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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 all of the tools that you use in your analysis.

        About MultiQC

        This report was generated using MultiQC, version 1.33

        Video: Using MultiQC Reports MultiQC homepage MultiQC documentation Source code Issue tracker

        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.

        This report has been generated by the nf-core/sarek analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2026-06-08, 21:03 UTC based on data in: /tmp/nxf.5RolC7qCZi

        General Statistics

        Showing 26/26 rows and 15/24 columns.
        Sample NameError rateNon-primaryReads mapped% Mapped% Proper pairs% MapQ 0 readsTotal seqsMean insert≥ 1X≥ 5X≥ 10X≥ 30X≥ 50XMedianVarsSNPIndelTs/TvMNPMultiallelicMultiallelic SNPChange rateTs/TvM Variants
        HCC1395_BL
        0.45%
        0.0M
        2.0M
        100.0%
        99.9%
        13.0%
        2.0M
        301.7bp
        100.0%
        100.0%
        100.0%
        100.0%
        99.0%
        149X
        4
        0
        0
        0.00
        0
        0
        0
        HCC1395_BL.deconflicted_germline
        354
        334
        20
        3.58
        0
        0
        0
        HCC1395_BL.deconflicted_germline_custom.ann_snpEff
        131949
        3.461
        0.00M
        HCC1395_BL_custom.ann_snpEff
        193675569
        0.000
        0.00M
        HCC1395_tumor
        0.45%
        0.0M
        5.3M
        100.0%
        99.9%
        13.1%
        5.3M
        292.3bp
        100.0%
        100.0%
        100.0%
        100.0%
        100.0%
        334X
        HCC1395_tumor_vs_HCC1395_BL.manta.diploid_sv
        3
        0
        0
        0.00
        0
        0
        0
        HCC1395_tumor_vs_HCC1395_BL.manta.diploid_sv_custom.ann_snpEff
        15569994
        0.000
        0.00M
        HCC1395_tumor_vs_HCC1395_BL.manta.somatic_sv
        2
        0
        0
        0.00
        0
        0
        0
        HCC1395_tumor_vs_HCC1395_BL.manta.somatic_sv_custom.ann_snpEff
        23354991
        0.000
        0.00M
        HCC1395_tumor_vs_HCC1395_BL.strelka.somatic_indels
        42
        0
        42
        0.00
        0
        0
        0
        HCC1395_tumor_vs_HCC1395_BL.strelka.somatic_indels_custom.ann_snpEff
        1112142
        0.000
        0.00M
        HCC1395_tumor_vs_HCC1395_BL.strelka.somatic_snvs
        300
        300
        0
        1.07
        0
        0
        0
        HCC1395_tumor_vs_HCC1395_BL.strelka.somatic_snvs_custom.ann_snpEff
        155699
        1.069
        0.00M
        Sig_18_Blood
        0.46%
        0.0M
        2.5M
        100.0%
        100.0%
        12.4%
        2.5M
        309.3bp
        100.0%
        100.0%
        100.0%
        100.0%
        99.0%
        195X
        4
        0
        0
        0.00
        0
        0
        0
        Sig_18_Blood.deconflicted_germline
        395
        369
        26
        2.93
        0
        0
        0
        Sig_18_Blood.deconflicted_germline_custom.ann_snpEff
        118253
        2.865
        0.00M
        Sig_18_Blood_custom.ann_snpEff
        193675569
        0.000
        0.00M
        Sig_18_tissue
        0.42%
        0.0M
        12.3M
        100.0%
        99.9%
        12.0%
        12.3M
        239.5bp
        100.0%
        100.0%
        100.0%
        100.0%
        100.0%
        838X
        custom_Sig_18_tumor_normal.manta.diploid_sv
        6
        0
        3
        0.00
        0
        0
        0
        custom_Sig_18_tumor_normal.manta.diploid_sv_custom.ann_snpEff
        7784997
        0.000
        0.00M
        custom_Sig_18_tumor_normal.manta.somatic_sv
        0
        0
        0
        0.00
        0
        0
        0
        custom_Sig_18_tumor_normal.manta.somatic_sv_custom.ann_snpEff
        0
        0.000
        0.00M
        custom_Sig_18_tumor_normal.strelka.somatic_indels
        88
        0
        88
        0.00
        0
        0
        0
        custom_Sig_18_tumor_normal.strelka.somatic_indels_custom.ann_snpEff
        530795
        0.000
        0.00M
        custom_Sig_18_tumor_normal.strelka.somatic_snvs
        237
        237
        0
        1.24
        0
        0
        0
        custom_Sig_18_tumor_normal.strelka.somatic_snvs_custom.ann_snpEff
        197088
        1.236
        0.00M
        Expand table

