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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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        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 v1.2.2, 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
        December 10, 2024
        Project
        monocyte_salt_rnaseq
        Contact E-mail
        niels@mhlangalab.org

        Report generated on 2024-12-10, 17:49 CET based on data in:

        Change sample names:

        General Statistics

        Showing 15/15 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
        46356_DA_0h_1_A47
        51.8%
        43.7
        47.2%
        98.9%
        0.1%
        240 bp
        67.4%
        100.0%
        42.4
        100.0%
        40.4
        0.9 X
        48.1
        0.2
        46357_DB_0h_1_A48
        50.4%
        18.2
        47.9%
        99.3%
        0.2%
        213 bp
        62.0%
        100.0%
        17.7
        100.0%
        16.8
        0.4 X
        20.4
        0.1
        46358_DC_0h_1_A77
        56.8%
        19.4
        47.9%
        98.6%
        0.1%
        227 bp
        69.5%
        100.0%
        18.5
        100.0%
        17.6
        0.4 X
        21.1
        0.1
        46359_DA_med_4h_1_A70
        32.2%
        19.9
        48.0%
        98.4%
        0.2%
        213 bp
        39.4%
        100.0%
        19.1
        100.0%
        18.1
        0.4 X
        21.7
        0.1
        46360_DA_NaCl_4h_1_A60
        51.9%
        38.6
        46.5%
        99.1%
        0.1%
        239 bp
        67.3%
        100.0%
        37.7
        100.0%
        35.8
        0.8 X
        43.3
        0.2
        46361_DB_med_4h_1_A61
        36.8%
        31.6
        47.2%
        99.2%
        0.1%
        246 bp
        47.8%
        100.0%
        30.9
        100.0%
        29.4
        0.7 X
        35.4
        0.1
        46362_DB_NaCl_4h_1_A59
        40.0%
        42.4
        47.2%
        99.2%
        0.1%
        222 bp
        51.8%
        100.0%
        41.6
        100.0%
        39.5
        0.9 X
        47.4
        0.2
        46363_DC_med_4h_2_A04
        41.1%
        35.3
        47.8%
        99.2%
        0.1%
        217 bp
        53.1%
        100.0%
        34.5
        100.0%
        32.8
        0.7 X
        39.0
        0.2
        46364_DC_NaCl_4h_2_A05
        29.2%
        2.9
        44.6%
        99.2%
        0.1%
        227 bp
        36.8%
        100.0%
        2.9
        100.0%
        2.7
        0.1 X
        3.2
        0.0
        46365_DA_med_24h_1_A06
        41.8%
        45.1
        45.5%
        99.1%
        0.1%
        215 bp
        55.0%
        100.0%
        44.1
        100.0%
        42.0
        0.9 X
        49.4
        0.3
        46366_DA_NaCl_24h_2_A57
        22.9%
        16.4
        45.8%
        99.4%
        0.1%
        253 bp
        27.8%
        100.0%
        16.1
        100.0%
        15.4
        0.3 X
        18.0
        0.1
        46367_DB_med_24h_1_A58
        29.6%
        16.9
        46.7%
        99.2%
        0.1%
        233 bp
        36.5%
        100.0%
        16.6
        100.0%
        15.7
        0.4 X
        18.8
        0.1
        46368_DB_NaCl_24h_1_A07
        36.9%
        37.7
        46.4%
        99.3%
        0.1%
        256 bp
        48.3%
        100.0%
        36.7
        100.0%
        34.9
        0.8 X
        41.8
        0.2
        46369_DC_med_24h_2_A78
        40.9%
        36.2
        46.4%
        99.0%
        0.1%
        215 bp
        53.7%
        100.0%
        35.1
        100.0%
        33.3
        0.8 X
        39.7
        0.3
        46370_DC_NaCl_24h_2_A01
        48.9%
        28.9
        50.2%
        96.6%
        0.2%
        203 bp
        65.4%
        100.0%
        26.2
        100.0%
        23.4
        0.7 X
        36.7
        0.0

        Workflow explanation

        Preprocessing of reads was done automatically by seq2science v1.2.2 using the rna-seq workflow. Paired-end reads were trimmed with fastp v0.23.2 with default options. Genome assembly GRCh38 was downloaded with genomepy 0.16.1. 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'. RNA-seq read duplication types were analyzed using dupRadar v1.28.0. Read counting and summarizing to gene-level was performed on filtered bam using HTSeq-count v2.0.2. The UCSC genome browser was used to visualize and inspect alignment. 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 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 77307 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 treatmenttimepoint_salt4h_control0h

        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 treatmenttimepoint_salt4h_control0h

        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 treatmenttimepoint_control24h_control0h

        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 treatmenttimepoint_control24h_control0h

        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 treatmenttimepoint_salt24h_control0h

        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 treatmenttimepoint_salt24h_control0h

        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 treatmenttimepoint_salt24h_control24h

        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 treatmenttimepoint_salt24h_control24h

        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 treatment_salt_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 treatment_salt_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 treatmenttimepoint_salt4h_control4h

        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 treatmenttimepoint_salt4h_control4h

