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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 1.18

        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 nf-core/rnasplice analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2025-12-11, 09:27 PST based on data in: /gscratch/scrubbed/srlab/nxf.xxRlPtQ3Zq

        General Statistics

        Showing 39/39 rows and 9/12 columns.
        Sample Name% AssignedM AssignedError rateM Non-PrimaryM Reads Mapped% Mapped% Proper PairsM Total seqsM Reads Mapped
        POC-201-TP1
        40.4%
        8.4
        1.18%
        1.8
        27.6
        70.0%
        55.6%
        39.4
        29.4
        POC-201-TP2
        32.2%
        7.4
        1.18%
        3.0
        27.8
        65.5%
        51.5%
        42.5
        30.9
        POC-201-TP3
        21.5%
        3.5
        1.17%
        1.0
        13.6
        42.9%
        32.6%
        31.7
        14.6
        POC-219-TP1
        35.2%
        7.5
        1.18%
        1.8
        27.1
        66.5%
        52.3%
        40.8
        28.9
        POC-219-TP2
        29.8%
        5.8
        1.21%
        1.7
        23.2
        62.6%
        49.2%
        37.1
        24.9
        POC-219-TP3
        10.3%
        2.2
        1.13%
        0.4
        7.4
        17.7%
        13.4%
        41.9
        7.8
        POC-219-TP4
        28.5%
        5.7
        1.18%
        1.4
        21.1
        55.2%
        42.3%
        38.2
        22.5
        POC-222-TP1
        39.7%
        8.1
        1.19%
        1.9
        27.4
        71.7%
        57.0%
        38.2
        29.3
        POC-222-TP2
        32.2%
        7.4
        1.20%
        2.1
        28.3
        64.8%
        50.9%
        43.7
        30.4
        POC-222-TP3
        42.6%
        10.2
        1.15%
        2.3
        32.2
        71.7%
        57.2%
        44.9
        34.5
        POC-222-TP4
        34.0%
        5.9
        1.19%
        1.5
        22.2
        67.0%
        51.8%
        33.1
        23.7
        POC-255-TP1
        37.7%
        7.5
        1.19%
        1.6
        25.3
        67.3%
        52.8%
        37.6
        26.9
        POC-255-TP2
        33.6%
        7.0
        1.20%
        1.8
        26.1
        65.5%
        52.0%
        39.8
        27.8
        POC-255-TP3
        21.6%
        5.4
        1.17%
        1.9
        23.6
        49.3%
        37.3%
        47.8
        25.4
        POC-255-TP4
        37.7%
        8.1
        1.18%
        1.8
        28.3
        69.3%
        54.2%
        40.9
        30.1
        POC-259-TP1
        30.9%
        5.8
        1.21%
        1.6
        21.2
        59.9%
        47.4%
        35.4
        22.8
        POC-259-TP2
        32.7%
        7.2
        1.20%
        2.2
        25.5
        61.8%
        48.1%
        41.2
        27.6
        POC-259-TP3
        31.8%
        7.2
        1.20%
        1.6
        27.7
        63.6%
        49.5%
        43.5
        29.2
        POC-259-TP4
        38.8%
        7.7
        1.19%
        1.7
        26.3
        69.4%
        55.2%
        37.9
        28.0
        POC-40-TP1
        34.0%
        6.4
        1.22%
        1.8
        24.4
        68.4%
        54.1%
        35.7
        26.2
        POC-40-TP2
        35.0%
        7.7
        1.20%
        1.9
        28.9
        68.9%
        54.4%
        42.0
        30.9
        POC-40-TP3
        35.3%
        7.6
        1.18%
        2.2
        27.7
        68.8%
        54.2%
        40.3
        29.9
        POC-40-TP4
        37.1%
        7.4
        1.22%
        1.6
        26.8
        70.3%
        55.2%
        38.1
        28.4
        POC-42-TP1
        32.5%
        6.1
        1.23%
        1.7
        24.1
        67.6%
        52.3%
        35.7
        25.8
        POC-42-TP2
        35.1%
        8.0
        1.23%
        1.7
        29.9
        68.5%
        55.3%
        43.6
        31.5
        POC-42-TP3
        35.7%
        7.3
        1.21%
        1.8
        27.3
        69.9%
        54.0%
        39.0
        29.0
        POC-42-TP4
        25.0%
        4.2
        1.24%
        1.2
        17.3
        54.4%
        39.6%
        31.7
        18.5
        POC-52-TP1
        9.1%
        1.5
        1.16%
        0.4
        5.5
        17.3%
        12.2%
        31.9
        5.9
        POC-52-TP2
        33.5%
        7.4
        1.20%
        1.9
        28.0
        67.0%
        53.1%
        41.8
        29.9
        POC-52-TP3
        36.7%
        6.9
        1.16%
        1.5
        23.6
        65.8%
        52.6%
        35.8
        25.1
        POC-52-TP4
        42.5%
        7.8
        1.16%
        1.7
        24.8
        70.9%
        57.6%
        35.0
        26.5
        POC-53-TP1
        34.0%
        7.0
        1.20%
        1.8
        25.5
        65.4%
        51.5%
        39.0
        27.3
        POC-53-TP2
        33.7%
        6.7
        1.20%
        1.7
        24.5
        64.8%
        51.6%
        37.8
        26.2
        POC-53-TP3
        37.8%
        8.2
        1.17%
        1.9
        28.1
        68.2%
        54.0%
        41.2
        30.0
        POC-53-TP4
        31.2%
        6.3
        1.18%
        1.2
        22.0
        56.2%
        44.9%
        39.2
        23.2
        POC-57-TP1
        36.0%
        7.0
        1.20%
        1.9
        25.0
        68.5%
        54.6%
        36.4
        26.8
        POC-57-TP2
        23.9%
        4.7
        1.23%
        1.6
        21.4
        57.1%
        43.0%
        37.4
        23.0
        POC-57-TP3
        38.5%
        8.8
        1.17%
        2.2
        30.6
        70.8%
        57.2%
        43.2
        32.8
        POC-57-TP4
        42.3%
        9.1
        1.19%
        1.8
        30.2
        73.3%
        58.9%
        41.1
        32.0