        altera/sarek Workflow Summary

        Input/output options

        input
        s3://natera-platform-sandbox/pipeline-inputs/test_sarek/end_to_end_regression/samplesheet/workorder.csv
        outdir
        s3://natera-rnd-pltf-dev-s3-gitlab-results/sarek/build2810611/regression-run/63904030

        Main options

        hrd_coverage_profile
        s3://natera-platform-sandbox/pipeline-resources/AIH/hrd_score_altera_bam/dbsnp_baseline_altera.260120.tsv.gz
        intervals_dir
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/intervals/regression_intervals
        intervals_vc_dir
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/intervals/regression_padded_intervals
        mimsi_microsatellites_list
        s3://natera-platform-sandbox/pipeline-resources/mimsi/1500_dropped_panel_with_boosted_msi_regions.tsv
        mimsi_model
        s3://natera-platform-sandbox/pipeline-resources/mimsi/mi_msi_v0_4_0_200x_attn.model
        tools
        ngscheckmate,contamination,tnseq,strelka,manta,msisensor2,mimsi,cnvkit,facets,tmb,hrd,whatshap,snpeff,merge,sentieon_haplotyper_rf,chip_detection,snv_indel,germline_cnv,sex_estimation,somacnv
        wes
        true

        Variant Calling

        chip_cohort_blacklist
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP/cohort_blacklist.bed.gz
        chip_cosmic_heme
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP/cosmic_heme.tsv.gz
        chip_encode_blacklist
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP/encode_blacklist.bed.gz
        chip_gene_family_blacklist
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP/gene_family_blacklist.bed.gz
        chip_gene_tiers
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP/gene_tiers.tsv
        chip_pon
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP_PON/pon.hotspot_protected.raw.vcf.gz
        chip_pon_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CHIP_PON/pon.hotspot_protected.raw.vcf.gz.tbi
        cnvkit_reference
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/cnvkit/cnvkit_wes_altera.reference.cnn
        pon
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/PON/pon_tnseq_42_curated_v4.vcf.gz
        pon_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/PON/pon_tnseq_42_curated_v4.vcf.gz.tbi
        pot
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/POT/aih_tumor_1577_pot_1pct_artifacts_only.vcf.gz
        pot_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/POT/aih_tumor_1577_pot_1pct_artifacts_only.vcf.gz.tbi

        General reference genome options

        igenomes_base
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes/
        optitype_reference
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/optitype/original_v3.15_2014/