        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 donor treatment timepoint treatmenttimepoint flowcell full_path_to_fastq_R1 full_path_to_fastq_R2 assembly condition descriptive_name color
        46356_DA_0h_1_A47 A control 0h control0h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46356_DA_0h_1_A47_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46356_DA_0h_1_A47_R2.fastq.gz GRCh38 baseline control_0h_A lightgrey
        46357_DB_0h_1_A48 B control 0h control0h 2024-10-16_AAFHFNMM5 /vol/rimlsfnwiz/fastq/2024-10-16_AAFHFNMM5/Camille\_Laberthonniere/46357_DB_0h_1_A48_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-16_AAFHFNMM5/Camille\_Laberthonniere/46357_DB_0h_1_A48_R2.fastq.gz GRCh38 baseline control_0h_B lightgrey
        46358_DC_0h_1_A77 C control 0h control0h 2024-10-16_AAFHFNMM5 /vol/rimlsfnwiz/fastq/2024-10-16_AAFHFNMM5/Camille\_Laberthonniere/46358_DC_0h_1_A77_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-16_AAFHFNMM5/Camille\_Laberthonniere/46358_DC_0h_1_A77_R2.fastq.gz GRCh38 baseline control_0h_C lightgrey
        46359_DA_med_4h_1_A70 A control 4h control4h 2024-10-16_AAFHFNMM5 /vol/rimlsfnwiz/fastq/2024-10-16_AAFHFNMM5/Camille\_Laberthonniere/46359_DA_med_4h_1_A70_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-16_AAFHFNMM5/Camille\_Laberthonniere/46359_DA_med_4h_1_A70_R2.fastq.gz GRCh38 medium4 control_4h_A darkgrey
        46360_DA_NaCl_4h_1_A60 A salt 4h salt4h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46360_DA_NaCl_4h_1_A60_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46360_DA_NaCl_4h_1_A60_R2.fastq.gz GRCh38 nacl4 salt_4h_A royalblue
        46361_DB_med_4h_1_A61 B control 4h control4h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46361_DB_med_4h_1_A61_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46361_DB_med_4h_1_A61_R2.fastq.gz GRCh38 medium4 control_4h_B darkgrey
        46362_DB_NaCl_4h_1_A59 B salt 4h salt4h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46362_DB_NaCl_4h_1_A59_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46362_DB_NaCl_4h_1_A59_R2.fastq.gz GRCh38 nacl4 salt_4h_B royalblue
        46363_DC_med_4h_2_A04 C control 4h control4h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46363_DC_med_4h_2_A04_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46363_DC_med_4h_2_A04_R2.fastq.gz GRCh38 medium4 control_4h_C darkgrey
        46364_DC_NaCl_4h_2_A05 C salt 4h salt4h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46364_DC_NaCl_4h_2_A05_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46364_DC_NaCl_4h_2_A05_R2.fastq.gz GRCh38 nacl4 salt_4h_C royalblue
        46365_DA_med_24h_1_A06 A control 24h control24h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46365_DA_med_24h_1_A06_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46365_DA_med_24h_1_A06_R2.fastq.gz GRCh38 medium24 control_24h_A dimgrey
        46366_DA_NaCl_24h_2_A57 A salt 24h salt24h 2024-10-11_AAFHFNLM5 /vol/rimlsfnwiz/fastq/2024-10-11_AAFHFNLM5/Camille\_Laberthonniere/46366_DA_NaCl_24h_2_A57_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-11_AAFHFNLM5/Camille\_Laberthonniere/46366_DA_NaCl_24h_2_A57_R2.fastq.gz GRCh38 nacl24 salt_24h_A darkblue
        46367_DB_med_24h_1_A58 B control 24h control24h 2024-10-11_AAFHFNLM5 /vol/rimlsfnwiz/fastq/2024-10-11_AAFHFNLM5/Camille\_Laberthonniere/46367_DB_med_24h_1_A58_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-11_AAFHFNLM5/Camille\_Laberthonniere/46367_DB_med_24h_1_A58_R2.fastq.gz GRCh38 medium24 control_24h_B dimgrey
        46368_DB_NaCl_24h_1_A07 B salt 24h salt24h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46368_DB_NaCl_24h_1_A07_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46368_DB_NaCl_24h_1_A07_R2.fastq.gz GRCh38 nacl24 salt_24h_B darkblue
        46369_DC_med_24h_2_A78 C control 24h control24h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46369_DC_med_24h_2_A78_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46369_DC_med_24h_2_A78_R2.fastq.gz GRCh38 medium24 control_24h_C dimgrey
        46370_DC_NaCl_24h_2_A01 C salt 24h salt24h 2024-10-14_AAFHFLNM5 /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46370_DC_NaCl_24h_2_A01_R1.fastq.gz /vol/rimlsfnwiz/fastq/2024-10-14_AAFHFLNM5/Camille\_Laberthonniere/46370_DC_NaCl_24h_2_A01_R2.fastq.gz GRCh38 nacl24 salt_24h_C darkblue

        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: /vol/cellbio/mhlanga/nvelthuijs/genomes  # where to look for or download the genomes
fastq_dir: /vol/cellbio/mhlanga/nvelthuijs/monocyte_salt_rnaseq/fastq  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: niels@mhlangalab.org

# 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:
  - treatment_salt_control
  - treatmenttimepoint_salt4h_control4h
  - treatmenttimepoint_salt24h_control24h
  - treatmenttimepoint_control24h_control0h
  - treatmenttimepoint_salt4h_control0h
  - treatmenttimepoint_salt24h_control0h

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

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