Loading report..

Toolbox

MultiQC Toolbox

Highlight Samples
    Rename Samples

    Paste two columns of a tab-delimited table here (eg. from Excel). First column should be the old name, second column the new name.

      Show / Hide Samples
        Explain with AI

        Configure AI settings to get explanations of plots and data in this report.

        Keys entered here will be stored in your browser's local storage. See the docs.
        Switch out sample names with random identifiers
        Export Plots
        px
        px
        X
        Note: Additional data was saved in multiqc_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
        Report settings are automatically saved in your browser as you use the toolbox. You can also save named configurations below.
        Load Settings

        Choose a saved report profile from the browser or load from a file:

        Tool Citations

        Please remember to cite all of the tools that you use in your analysis.

        About MultiQC

        This report was generated using MultiQC, version 1.33

        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

        MultiQC is developed by Seqera.

        Scroll to top

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2026-05-10, 17:43 UTC based on data in:
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792.bowtie2.log
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792_sorted_dedup.txt
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792.flagstat
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792.stats
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792.insert_size_metrics.txt
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792.peak_stats.tsv
        • /home/fchen3357/nf_work/50/1f8e8df4ca868840a97cab623ba241/SRR27410792.frip.tsv

        General Statistics

        Showing 3/3 rows and 10/15 columns.
        Sample NameInsert SizeMean Insert SizeDuplicationError rateNon-primaryReads mapped% Mapped% Proper pairs% MapQ 0 readsTotal seqsMean insertReadsReads mapped% Reads mapped% Aligned
        SRR27410792
        0.13%
        0.0M
        0.6M
        100.0%
        100.0%
        0.0%
        0.6M
        144.5bp
        0.6M
        0.6M
        100.0%
        91.7%
        SRR27410792_final_sorted
        112bp
        148bp
        sorted
        12.5%

        Picard

        Tools for manipulating high-throughput sequencing data.http://broadinstitute.github.io/picard

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        Created with MultiQC

        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
        Created with MultiQC

        Samtools

        Version: 1.23

        Toolkit for interacting with BAM/CRAM files.http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        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).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat

        Created with MultiQC

        Flagstat: Percentage of total

        This module parses the output from samtools flagstat

        Created with MultiQC

        Bowtie 2 / HiSAT2

        Results from both Bowtie 2 and HISAT2, tools for aligning reads against a reference genome.http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        Samtools1.23