        Reference genome options

        blacklist_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/blacklist_grch38.bed.gz
        blacklist_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/blacklist_grch38.bed.gz.tbi
        blacklist_header
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/blacklist_header.txt
        conpair_markers
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/Conpair/GRCh38.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.liftover.bed
        container_registry_seqera
        292967571998.dkr.ecr.us-west-2.amazonaws.com/community.wave.seqera.io
        dbsnp
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/dbsnp_146.hg38.vcf.gz
        dbsnp_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/dbsnp_146.hg38.vcf.gz.tbi
        dbsnp_vqsr
        --resource:dbsnp,known=false,training=true,truth=false,prior=2.0 dbsnp_146.hg38.vcf.gz
        dict
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Sequence/WholeGenomeFasta/Homo_sapiens_assembly38.dict
        exome_bed
        s3://natera-platform-sandbox/pipeline-inputs/test_sarek/end_to_end_regression/bed/xgen-exome-hyb-panel-v2-targets-hg38_short.mrg_chr21.bed
        fasta
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Sequence/WholeGenomeFasta/Homo_sapiens_assembly38.fasta
        fasta_fai
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Sequence/WholeGenomeFasta/Homo_sapiens_assembly38.fasta.fai
        genome_annotations
        s3://natera-platform-sandbox/pipeline-resources/ensembl/Homo_sapiens.GRCh38.110.gtf.gz
        germline_rescue_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/germline_rescue_targets.bed
        germline_resource
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/af-only-gnomad.hg38.vcf.gz
        germline_resource_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/af-only-gnomad.hg38.vcf.gz.tbi
        gt_correction_model
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/gt_correction_model_v2.joblib
        gt_correction_threshold_grch38
        0.5
        lowdepth_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/low_depth_grch38.tsv.gz
        lowdepth_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/low_depth_grch38.tsv.gz.tbi
        lowdepth_header
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/low_depth_header.txt
        ngscheckmate_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/NGSCheckMate/SNP_GRCh38_hg38_wChr.bed
        probe_bed
        s3://natera-platform-sandbox/pipeline-inputs/test_sarek/end_to_end_regression/bed/xgen-exome-hyb-panel-v2_AND_altera_v3_probes_short_hg38_chr21.bed
        repeatmasker_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/repeatmasker_grch38.bed.gz
        repeatmasker_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/repeatmasker_grch38.bed.gz.tbi
        repeatmasker_header
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/CustomBEDs/repeatmasker_header.txt
        rf_blacklist_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_blacklist.bed.gz
        rf_blacklist_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_blacklist.bed.gz.tbi
        rf_boosted_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_boosted_exons.bed.gz
        rf_boosted_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_boosted_exons.bed.gz.tbi
        rf_indel_fp_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/indel_fp_regions.bed.gz
        rf_indel_fp_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/indel_fp_regions.bed.gz.tbi
        rf_indel_fp_rates_tsv
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/indel_locus_fp_rates.tsv
        rf_indel_model
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/indel_rf_model_v7.joblib
        rf_low_depth_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_low_depth.bed.gz
        rf_low_depth_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_low_depth.bed.gz.tbi
        rf_repeatmasker_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_repeatmasker.bed.gz
        rf_repeatmasker_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/rf_repeatmasker.bed.gz.tbi
        rf_snv_fp_bed
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/snv_fp_regions.bed.gz
        rf_snv_fp_bed_tbi
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/snv_fp_regions.bed.gz.tbi
        rf_snv_model
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/igenomes//Homo_sapiens/GATK/GRCh38/Annotation/RF_Models/snv_rf_model_v4.joblib
        snpeff_cache
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/annotation-cache/snpeff_cache/
        snpeff_db
        GRCh38.105
        target_beds
        s3://natera-platform-sandbox/pipeline-inputs/test_sarek/end_to_end_regression/bed/xgen-exome-hyb-panel-v2-targets-hg38_AND_altera_v3_targets_postQC_hg38_chr21.bed,s3://natera-platform-sandbox/pipeline-inputs/test_sarek/end_to_end_regression/bed/altera_v3_targets_coding_postQC_hg38_chr21.bed
        vep_cache
        s3://natera-platform-sandbox/pipeline-resources/ngi-igenomes/annotation-cache/vep_cache/
        vep_cache_version
        113
        vep_genome
        GRCh38
        vep_species
        homo_sapiens

        Institutional config options

        modules_testdata_base_path
        s3://natera-platform-sandbox/pipeline-inputs/test_sarek/

