hofstenia_miamia_head_fragments_at_6_hours_post_amputation_replicate_2
        SRX5260880 HmiaM1 head-6hpa head-6hpa_3 RNA-seq_of_Hofstenia_miamia_head_fragments_at_6_hours_post_amputation_replicate_3
        SRX5260893 HmiaM1 tail-6hpa tail-6hpa_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_replicate_1 6h
        SRX5260890 HmiaM1 tail-6hpa tail-6hpa_2 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_replicate_2 6h
        SRX5260891 HmiaM1 tail-6hpa tail-6hpa_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_replicate_3 6h
        SRX5260881 HmiaM1 head-12hpa head-12hpa_1 RNA-seq_of_Hofstenia_miamia_head_fragments_at_12_hours_post_amputation_replicate_1
        SRX5260857 HmiaM1 head-12hpa head-12hpa_2 RNA-seq_of_Hofstenia_miamia_head_fragments_at_12_hours_post_amputation_replicate_2
        SRX5260856 HmiaM1 head-12hpa head-12hpa_3 RNA-seq_of_Hofstenia_miamia_head_fragments_at_12_hours_post_amputation_replicate_3
        SRX5260896 HmiaM1 tail-12hpa tail-12hpa_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_12_hours_post_amputation_replicate_1 12h
        SRX5260897 HmiaM1 tail-12hpa tail-12hpa_2 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_12_hours_post_amputation_replicate_2 12h
        SRX5260894 HmiaM1 tail-12hpa tail-12hpa_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_12_hours_post_amputation_replicate_3 12h
        SRX5260874 HmiaM1 head-6hpa-transcriptome head-6hpa-transcriptome RNA-seq_of_Hofstenia_miamia_head_fragments_at_6_hourss_past_amputation,_paired-end_for_transcriptome
        SRX5260903 HmiaM1 tail-6hpa-transcriptome tail-6hpa-transcriptome RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hourss_past_amputation,_paired-end_for_transcriptome
        SRX5260851 HmiaM1 head-48hpa-transcriptome head-48hpa-transcriptome RNA-seq_of_Hofstenia_miamia_head_fragments_at_48_hourss_past_amputation,_paired-end_for_transcriptome
        SRX5260871 HmiaM1 tail-48hpa-transcriptome tail-48hpa-transcriptome RNA-seq_of_Hofstenia_miamia_tail_fragments_at_48_hourss_past_amputation,_paired-end_for_transcriptome
        SRX5260870 HmiaM1 neoblast-transcriptome neoblast-transcriptome RNA-seq_of_Hofstenia_miamia_FACS_sorted_neoblasts,_paired-end_for_transcriptome
        SRX5260895 HmiaM1 tail-6hpa-control-rnai tail-6hpa-control-rnai_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_control_RNAi_injection_replicate_1 control
        SRX5260898 HmiaM1 tail-6hpa-control-rnai tail-6hpa-control-rnai_2 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_control_RNAi_injection_replicate_2 control
        SRX5260899 HmiaM1 tail-6hpa-control-rnai tail-6hpa-control-rnai_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_control_RNAi_injection_replicate_3 control
        SRX5260867 HmiaM1 tail-6hpa-egr-rnai tail-6hpa-egr-rnai_1 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_egr_RNAi_injection_replicate_1 rnai
        SRX5260866 HmiaM1 tail-6hpa-egr-rnai tail-6hpa-egr-rnai_2 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_egr_RNAi_injection_replicate_2 rnai
        SRX5260872 HmiaM1 tail-6hpa-egr-rnai tail-6hpa-egr-rnai_3 RNA-seq_of_Hofstenia_miamia_tail_fragments_at_6_hours_post_amputation_egr_RNAi_injection_replicate_3 rnai

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


# contact info for multiqc report and trackhub
email: yourmail@here.com

# 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)

# should the final output be stored as cram files (instead of bam) to save storage?
store_as_cram: false

# differential gene expression analysis
# for explanation, see: https://vanheeringen-lab.github.io/seq2science/content/DESeq2.html
contrasts:
  - deseq2_rnai_control
  - deseq2_6h_0h
  - deseq2_6h_3h
  - deseq2_6h_6h
#  - biological_replicates_tail-6hpa-egr-rnai_tail-6hpa-control-rnai

        MultiQC v1.14 (08138c8) - Written by Phil Ewels, available on GitHub.

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