Powered by

RNAseq report for zr4059

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 zr4059_multiqc_report_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:

        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 zr4059

        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 26/52 rows and 9/18 columns.
        Sample NameM Seqs% GC% Reads PF% Aligned% Dups% AssignedM AssignedrRNANumbers of genes detected
        S12M
        34.3
        44%
        90.2%
        54.2%
        5.5%
        13.2%
        2.8
        0.0
        36391
        S12M.markDups
        S13M
        29.5
        51%
        78.6%
        53.0%
        5.8%
        13.4%
        2.1
        0.0
        35680
        S13M.markDups
        S16F
        24.2
        43%
        99.3%
        71.9%
        28.8%
        55.3%
        11.5
        0.0
        30447
        S16F.markDups
        S19F
        24.3
        43%
        99.6%
        74.0%
        31.9%
        51.0%
        10.8
        0.0
        29721
        S19F.markDups
        S22F
        28.5
        43%
        99.7%
        74.0%
        31.4%
        53.5%
        13.4
        0.0
        31595
        S22F.markDups
        S23M
        40.5
        45%
        88.3%
        54.8%
        5.8%
        13.0%
        3.2
        0.0
        36621
        S23M.markDups
        S29F
        22.6
        42%
        99.7%
        68.4%
        23.3%
        45.5%
        8.5
        0.0
        34236
        S29F.markDups
        S31M
        20.4
        39%
        99.4%
        55.4%
        5.6%
        10.8%
        1.5
        0.0
        35892
        S31M.markDups
        S35F
        20.3
        43%
        99.6%
        73.9%
        29.2%
        51.8%
        9.2
        0.0
        30003
        S35F.markDups
        S36F
        21.7
        42%
        99.7%
        72.2%
        28.4%
        50.7%
        9.5
        0.0
        30343
        S36F.markDups
        S39F
        24.5
        43%
        99.7%
        74.7%
        31.1%
        52.9%
        11.4
        0.0
        29576
        S39F.markDups
        S3F
        22.1
        43%
        99.5%
        75.0%
        30.8%
        49.8%
        9.5
        0.0
        31014
        S3F.markDups
        S41F
        22.6
        43%
        99.7%
        69.9%
        27.2%
        48.6%
        9.2
        0.0
        33598
        S41F.markDups
        S44F
        25.0
        44%
        99.5%
        69.2%
        28.8%
        50.2%
        10.5
        0.0
        33202
        S44F.markDups
        S48M
        67.4
        49%
        79.5%
        54.1%
        7.5%
        12.9%
        4.8
        0.0
        37023
        S48M.markDups
        S50F
        20.3
        43%
        99.7%
        74.3%
        29.0%
        52.8%
        9.4
        0.0
        30358
        S50F.markDups
        S52F
        23.7
        42%
        99.7%
        67.0%
        19.3%
        49.9%
        9.4
        0.0
        34609
        S52F.markDups
        S53F
        23.2
        43%
        99.7%
        73.1%
        30.1%
        53.0%
        10.6
        0.0
        31244
        S53F.markDups
        S54F
        23.2
        43%
        99.7%
        69.8%
        26.5%
        48.4%
        9.4
        0.0
        33620
        S54F.markDups
        S59M
        21.9
        41%
        99.6%
        61.0%
        17.6%
        35.7%
        5.7
        0.0
        34938
        S59M.markDups
        S64M
        23.9
        42%
        99.4%
        50.4%
        33.2%
        18.9%
        3.5
        0.0
        35313
        S64M.markDups
        S6M
        43.8
        48%
        80.8%
        54.6%
        5.1%
        13.7%
        3.3
        0.0
        36536
        S6M.markDups
        S76F
        26.1
        44%
        99.6%
        74.7%
        35.2%
        48.3%
        10.9
        0.0
        32186
        S76F.markDups
        S77F
        27.9
        44%
        99.7%
        75.8%
        35.2%
        51.7%
        12.7
        0.0
        30052
        S77F.markDups
        S7M
        29.4
        45%
        89.3%
        54.0%
        5.4%
        13.3%
        2.4
        0.0
        36196
        S7M.markDups
        S9M
        37.6
        47%
        85.1%
        54.5%
        4.8%
        13.1%
        2.9
        0.0
        36344
        S9M.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.

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        Sequence Length Distribution

        All samples have sequences of a single length (151bp).

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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.

           
        loading..

        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).

        loading..

        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..

        Bam Stat

        All numbers reported in millions.

        loading..

        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).

        loading..

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        loading..

        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.

        loading..

        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.

        loading..

        Biotype Counts

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

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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.

        loading..

        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 60 days. If you want to download files after that, please contact us.

        Sample level files

        Showing 26/26 rows.
        Sample NameAlignment
        S12MBAM
        S13MBAM
        S16FBAM
        S19FBAM
        S22FBAM
        S23MBAM
        S29FBAM
        S31MBAM
        S35FBAM
        S36FBAM
        S39FBAM
        S3FBAM
        S41FBAM
        S44FBAM
        S48MBAM
        S50FBAM
        S52FBAM
        S53FBAM
        S54FBAM
        S59MBAM
        S64MBAM
        S6MBAM
        S76FBAM
        S77FBAM
        S7MBAM
        S9MBAM

        Files concerning all samples

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

        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
        v21.10.5
        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

        Workflow Summary

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

        Genome
        C_virginica-3.0
        DESeq2 FDR cutoff
        0.05
        DESeq2 Log2FC cutoff
        0.585
        gProfiler FDR cutoff
        0.05
        Trimming
        5'R2: 10bp / adapter1: NNNNNNNNNNAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC / adapter2: AGATCGGAAGAGCGTCGTGTAGGGAAAGA
        Strandedness
        Reverse
        Library Prep
        Zymo-Seq RiboFree Total RNA Library Kit

        Report generated on 2022-03-02, 06:49.