        Generic options

        task_job_queue
        Nextflow-OnDemand

        Core Nextflow options

        configFiles
        N/A
        container
        [GERMLINE_CNV:292967571998.dkr.ecr.us-west-2.amazonaws.com/sarek/altera_cnv:0.5.0, SOMA_CNV_PROBE_COUNTS|SOMA_CNV_ALLELE_COUNTS|SOMA_CNV_NORMALIZE|SOMA_CNV_BUILD_REFERENCE|SOMA_CNV_CALL|SOMA_CNV_EXPORT_VIEWER|SOMA_CNV_BGZIP_TABIX:292967571998.dkr.ecr.us-west-2.amazonaws.com/soma-cnv:20260604-3cb5ec5]
        containerEngine
        docker
        launchDir
        /code
        profile
        docker,test_regression,eks
        projectDir
        /tmp/home/.nextflow/assets/rd-platform/bioinformatics/nextflow/sarek
        runName
        gitlab-sarek-build2810611-regression-run-63904030
        userName
        nextflow
        workDir
        /natera-rnd-pltf-dev-nextflow-scratch-01/work

        Samtools Flagstat

        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

        Mosdepth

        Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.https://github.com/brentp/mosdepthDOI: 10.1093/bioinformatics/btx699

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 4 samples, a BED file was provided, so the data was calculated across those regions. For 4 samples, it's calculated across the entire genome length. 4 samples have both global and region reports, and we are showing the data for regions

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        XY coverage

        Created with MultiQC

        Bcftools

        Utilities for variant calling and manipulating VCFs and BCFs.https://samtools.github.io/bcftoolsDOI: 10.1093/gigascience/giab008

        Variant Substitution Types

        Created with MultiQC

        Variant Quality

        Created with MultiQC

        Indel Distribution

        Created with MultiQC

        Variant depths

        Read depth support distribution for called variants

        Created with MultiQC

        Vcftools

        Program to analyse and reporting on VCF files.https://vcftools.github.ioDOI: 10.1093/bioinformatics/btr330

        TsTv by Count

        Plot of TSTV-BY-COUNT - the transition to transversion ratio as a function of alternative allele count from the output of vcftools TsTv-by-count.

        Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations. Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation. Alternative allele count is the number of alternative alleles at the site. Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.) Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-count

        Created with MultiQC

        TsTv by Qual

        Plot of TSTV-BY-QUAL - the transition to transversion ratio as a function of SNP quality from the output of vcftools TsTv-by-qual.

        Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations. Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation. Quality here is the Phred-scaled quality score as given in the QUAL column of VCF. Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.) Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-qual

        Created with MultiQC

        SNPeff

        Annotates and predicts the effects of variants on genes (such as amino acid changes).http://snpeff.sourceforge.netDOI: 10.4161/fly.19695

        Variants by Genomic Region

        The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

        The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

        Created with MultiQC

        Variant Effects by Impact

        The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

        There are four levels of impacts predicted by SnpEff:

        • High: High impact (like stop codon)
        • Moderate: Middle impact (like same type of amino acid substitution)
        • Low: Low impact (ie silence mutation)
        • Modifier: No impact
        Created with MultiQC

        Variants by Effect Types

        The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

        This plot shows the effect of variants with respect to the mRNA.

        Created with MultiQC

        Variants by Functional Class

        The stacked bar plot shows the effect of variants and the number of variants for each effect type.

        This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

        • Silent: The amino acid does not change.
        • Missense: The amino acid is different.
        • Nonsense: The variant generates a stop codon.
        Created with MultiQC

        Variant Qualities

        The line plot shows the quantity as function of the variant quality score.

        The quality score corresponds to the QUAL column of the VCF file. This score is set by the variant caller.

        Created with MultiQC

        VEP

        Determines the effect of variants on genes, transcripts and protein sequences, as well as regulatory regions.https://www.ensembl.org/info/docs/tools/vep/index.htmlDOI: 10.1186/s13059-016-0974-4

