LORA pipeline MultiQC summary

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        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

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        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 1.27

        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/MultiQC/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

        LORA pipeline MultiQC summary
        LORA analysis

        Report summarising assembly, quast, prokka, etc

        Authors
        Sequana developers
        Want to know more?
        See the Sequana and LORA pipeline documentation.
        Citations
        If you used Sequanix, Sequana, Sequana_coverage tool, or any Sequana pipelines, please see How to cite ? section. In particular, if you use this report in a publication, please cite Sequana.
        Contact E-mail
        Report generated on 2026-03-26, 23:10 CET based on data in: /home/cokelaer/Work/github/paper_LORA/data/flye_10_bis

        General Statistics

        Showing 16/16 rows and 15/18 columns.
        Sample NameBOCDOCROIlengthCVC3N50 (Kbp)Assembly Length (Mbp)OrganismContigsBasesCDS% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF
        hifi
        2205.7Kbp
        2.2Mbp
        Genus species
        2
        2218423
        2284
        hifi2
        2206.0Kbp
        2.2Mbp
        Genus species
        2
        2218693
        2283
        hifi2/contig_1
        100.00
        82.22
        44
        2205964
        0.23
        0.94
        hifi2/contig_3
        100.00
        62.77
        8
        12729
        0.11
        0.95
        hifi3
        2205.7Kbp
        2.2Mbp
        Genus species
        2
        2218423
        2282
        hifi3/contig_1
        100.00
        65.71
        75
        2205694
        0.24
        0.91
        hifi3/contig_2
        100.00
        49.18
        12
        12729
        0.13
        0.93
        hifi4
        2205.3Kbp
        2.2Mbp
        Genus species
        2
        2218030
        2283
        hifi4/contig_1
        100.00
        49.23
        58
        2205301
        0.26
        0.91
        hifi4/contig_2
        100.00
        38.95
        15
        12729
        0.12
        0.84
        hifi5
        2206.9Kbp
        2.2Mbp
        Genus species
        2
        2219602
        2282
        hifi5/contig_1
        100.00
        32.70
        51
        2206873
        0.27
        0.91
        hifi5/contig_2
        100.00
        24.97
        0
        12729
        0.13
        1.00
        hifi/contig_1
        100.00
        112.31
        45
        2205694
        0.22
        0.94
        hifi/contig_2
        100.00
        80.50
        11
        12729
        0.09
        0.90
        stdin
        0.4%
        98.9%
        107.9Mb
        0.0M
        37.5%
        98.0%

        Sequana/coverage

        Sequana individual report pages:

        Depth of Coverage Histogram

        Histogram (normalised) of the depth of coverage. For convenience, only 99% the data (centered) to avoid outliers. For detailled histograms, please see the links above

        Created with MultiQC

        Depth of coverage

        Depth of coverage: average number of reads mapping on each genome position

        Created with MultiQC

        Breadth of coverage

        Breadth of coverage: proportion of the genome covered by at least one read

        Created with MultiQC

        Coefficient of Variation

        The ratio of DOC mean by DOC standard deviation

        Created with MultiQC

        Contig length

        Length of the contig/chromosome

        Created with MultiQC

        ROI

        Number of regions of interest

        Created with MultiQC

        C3

        Centralness (roughly speaking, ratio of outliers versus total genome length).

        Created with MultiQC

        QUAST

        Quality assessment tool for genome assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 5/5 rows and 4/4 columns.
        Sample NameN50 (Kbp)L50 (K)Largest contig (Kbp)Length (Mbp)
        hifi
        2205.7Kbp
        0.0K
        2205.7Kbp
        2.2Mbp
        hifi2
        2206.0Kbp
        0.0K
        2206.0Kbp
        2.2Mbp
        hifi3
        2205.7Kbp
        0.0K
        2205.7Kbp
        2.2Mbp
        hifi4
        2205.3Kbp
        0.0K
        2205.3Kbp
        2.2Mbp
        hifi5
        2206.9Kbp
        0.0K
        2206.9Kbp
        2.2Mbp

        Number of Contigs

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

        Created with MultiQC

        BUSCO

        Version: 6.0.0

        Assesses genome assembly and annotation completeness.URL: http://busco.ezlab.orgDOI: 10.1093/bioinformatics/btv351

        BUSCO v2 provides quantitative measures for the assessment of genome assembly, gene set, and transcriptome completeness, based on evolutionarily-informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB v9.

        Lineage: bacteria_odb10

        Created with MultiQC

        Prokka

        Rapid annotation of prokaryotic genomes.URL: http://www.vicbioinformatics.com/software.prokka.shtmlDOI: 10.1093/bioinformatics/btu153

        This barplot shows the distribution of different types of features found in each contig.

        Prokka can detect different features:

        • CDS
        • rRNA
        • tmRNA
        • tRNA
        • miscRNA
        • signal peptides
        • CRISPR arrays

        This barplot shows you the distribution of these different types of features found in each contig.

        Created with MultiQC

        fastp

        Version: 0.23.3

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC

        Software Versions

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

        SoftwareVersion
        BUSCO6.0.0
        fastp0.23.3

        MultiQC v1.27 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap and FileSaver.js.