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RNAseq report for sample_report

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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in sample_report_multiqc_report_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 1.9

        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

        RNAseq report for sample_report

        This report includes summaries of data quality, data processing, and snapshots of results for your RNA-Seq study. This report should assist you to get a general picture of the study, to spot any irregularities in the sample or data, and to explore the most significant results in differential gene expression. Please consult our RNAseq report documentation on how to use this report.


        General Statistics

        Showing 8/16 rows and 9/18 columns.
        Sample NameM Seqs% GC% Reads PF% Aligned% Dups% rRNA% AssignedM AssignedNumbers of genes detected
        Ctl_PE_1
        7.9
        48%
        99.4%
        94.0%
        5.2%
        0.15%
        77.7%
        6.0
        25329
        Ctl_PE_1.markDups
        Ctl_PE_2
        7.4
        49%
        99.1%
        93.2%
        4.7%
        0.22%
        78.2%
        5.5
        25084
        Ctl_PE_2.markDups
        Ctl_SE_1
        73.0
        46%
        99.8%
        91.2%
        53.0%
        0.15%
        72.7%
        51.5
        36044
        Ctl_SE_1.markDups
        Ctl_SE_2
        94.7
        47%
        99.6%
        91.1%
        56.5%
        0.21%
        73.3%
        67.4
        37390
        Ctl_SE_2.markDups
        Zika_PE_1
        7.4
        48%
        99.2%
        93.8%
        4.1%
        0.41%
        76.2%
        5.4
        24902
        Zika_PE_1.markDups
        Zika_PE_2
        7.6
        48%
        99.1%
        93.5%
        4.1%
        0.39%
        76.7%
        5.6
        24837
        Zika_PE_2.markDups
        Zika_SE_1
        66.5
        46%
        99.5%
        91.0%
        49.8%
        0.36%
        71.8%
        46.0
        35522
        Zika_SE_1.markDups
        Zika_SE_2
        76.3
        47%
        99.6%
        91.0%
        52.1%
        0.38%
        71.4%
        52.5
        36457
        Zika_SE_2.markDups

        FastQC

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Only QC results of read 1 are plotted here. Please contact us for reads 2 QC plots if interested.

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

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        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

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        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

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        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

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        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

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        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        8 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        No samples found with any adapter contamination > 0.1%

        Trim Galore

        Trim Galore is a wrapper around Cutadapt and FastQC to consistently apply adpater and quality trimming to FastQ files.

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Trim Galore.

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        Trimmed Sequence Lengths

        This plot shows the number of reads with certain lengths of adapter trimmed. Quality trimmed and hard trimmed sequences are not included.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length. See the cutadapt documentation for more information on how these numbers are generated.

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        STAR

        STAR is an ultrafast universal RNA-seq aligner.

        The sorted BAM files produced by STAR can be downloaded in the Download data section.

        Alignment Scores

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        RSeQC

        RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.

        Read Distribution

        Read Distribution calculates how mapped reads are distributed over genome features.

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        Inner Distance

        Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

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        Read Duplication

        read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

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        Junction Annotation

        Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

           
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        Junction Saturation

        Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

        Click a line to see the data side by side (as in the original RSeQC plot).

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        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).

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        Bam Stat

        All numbers reported in millions.

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        QualiMap

        QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

        Gene Coverage Profile

        Mean distribution of coverage depth across the length of all mapped transcripts.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

        QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

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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
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        Preseq

        Preseq estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count. A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.

        Complexity curve

        Note that the x axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.

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        DupRadar

        provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.

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        Biotype Counts

        shows reads overlapping genomic features of different biotypes, counted by featureCounts.

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        featureCounts

        Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.

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        Distances/similarities between samples

        This section plots the distances or similarities between samples in the form of heatmap, PCA, and/or MDS plots.

        Similarity matrix of samples

        The similarities (Pearson correlation coefficient) between samples are visualized here in the form of heatmap. Larger values indicate higher similarity between samples. The similarities were calculated using normalized and 'rlog' transformed read counts of all genes using DESeq2.

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        Multidimensional scaling analysis of samples

        Multidimensional scaling was conducted to visualize the distance/similarity between samples. Top 500 genes with highest variance among samples were used to make this plot.

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        Top gene expression patterns

        Normalized read counts of top genes with highest variance, calculated using DESeq2. Values plotted in Log2 scale after centering per gene. A static version of this figure can be download in the Download data section.

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        Differential gene expression

        DESeq2 calculates the expression levels of genes and conducts statistical analysis of differential gene expression.