        General Statistics

        Table showing general statistics of VEP annotation run

        Showing 12/12 rows and 7/8 columns.
        Sample NameOverlapped regulatory featuresOverlapped transcriptsOverlapped genesExisting variantsNovel variantsVariants filtered outVariants processedLines of input read
        HCC1395_BL.deconflicted_germline_custom.ann_snpEff_VEP.ann
        16
        201
        193
        354
        0
        0
        354
        354
        HCC1395_BL_custom.ann_snpEff_VEP.ann
        1
        6
        6
        3
        0
        0
        3
        4
        HCC1395_tumor_vs_HCC1395_BL.manta.diploid_sv_custom.ann_snpEff_VEP.ann
        0
        4
        4
        3
        0
        0
        3
        3
        HCC1395_tumor_vs_HCC1395_BL.manta.somatic_sv_custom.ann_snpEff_VEP.ann
        0
        2
        2
        2
        0
        0
        2
        2
        HCC1395_tumor_vs_HCC1395_BL.strelka.somatic_indels_custom.ann_snpEff_VEP.ann
        5
        51
        51
        42
        0
        0
        42
        42
        HCC1395_tumor_vs_HCC1395_BL.strelka.somatic_snvs_custom.ann_snpEff_VEP.ann
        15
        169
        162
        300
        0
        0
        300
        300
        Sig_18_Blood.deconflicted_germline_custom.ann_snpEff_VEP.ann
        16
        199
        192
        395
        0
        0
        395
        395
        Sig_18_Blood_custom.ann_snpEff_VEP.ann
        1
        6
        6
        3
        0
        0
        3
        4
        custom_Sig_18_tumor_normal.manta.diploid_sv_custom.ann_snpEff_VEP.ann
        0
        10
        10
        6
        0
        0
        6
        6
        custom_Sig_18_tumor_normal.manta.somatic_sv_custom.ann_snpEff_VEP.ann
        0
        0
        0
        0
        0
        0
        custom_Sig_18_tumor_normal.strelka.somatic_indels_custom.ann_snpEff_VEP.ann
        6
        81
        81
        88
        0
        0
        88
        88
        custom_Sig_18_tumor_normal.strelka.somatic_snvs_custom.ann_snpEff_VEP.ann
        11
        154
        151
        237
        0
        0
        237
        237
        Expand table

        Variant classes

        Classes of variants found in the data.

        Created with MultiQC

        Consequences

        Predicted consequences of variations.

        Created with MultiQC

        SIFT summary

        SIFT variant effect prediction.

        Created with MultiQC

        PolyPhen summary

        PolyPhen variant effect prediction.

        Created with MultiQC

        Variants by chromosome

        Number of variants found on each chromosome.

        Created with MultiQC

        Position in protein

        Relative position of affected amino acids in protein.

