Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Export Plots

        px
        px
        X

        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.

        Choose Plots

        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

        Save Settings

        You can save the toolbox settings for this report to the browser.

        Load Settings

        Choose a saved report profile from the dropdown box below:

        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 v1.2.2, 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
        May 23, 2025
        Project
        monocyte_salt-taurine_RNAseq
        Contact E-mail
        niels@mhlangalab.org

        Report generated on 2025-05-23, 17:43 CEST based on data in:

        Change sample names:

        General Statistics

        Showing 16/16 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
        47576_Media_B1_A06
        11.6%
        22.7
        47.1%
        98.5%
        0.3%
        195 bp
        14.6%
        100.0%
        22.1
        100.0%
        20.9
        0.5 X
        25.4
        0.2
        47577_NaCl_B1_A08
        13.0%
        28.7
        46.8%
        98.8%
        0.7%
        171 bp
        16.1%
        100.0%
        28.1
        100.0%
        26.4
        0.6 X
        32.2
        0.2
        47578_Tau_B1_A09
        13.1%
        32.4
        46.8%
        98.9%
        0.2%
        184 bp
        16.3%
        100.0%
        31.6
        100.0%
        29.7
        0.7 X
        36.2
        0.3
        47579_NaCl_Tau_B1_A26
        16.1%
        24.7
        48.9%
        98.9%
        0.3%
        186 bp
        20.4%
        100.0%
        24.1
        100.0%
        22.0
        0.6 X
        29.8
        0.3
        47580_Media_A_A27
        13.4%
        27.9
        47.2%
        98.7%
        0.5%
        188 bp
        16.8%
        100.0%
        27.2
        100.0%
        25.5
        0.6 X
        31.4
        0.3
        47581_NaCl_A_A28
        14.2%
        25.0
        47.2%
        98.7%
        0.5%
        188 bp
        17.7%
        100.0%
        24.4
        100.0%
        22.8
        0.5 X
        28.3
        0.3
        47582_Tau_A_A29
        13.6%
        24.8
        48.4%
        98.6%
        0.5%
        187 bp
        17.1%
        100.0%
        24.2
        100.0%
        22.5
        0.5 X
        28.5
        0.3
        47583_NaCl_Tau_A_A30
        15.8%
        28.6
        48.2%
        98.5%
        0.4%
        182 bp
        19.9%
        100.0%
        27.9
        100.0%
        25.8
        0.6 X
        33.2
        0.4
        47584_Media_B2_A37
        12.4%
        29.0
        47.0%
        98.9%
        0.3%
        192 bp
        15.4%
        100.0%
        28.6
        100.0%
        27.1
        0.6 X
        31.7
        0.4
        47585_NaCl_B2_A38
        12.4%
        31.2
        46.7%
        99.1%
        0.2%
        194 bp
        15.6%
        100.0%
        30.4
        100.0%
        28.7
        0.7 X
        34.6
        0.4
        47586_Tau_B2_A39
        11.9%
        32.9
        47.0%
        98.8%
        0.2%
        190 bp
        14.9%
        100.0%
        32.0
        100.0%
        30.3
        0.7 X
        36.0
        0.4
        47587_NaCl_Tau_B2_A45
        13.7%
        32.4
        47.4%
        98.8%
        0.3%
        182 bp
        17.3%
        100.0%
        31.6
        100.0%
        29.7
        0.7 X
        36.3
        0.4
        47588_Media_C_A60
        11.4%
        29.0
        45.7%
        99.0%
        0.4%
        183 bp
        14.1%
        100.0%
        28.3
        100.0%
        26.9
        0.6 X
        31.4
        0.3
        47589_NaCl_C_A85
        12.0%
        30.6
        45.7%
        98.4%
        0.3%
        195 bp
        15.1%
        100.0%
        29.7
        100.0%
        28.1
        0.6 X
        33.2
        0.3
        47590_Tau_C_A86
        14.0%
        34.4
        46.0%
        98.6%
        0.3%
        178 bp
        17.4%
        100.0%
        33.7
        100.0%
        31.7
        0.7 X
        37.7
        0.4
        47591_NaCl_Tau_C_A87
        13.4%
        31.7
        46.3%
        98.6%
        0.2%
        183 bp
        16.8%
        100.0%
        31.0
        100.0%
        29.2
        0.7 X
        35.3
        0.3

        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'. 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. 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 treatment_Salt_SaltTaurine

        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_SaltTaurine

        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 treatment_SaltTaurine_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_SaltTaurine_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 treatment_Taurine_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_Taurine_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 treatment_SaltTaurine_Taurine