        Summary table of gene differential expression

        General statistics of differentially expressed genes in pairwise comparisons. Genes with adjusted p-values smaller than 0.05 were considered differentially expressed.

        Showing 1/1 rows and 4/4 columns.
        Comparison(cond.1_cond.2)Higher in condition 1Higher in condition 2Not differentially expressedDid not pass filter
        Control_vs_Zika
        698
        536
        21333
        21227

        Scatter plot

        Scatter plot is a simple and straightforward way to visualize differential gene expression results. Expression levels of genes in one condition are shown on X-axis while those in other are shown on Y-axis.
        The scatter plots here include differentially expressed genes (up to the first 1000 genes) and up to 1000 randomly selected non-differentially expressed genes. You can download the scatter plots with all genes in the Download data section.
        Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Count data transformation was carried out using the 'rlog' method in DESeq2

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        MA plot

        MA plot is a type of visualization of differential gene expression results often used in publications. Expression levels are shown on X-axis while log2 of fold changes are shown on Y-axis.
        The MA plots here include differentially expressed genes (up to the first 1000 genes) and up to 1000 randomly selected non-differentially expressed genes. You can download the MA plots with all genes in the Download data section.
        Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Shrinkage of effect size was carried out using the 'normal' method in DESeq2

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        Top differentially expressed genes in comparison Control vs. Zika

        Top 50 differentially expressed genes, ranked by FDR, in comparison Control vs. Zika. Full DESeq2 results can be downloaded in the Download data section.
        Genes with positive Log2 fold changes have higher expression in Control. Genes with negative Log2 fold changes have higher expression in Zika.

        Showing 50/50 rows and 4/4 columns.
        RankGene nameMean countsLog2 Fold changeFalse discovery rate
        12354-2.426.3e-130
        25516-2.491.4e-118
        32090-2.119.6e-116
        42510-2.505.6e-111
        5380-3.061.8e-101
        61805-2.111.6e-98
        7334-3.167.2e-93
        82684-1.831.1e-92
        91841-2.229.9e-90
        101531-1.921.8e-86
        115135-1.722.5e-79
        12639-2.893.0e-75
        13522-2.961.5e-74
        1410342.091.0e-73
        15322-2.811.2e-72
        1611012.392.0e-71
        17617-3.042.0e-68
        182699-2.161.1e-67
        193068-1.651.7e-64
        20247-3.294.2e-60
        2159032.095.0e-54
        22656-2.262.8e-49
        23366-2.315.3e-47
        2420111.721.2e-44
        252079-1.487.0e-43
        261103.617.5e-43
        273485-1.731.4e-42
        2811541.871.5e-41
        291286-1.883.3e-41
        3010231.942.5e-40
        316051.811.7e-39
        323372.311.7e-39
        334793-1.332.9e-39
        3414661.701.1e-38
        357551.706.3e-38
        364342.323.2e-37
        37284-2.321.0e-36
        381523-1.381.6e-36
        39566-1.941.0e-34
        404312.011.3e-33
        411479-2.579.4e-33
        42414-1.881.1e-32
        4311681.741.2e-32
        4410471.831.4e-31
        454331.781.5e-31
        467501.598.0e-31
        475422.391.4e-30
        48564-1.664.8e-30
        4911041.415.9e-30
        502382.226.3e-30

        Gene set enrichment analysis

        g:Profiler performs functional enrichment analysis also known as gene set enrichment analysis on input gene list. It maps genes to known functional information sources and detects statistically significantly enriched terms.

        Summary table of gene set enrichment analysis

        General statistics of gene set enrichment analysis in pairwise comparisons. Gene sets with false discovery rate smaller than 0.05 were considered enriched.

        Showing 1/1 rows and 3/3 columns.
        Comparison(cond.1_cond.2)Higher in condition 1Higher in condition 2Not enriched
        Control_vs_Zika
        207
        308
        12169

        Top enriched gene sets in comparison Control vs. Zika

        Top 30 gene sets, ranked by p-value, in comparison Control vs. Zika. Full g:Profiler results can be downloaded in the Download data section.