        Created with MultiQC

        Software Versions

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

        GroupSoftwareVersion
        AGGREGATE_VARIANT_QCaggregate_qc_metrics2.0.0
        python3.12.6
        ANNOTATE_BED_INFObcftools1.22
        BCFTOOLS_ANNOTATEbcftools1.2
        BCFTOOLS_CONCATbcftools1.2
        BCFTOOLS_MPILEUPbcftools1.2
        BCFTOOLS_QUERYbcftools1.22
        BCFTOOLS_STATSbcftools1.2
        BGZIPTABIX_GERMLINE_CNVtabix1.2
        CNVKIT_BATCHcnvkit0.9.10
        samtools1.17
        CNVKIT_CALLcnvkit0.9.10
        CNVKIT_EXPORTcnvkit0.9.10
        CNVKIT_GENEMETRICScnvkit0.9.10
        CNV_FACETSfacets0.6.2
        CONCAT_WHATSHAP_TNSEQ_VCFSbcftools1.2
        CONCAT_WHATSHAP_VCFSbcftools1.2
        CONPAIR_CONTAMINATIONconpair1.0
        numpy1.24.4
        python3.8.20
        CONPAIR_PILEUPconpair1.0
        gatk4.6.2.0 Using
        python3.8.20
        DIFF_DV_SENTIEONcyvcf20.31.1
        python3.11.14
        DV_RESCUEdeepvariant1.6.1
        ENSEMBLVEP_VEPensemblvep113.0
        EXTRACT_CHROMOSOMESbcftools1.22
        EXTRACT_CHROMOSOMES_TNSEQbcftools1.22
        GT_CORRECTIONbedtools2.31.1
        cyvcf20.31.1
        lightgbm4.6.0
        python3.11.14
        HARD_FILTER_VCFpython3.9.15
        INDEL_ENTROPY_ANNOTATIONnumpy2.4.0
        pysam0.23.3
        python3.13.11
        scipy1.16.3
        LABEL_HOTSPOTSpython3.9.15
        MANTA_SOMATICmanta1.6.0
        MERGE_FORCECALLEDbcftools1.22
        MERGE_SENTIEON_HAPLOTYPER_VCFSgatk44.5.0.0
        MERGE_STRELKA_INDELSgatk44.5.0.0
        MERGE_STRELKA_SNVSgatk44.5.0.0
        MIMSI_ANALYZEmimsi0.4.5.1
        MSISENSOR2_MSImsisensor20.1
        Mosdepthmosdepth0.3.8
        N7_COSMIC_SIGSn7_cosmic_sigs0.0.1
        r-base4.3.3
        PREP_STRELKA_INDELbcftools1.22
        PREP_TNSEQ_SNVbcftools1.22
        SAMTOOLS_MPILEUPsamtools1.21
        SAMTOOLS_STATSsamtools1.21
        SCARHRDscarHRD0.6.2
        SENTIEON_FORCECALLsentieon202308.03
        SENTIEON_HAPLOTYPERsentieon202308.03
        SENTIEON_HAPLOTYPER_RF_FILTERbedtools2.31.1
        cyvcf20.31.1
        lightgbm4.6.0
        python3.11.14
        SENTIEON_TNFILTERsentieon202308.03
        SENTIEON_TNHAPLOTYPER2sentieon202308.03
        SNPEFF_SNPEFFsnpeff5.1d
        SOMA_CNV_ALLELE_COUNTSsoma-cnv20260604-3cb5ec5
        SOMA_CNV_BGZIP_TABIXhtslib1.21
        SOMA_CNV_CALLsoma-cnv20260604-3cb5ec5
        SOMA_CNV_NORMALIZEsoma-cnv20260604-3cb5ec5
        SOMA_CNV_PROBE_COUNTSsoma-cnv20260604-3cb5ec5
        SPLIT_MULTIALLELICbcftools1.22
        STRELKA_SOMATICstrelka2.9.10
        TABIX_BGZIPTABIXtabix1.2
        TABIX_TABIXtabix1.2
        TABIX_VEPtabix1.2
        TIH_HRD_CALLINGhrd_calling0.0.1
        r-base4.3.3
        TMB_SNV_INDELpython3.9.15
        VARIANT_QC_TO_CSVpython3.12.6
        variant_qc_to_csv1.0.0
        VCFTOOLS_TSTV_COUNTvcftools0.1.16
        WHATSHAPwhatshap2.8
        WHATSHAP_TNSEQwhatshap2.8
        WorkflowNextflow25.10.2
        altera/sarekv3.5.0-g3298dc3

        nf-core/sarek Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.https://github.com/nf-core/sarek

        Methods

        Data was processed using nf-core/sarek v3.5.0 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 v25.10.2 (Di Tommaso et al., 2017) with the following command:

        nextflow run 'https://gitlab.natera.com/rd-platform/bioinformatics/nextflow/sarek.git' 'https://gitlab.natera.com/rd-platform/bioinformatics/nextflow/sarek.git' -name gitlab-sarek-build2810611-regression-run-63904030 -r 3298dc303ca456eca35c00bea397722f955ed993 -profile docker,test_regression,eks --outdir 's3://natera-rnd-pltf-dev-s3-gitlab-results/sarek/build2810611/regression-run/63904030' -output-dir 's3://natera-rnd-pltf-dev-s3-gitlab-results/sarek/build2810611/regression-run/63904030'

        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:
        • If available, make sure to update the text to include the Zenodo DOI of the pipeline version used.
        • 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.

        MultiQC v1.33 - Written by Phil Ewels, available on GitHub.

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