        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_SaltTaurine_Taurine

        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 original_donor donor treatment flowcell barcode full_path_to_fastq_R1 full_path_to_fastq_R2 assembly descriptive_name color
        47576_Media_B1_A06 B1 B Control 2025-05-19_AAGKFFFM5 A06 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47576_Media_B1_A06_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47576_Media_B1_A06_R2.fastq.gz GRCh38 Control_donorB dimgreay
        47577_NaCl_B1_A08 B1 B Salt 2025-05-19_AAGKFFFM5 A08 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47577_NaCl_B1_A08_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47577_NaCl_B1_A08_R2.fastq.gz GRCh38 Salt_donorB firebrick
        47578_Tau_B1_A09 B1 B Taurine 2025-05-19_AAGKFFFM5 A09 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47578_Tau_B1_A09_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47578_Tau_B1_A09_R2.fastq.gz GRCh38 Taurine_donorB royalblue
        47579_NaCl_Tau_B1_A26 B1 B SaltTaurine 2025-05-19_AAGKFFFM5 A26 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47579_NaCl_Tau_B1_A26_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47579_NaCl_Tau_B1_A26_R2.fastq.gz GRCh38 SaltTaurine_donorB plum
        47580_Media_A_A27 A A Control 2025-05-19_AAGKFFFM5 A27 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47580_Media_A_A27_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47580_Media_A_A27_R2.fastq.gz GRCh38 Control_donorA dimgreay
        47581_NaCl_A_A28 A A Salt 2025-05-19_AAGKFFFM5 A28 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47581_NaCl_A_A28_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47581_NaCl_A_A28_R2.fastq.gz GRCh38 Salt_donorA firebrick
        47582_Tau_A_A29 A A Taurine 2025-05-19_AAGKFFFM5 A29 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47582_Tau_A_A29_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47582_Tau_A_A29_R2.fastq.gz GRCh38 Taurine_donorA royalblue
        47583_NaCl_Tau_A_A30 A A SaltTaurine 2025-05-19_AAGKFFFM5 A30 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47583_NaCl_Tau_A_A30_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47583_NaCl_Tau_A_A30_R2.fastq.gz GRCh38 SaltTaurine_donorA plum
        47584_Media_B2_A37 B2 D Control 2025-05-19_AAGKFFFM5 A37 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47584_Media_B2_A37_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47584_Media_B2_A37_R2.fastq.gz GRCh38 Control_donorD dimgreay
        47585_NaCl_B2_A38 B2 D Salt 2025-05-19_AAGKFFFM5 A38 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47585_NaCl_B2_A38_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47585_NaCl_B2_A38_R2.fastq.gz GRCh38 Salt_donorD firebrick
        47586_Tau_B2_A39 B2 D Taurine 2025-05-19_AAGKFFFM5 A39 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47586_Tau_B2_A39_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47586_Tau_B2_A39_R2.fastq.gz GRCh38 Taurine_donorD royalblue
        47587_NaCl_Tau_B2_A45 B2 D SaltTaurine 2025-05-19_AAGKFFFM5 A45 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47587_NaCl_Tau_B2_A45_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47587_NaCl_Tau_B2_A45_R2.fastq.gz GRCh38 SaltTaurine_donorD plum
        47588_Media_C_A60 C C Control 2025-05-19_AAGKFFFM5 A60 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47588_Media_C_A60_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47588_Media_C_A60_R2.fastq.gz GRCh38 Control_donorC dimgreay
        47589_NaCl_C_A85 C C Salt 2025-05-19_AAGKFFFM5 A85 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47589_NaCl_C_A85_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47589_NaCl_C_A85_R2.fastq.gz GRCh38 Salt_donorC firebrick
        47590_Tau_C_A86 C C Taurine 2025-05-19_AAGKFFFM5 A86 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47590_Tau_C_A86_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47590_Tau_C_A86_R2.fastq.gz GRCh38 Taurine_donorC royalblue
        47591_NaCl_Tau_C_A87 C C SaltTaurine 2025-05-19_AAGKFFFM5 A87 /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47591_NaCl_Tau_C_A87_R1.fastq.gz /vol/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/47591_NaCl_Tau_C_A87_R2.fastq.gz GRCh38 SaltTaurine_donorC plum

        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/rimlsrawdata/fastq/2025-05-19_AAGKFFFM5/Niels_Velthuijs/  # 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
  - treatment_Taurine_Control
  - treatment_SaltTaurine_Control
  - treatment_SaltTaurine_Taurine
  - treatment_Salt_SaltTaurine

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

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