        Showing 30/30 rows and 4/4 columns.
        RankGene set nameGene set categoryAdjusted p-valueExpression pattern
        1DNA-templated DNA replicationGO:Biological Process0.000Higher in Control
        2DNA replicationGO:Biological Process0.000Higher in Control
        3cell cycle processGO:Biological Process0.000Higher in Control
        4response to endoplasmic reticulum stressGO:Biological Process0.000Higher in Zika
        5mitotic cell cycle processGO:Biological Process0.000Higher in Control
        6cell cycleGO:Biological Process0.000Higher in Control
        7mitotic cell cycleGO:Biological Process0.000Higher in Control
        8DNA strand elongationReactome Pathway0.000Higher in Control
        9double-strand break repair via break-induced replicationGO:Biological Process0.000Higher in Control
        10DNA metabolic processGO:Biological Process0.000Higher in Control
        11DNA unwinding involved in DNA replicationGO:Biological Process0.000Higher in Control
        12chromosome organizationGO:Biological Process0.000Higher in Control
        13Unwinding of DNAReactome Pathway0.000Higher in Control
        14endoplasmic reticulum unfolded protein responseGO:Biological Process0.000Higher in Zika
        15DNA duplex unwindingGO:Biological Process0.000Higher in Control
        16nuclear divisionGO:Biological Process0.000Higher in Control
        17cell cycle phase transitionGO:Biological Process0.000Higher in Control
        18intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stressGO:Biological Process0.000Higher in Zika
        19Cell Cycle MitoticReactome Pathway0.000Higher in Control
        20cell cycle checkpoint signalingGO:Biological Process0.000Higher in Control
        21DNA geometric changeGO:Biological Process0.000Higher in Control
        22DNA repairGO:Biological Process0.000Higher in Control
        23DNA replication initiationGO:Biological Process0.000Higher in Control
        24response to topologically incorrect proteinGO:Biological Process0.000Higher in Zika
        25cellular response to topologically incorrect proteinGO:Biological Process0.000Higher in Zika
        26regulation of cell cycle processGO:Biological Process0.000Higher in Control
        27response to unfolded proteinGO:Biological Process0.000Higher in Zika
        28cellular response to unfolded proteinGO:Biological Process0.000Higher in Zika
        29regulation of response to endoplasmic reticulum stressGO:Biological Process0.000Higher in Zika
        30Cell CycleReactome Pathway0.000Higher in Control

        Download data

        This section contains links to download your original data, and data and/or images generated by various bioinformatics tools. There may be files for each sample, files for all samples, and files for group comparisons. To download individual files, click on the corresponding links. There are also instructions at the bottom of the this section if you want to download everything in batch.

        Links in this section expire after 270 days. If you want to download files after that, please contact us.

        Sample level files

        Showing 8/8 rows and 3/3 columns.
        Sample NameRead #1Read #2Alignment
        Ctl_PE_1FASTQFASTQBAM
        Ctl_PE_2FASTQFASTQBAM
        Ctl_SE_1FASTQBAM
        Ctl_SE_2FASTQBAM
        Zika_PE_1FASTQFASTQBAM
        Zika_PE_2FASTQFASTQBAM
        Zika_SE_1FASTQBAM
        Zika_SE_2FASTQBAM

        Files concerning all samples

        These files provide an overview of all samples (some of these are already displayed interactively for you in sections above):

        Comparison level files

        Showing 1/1 rows and 4/4 columns.
        Comparison(group1_vs_group2)DEG comparison resultsMA plotScatter plotPathway enrichment results
        Control_vs_ZikaXLSXJPGJPGXLSX

        Instructions to download all files

        1. Download a script to download all files. We assume it is in your Downloads folder.
        2. Find and open Terminal(Mac/Linux) or Windows Powershell(Windows).
        3. Type cd ~/Downloads and Enter. (If your download folder is different, please change accordingly)
        4. Copy and Paste bash download_links.ps1 (Mac/Linux) or Powershell.exe -ExecutionPolicy Bypass -File .\download_links.ps1 (Windows) and Enter.

        Software Versions

        Software versions are collected at run time from the software output. This pipeline is adapted from nf-core RNAseq pipeline.

        RNAseq pipeline
        v2.1.0
        Nextflow
        v22.10.4
        FastQC
        v0.11.9
        Trim Galore!
        v0.6.6
        STAR
        v2.6.1d
        Samtools
        v1.9
        Preseq
        v2.0.3
        Picard MarkDuplicates
        v2.23.9
        dupRadar
        v1.18.0
        RSeQC
        v4.0.0
        Qualimap
        v2.2.2-dev
        featureCounts
        v2.0.1
        DESeq2
        v1.28.0
        gProfiler
        v1.0.0

        Workflow Summary

        This section summarizes important parameters used in the pipeline. They were collected when the pipeline was started.

        Genome
        GRCh38
        DESeq2 FDR cutoff
        0.05
        DESeq2 Log2FC cutoff
        0.585
        gProfiler FDR cutoff
        0.05
        Trimming
        only using Illumina adatpers
        Strandedness
        None
        Library Prep
        Illumina RNA kits

        Report generated on 2024-03-14, 23:35.