        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.DOI: 10.1093/bioinformatics/btt656.

        loading..

        Samtools

        Version: 1.17

        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 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.

        loading..

        Alignment metrics

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

        loading..

        Samtools Flagstat

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

        loading..

        Mapped reads per contig

        The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

           
        loading..

        Software Versions

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

        SoftwareVersion
        Samtools1.17

        nf-core/rnasplice Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/rnasplice v1.0.4 (doi: 10.5281/zenodo.8424632) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.6 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/rnasplice -resume -c /gscratch/srlab/strigg/bin/uw_hyak_srlab.config --input samplesheet.csv --outdir /gscratch/scrubbed/strigg/analyses/20251205_rnasplice_Ptua --source genome_bam --contrasts contrastsheet.csv --skip_alignment --fasta /gscratch/srlab/strigg/GENOMES/Pocillopora_meandrina_HIv1.assembly.fasta --gtf /gscratch/srlab/strigg/GENOMES/Pocillopora_meandrina_HIv1.genes-validated.gtf --sashimi_plot false

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/rnasplice Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        CONTRASTSHEET_CHECK python 3.9.5
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.12.0
        yaml 6.0.1
        CUSTOM_GETCHROMSIZES getchromsizes 1.16.1
        DEXSEQ_ANNOTATION htseq 2.0.2
        DEXSEQ_COUNT htseq 2.0.2
        DEXSEQ_EXON bioconductor-dexseq 1.36.0
        r-base 4.0.3
        GTF_GENE_FILTER python 3.9.5
        MAKE_TRANSCRIPTS_FASTA rsem 1.3.1
        star 2.7.10a
        RMATS_POST rmats 4.1.2
        SAMPLESHEET_CHECK python 3.9.5
        SAMTOOLS_FLAGSTAT samtools 1.17
        SAMTOOLS_IDXSTATS samtools 1.17
        SAMTOOLS_INDEX samtools 1.17
        SAMTOOLS_SORT samtools 1.17
        SAMTOOLS_STATS samtools 1.17
        SUBREAD_FEATURECOUNTS subread 2.0.1
        Workflow Nextflow 25.04.6
        nf-core/rnasplice 1.0.4

        nf-core/rnasplice Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        revision
        master
        runName
        intergalactic_brattain
        containerEngine
        singularity
        launchDir
        /mmfs1/gscratch/scrubbed/strigg/analyses/20251205_rnasplice_Ptua
        workDir
        /mmfs1/gscratch/scrubbed/strigg/analyses/20251205_rnasplice_Ptua/work
        projectDir
        /mmfs1/gscratch/srlab/nextflow/bin/assets/nf-core/rnasplice
        userName
        strigg
        profile
        standard
        configFiles
        N/A

        Input/output options

        input
        samplesheet.csv
        contrasts
        contrastsheet.csv
        source
        genome_bam
        outdir
        /gscratch/scrubbed/strigg/analyses/20251205_rnasplice_Ptua

        Reference genome options

        fasta
        /gscratch/srlab/strigg/GENOMES/Pocillopora_meandrina_HIv1.assembly.fasta
        gtf
        /gscratch/srlab/strigg/GENOMES/Pocillopora_meandrina_HIv1.genes-validated.gtf
        igenomes_base
        s3://ngi-igenomes/igenomes/

        Institutional config options

        config_profile_description
        UW Hyak Roberts labs cluster profile provided by nf-core/configs.
        config_profile_contact
        Shelly A. Wanamaker @shellywanamaker
        config_profile_url
        https://faculty.washington.edu/sr320/

        Alignment options

        aligner
        star_salmon
        skip_alignment
        true

        rMATS options

        rmats
        true

        DEXSeq DEU options

        dexseq_exon
        true

        edgeR DEU options

        edger_exon
        true

        DEXSeq DTU options

        dexseq_dtu
        true
        min_samps_gene_expr
        4
        min_samps_feature_expr
        2
        min_samps_feature_prop
        2

        Miso

        miso_genes
        ENSG00000004961, ENSG00000005302, ENSG00000147403
        miso_genes_file
        N/A

        MultiQC v1.18 - Written by Phil Ewels, available on GitHub.

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