MoP4 - pipeline

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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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        About MultiQC

        This report was generated using MultiQC, version 1.27.1

        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

        MoP4 - pipeline

        Please cite
        Cozzuto L, Liu H, Pryszcz LP, Pulido TH, Delgado-Tejedor A, Ponomarenko J, Novoa EM. MasterOfPores: A Workflow for the Analysis of Oxford Nanopore Direct RNA Sequencing Datasets. Front Genet. 2020 Mar 17;11:211. doi: 10.3389/fgene.2020.00211
        And
        Cozzuto L, Delgado-Tejedor A, Hermoso Pulido T, Novoa EM, Ponomarenko J. Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores. N. Methods Mol Biol. 2023;2624:185-205. doi: 10.1007/978-1-0716-2962-8_13.
        Report generated on 2025-04-01, 17:14 UTC based on data in: /nfs/scratch01/bi/master_of_pores/98/7e30a12d4cb8dc12c41fa177a01313

        General Statistics

        Showing 2/2 rows and 4/16 columns.
        Sample NameReadsBasesRead N50Longest ReadShortest ReadMean LengthMedian LengthMean QualMedian QualMedian lengthRead N50Median QualMedian Identity# Reads (K)Total Bases (Mb)Aligned Bases (Mb)
        A
        0.0M
        3.3Mb
        2463bp
        5421bp
        6bp
        1616.0bp
        2085
        18.1
        18.5
        2156bp
        2463bp
        18.6
        98.5%
        2.0K
        3.3Mb
        3.3Mb
        m6A
        0.0M
        3.0Mb
        2201bp
        6248bp
        147bp
        1496.0bp
        1514
        13.6
        13.1
        1511bp
        2200bp
        13.1
        91.9%
        2.0K
        3.0Mb
        3.0Mb

        Alignment QC

        Showing 2/2 rows and 7/7 columns.
        File nameMapped readsMap %BasesBases %Avg read lengthMax read lengthidentity
        A_s.bam
        2007
        100.0%
        3262038
        99.1%
        1639
        5421
        98.14%
        m6A_s.bam
        1999
        100.0%
        2946985
        98.7%
        1494
        6248
        91.40%

        Assigned counts

        Showing 2/2 rows.
        File nameCounts
        A
        2006
        m6A
        1994

        nanoq

        Reports read quality and length from nanopore sequencing data.URL: https://github.com/nerdna/nanoqDOI: 10.21105/joss.02991

        Nanoq Summary

        Statistics from Nanoq reports

        Showing 2/2 rows and 9/9 columns.
        Sample NameReadsBasesRead N50Longest ReadShortest ReadMean LengthMedian LengthMean QualMedian Qual
        A
        0.0M
        3.3Mb
        2463bp
        5421bp
        6bp
        1616.0bp
        2085
        18.1
        18.5
        m6A
        0.0M
        3.0Mb
        2201bp
        6248bp
        147bp
        1496.0bp
        1514
        13.6
        13.1

        Read quality

        Read counts categorised by read quality (Phred score).

        Sequencing machines assign each generated read a quality score using the Phred scale. The phred score represents the liklelyhood that a given read contains errors. High quality reads have a high score.

        Created with MultiQC

        Read lengths

        Read counts categorised by read length.

        Created with MultiQC

        NanoStat

        Reports various statistics for long read dataset in FASTQ, BAM, or albacore sequencing summary format (supports NanoPack; NanoPlot, NanoComp).URL: https://github.com/wdecoster/nanostat; https://github.com/wdecoster/nanoplotDOI: 10.1093/bioinformatics/bty149

        Programs are part of the NanoPack family for summarising results of sequencing on Oxford Nanopore methods (MinION, PromethION etc.)

        Summary Statistics (Aligned)

        Showing 2/2 rows and 7/10 columns.
        Sample NameMedian lengthMean lengthRead N50Median QualMean QualMedian IdentityMean Identity# Reads (K)Total Bases (Mb)Aligned Bases (Mb)
        A
        2156bp
        1640bp
        2463bp
        18.6
        18.3
        98.5%
        98.1%
        2.0K
        3.3Mb
        3.3Mb
        m6A
        1511bp
        1492bp
        2200bp
        13.1
        13.6
        91.9%
        92.2%
        2.0K
        3.0Mb
        3.0Mb

        Reads by quality

        Read counts categorised by read quality (Phred score).

        Sequencing machines assign each generated read a quality score using the Phred scale. The phred score represents the liklelyhood that a given read contains errors. High quality reads have a high score.

        Created with MultiQC