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        Note that additional data was saved in multiqc_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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        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 1.13

        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

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

        This report has been generated by the Arcadia-Science/metagenomics analysis pipeline using the Illumina workflow.

        Report generated on 2023-05-21, 02:28 UTC based on data in: /tmp/nxf.1x5laC0nuT

        General Statistics

        Showing 18/18 rows and 12/15 columns.
        Sample NameN50 (Kbp)Assembly Length (Mbp)Error rateM Non-PrimaryM Reads Mapped% Mapped% Proper PairsM Total seqs% DuplicationGC content% PF% Adapter
        EL12weeks
        0.69%
        0.0
        70.3
        58.5%
        17.7%
        120.2
        28.4%
        46.9%
        99.7%
        0.0%
        EL12weeks.reformatted
        4.6Kbp
        104.9Mbp
        EL2weeks
        0.86%
        0.0
        105.1
        97.8%
        93.3%
        107.5
        3.6%
        40.7%
        98.9%
        0.4%
        EL2weeks.reformatted
        32.3Kbp
        88.6Mbp
        EL4weeks
        0.64%
        0.0
        140.0
        98.1%
        92.6%
        142.7
        6.4%
        42.7%
        99.5%
        0.2%
        EL4weeks.reformatted
        25.5Kbp
        83.3Mbp
        OM2weeks
        0.79%
        0.0
        110.0
        97.9%
        90.4%
        112.3
        4.4%
        45.1%
        99.1%
        0.1%
        OM2weeks.reformatted
        20.4Kbp
        85.5Mbp
        OM4weeks
        0.77%
        0.0
        107.6
        98.0%
        90.6%
        109.8
        6.4%
        42.3%
        99.3%
        0.1%
        OM4weeks.reformatted
        5.8Kbp
        83.3Mbp
        OM8weeks
        0.79%
        0.0
        111.1
        97.9%
        90.6%
        113.4
        4.9%
        45.5%
        99.2%
        0.2%
        OM8weeks.reformatted
        12.0Kbp
        120.6Mbp
        WH1month
        1.12%
        0.0
        116.5
        97.3%
        92.6%
        119.7
        3.2%
        58.5%
        98.5%
        0.8%
        WH1month.reformatted
        10.0Kbp
        133.4Mbp
        WH2months
        1.13%
        0.0
        122.2
        97.8%
        93.6%
        125.0
        3.1%
        62.0%
        98.4%
        0.8%
        WH2months.reformatted
        6.6Kbp
        162.9Mbp
        WH4months
        0.43%
        0.0
        111.6
        98.5%
        93.5%
        113.3
        18.5%
        53.6%
        99.5%
        0.2%
        WH4months.reformatted
        28.2Kbp
        117.8Mbp

        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.

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        Sequence Quality

        Average sequencing quality over each base of all reads.

        loading..

        GC Content

        Average GC content over each base of all reads.

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        N content

        Average N content over each base of all reads.

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        QUAST

        QUAST is a quality assessment tool for genome assemblies, written by the Center for Algorithmic Biotechnology.DOI: 10.1093/bioinformatics/btt086.

        Assembly Statistics

        Showing 9/9 rows and 4/4 columns.
        Sample NameN50 (Kbp)L50 (K)Largest contig (Kbp)Length (Mbp)
        EL12weeks.reformatted
        4.6Kbp
        3.3K
        270.1Kbp
        104.9Mbp
        EL2weeks.reformatted
        32.3Kbp
        0.6K
        863.9Kbp
        88.6Mbp
        EL4weeks.reformatted
        25.5Kbp
        0.7K
        610.0Kbp
        83.3Mbp
        OM2weeks.reformatted
        20.4Kbp
        0.9K
        1401.5Kbp
        85.5Mbp
        OM4weeks.reformatted
        5.8Kbp
        2.8K
        266.5Kbp
        83.3Mbp
        OM8weeks.reformatted
        12.0Kbp
        1.9K
        489.5Kbp
        120.6Mbp
        WH1month.reformatted
        10.0Kbp
        2.5K
        568.7Kbp
        133.4Mbp
        WH2months.reformatted
        6.6Kbp
        4.0K
        397.2Kbp
        162.9Mbp
        WH4months.reformatted
        28.2Kbp
        0.8K
        568.7Kbp
        117.8Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        loading..

        Samtools

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        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..

        Arcadia-Science/metagenomics Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        BOWTIE2_ASSEMBLY_ALIGN bowtie2 2.4.2
        pigz 2.3.4
        samtools 1.11
        BOWTIE2_ASSEMBLY_BUILD bowtie2 2.4.2
        CHECK_SAMPLESHEET python 3.9.5
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.10.6
        yaml 6.0
        FASTP fastp 0.23.2
        METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS metabat2 2.15
        METASPADES metaspades 3.15.3
        python 3.9.6
        PRODIGAL pigz 2.6
        prodigal 2.6.3
        QUAST quast 5.2.0
        SAMTOOLS_STATS samtools 1.16.1
        SOURMASH_COMPARE sourmash 4.6.1
        SOURMASH_GATHER sourmash 4.6.1
        SOURMASH_SKETCH sourmash 4.6.1
        SOURMASH_TAXANNOTATE sourmash 4.6.1
        Workflow Arcadia-Science/metagenomics 1.0dev
        Nextflow 23.04.1

        Arcadia-Science/metagenomics Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        runName
        TMI_illumina_final_2
        containerEngine
        docker
        launchDir
        /
        workDir
        /nf-metagenomics/scratch/4VPcSbY63dfA7t
        projectDir
        /.nextflow/assets/Arcadia-Science/metagenomics
        userName
        root
        profile
        docker
        configFiles
        /.nextflow/assets/Arcadia-Science/metagenomics/nextflow.config, /nextflow.config

        Input/output options

        input
        s3://nf-metagenomics/metagenomics_samplesheets/TMI_illumina_samplesheet_s3uri.csv
        outdir
        s3://nf-metagenomics/TMI_illumina_processing
        platform
        illumina
        email
        [email protected]
        sourmash_dbs
        s3://nf-metagenomics/databases/sourmash-cover-dbs.csv
        diamond_db
        s3://software-databases/uniprot/2023-04-26-uniref90.dmnd
        diamond_columns
        qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen staxids sscinames stitle
        multiqc_title
        TMI Illumina

        Max job request options

        max_memory
        400.GB

        Other parameters

        config_profile_description
        N/A
        config_profile_contact
        N/A
        config_profile_url
        N/A
        config_profile_name
        N/A

        MultiQC v1.13 - Written by Phil Ewels, available on GitHub.

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