MultiQC Report

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Note: 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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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.

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.

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Report generated on 2026-06-04, 17:08 UTC based on data in: /tmp/nxf.6RJhfc3liy

General Statistics

Showing ⁴/₄ rows and ⁸/₁₄ columns.

Sample Name	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Mean insert	≥ 1X	≥ 5X	≥ 10X	≥ 30X	≥ 50X	Median
HCC1395_BL	0.36%	0.0M	1.3M	100.0%	100.0%	7.9%	1.3M	283.6bp	100.0%	100.0%	100.0%	100.0%	99.0%	149X
HCC1395_tumor	0.36%	0.0M	3.6M	100.0%	100.0%	7.7%	3.6M	273.5bp	100.0%	100.0%	100.0%	100.0%	100.0%	334X
Sig_18_Blood	0.38%	0.0M	1.7M	100.0%	100.0%	7.4%	1.7M	291.0bp	100.0%	100.0%	100.0%	100.0%	99.0%	195X
Sig_18_tissue	0.36%	0.0M	9.1M	100.0%	100.0%	6.9%	9.1M	230.1bp	100.0%	100.0%	100.0%	100.0%	100.0%	839X

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	Samtools Flagstat: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_flagstat_stats-error_rate`
\|\|	Samtools Flagstat: stats	Non-primary	Non-primary alignments (millions)	`samtools_flagstat_stats-non_primary_alignments`	read_count
\|\|	Samtools Flagstat: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_flagstat_stats-reads_mapped`	read_count
\|\|	Samtools Flagstat: stats	% Mapped	% Mapped reads	`samtools_flagstat_stats-reads_mapped_percent`
\|\|	Samtools Flagstat: stats	% Proper pairs	% Properly paired reads	`samtools_flagstat_stats-reads_properly_paired_percent`
\|\|	Samtools Flagstat: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_flagstat_stats-reads_MQ0_percent`
\|\|	Samtools Flagstat: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_flagstat_stats-raw_total_sequences`	read_count
\|\|	Samtools Flagstat: stats	Mean insert	Average insert size	`samtools_flagstat_stats-insert_size_average`
\|\|	Mosdepth	≥ 1X	Fraction of genome with at least 1X coverage	`mosdepth-1_x_pc`
\|\|	Mosdepth	≥ 5X	Fraction of genome with at least 5X coverage	`mosdepth-5_x_pc`
\|\|	Mosdepth	≥ 10X	Fraction of genome with at least 10X coverage	`mosdepth-10_x_pc`
\|\|	Mosdepth	≥ 30X	Fraction of genome with at least 30X coverage	`mosdepth-30_x_pc`
\|\|	Mosdepth	≥ 50X	Fraction of genome with at least 50X coverage	`mosdepth-50_x_pc`
\|\|	Mosdepth	Median	Median coverage	`mosdepth-median_coverage`

altera/sarek Workflow Summary

this information is collected when the pipeline is started.https://github.com/altera/sarek

Input/output options

input: s3://natera-platform-sandbox/pipeline-inputs/test_sarek/end_to_end_regression/samplesheet/workorder.csv
outdir: s3://ai-pipelines-experiment/chnobles/nf_sarek_regression_somacnv_v5/results

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: 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:292967571998.dkr.ecr.us-west-2.amazonaws.com/soma-cnv:20260604-914d02b]
launchDir: /code
profile: test_regression,eks
projectDir: /code
runName: sarek-regression-somacnv-v5
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

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
HCC1395_BL	1.3M	1.3M	1.3M	0.3M	0.0M	0.1M	189.7Mb	0.0Mb	43.6Mb	0.0M	0.0M	0.6M	0.1M
HCC1395_tumor	3.6M	3.6M	3.6M	1.5M	0.0M	0.3M	517.0Mb	0.0Mb	228.9Mb	0.0M	0.0M	1.6M	0.2M
Sig_18_Blood	1.7M	1.7M	1.7M	0.3M	0.0M	0.1M	242.9Mb	0.0Mb	38.8Mb	0.0M	0.0M	0.8M	0.1M
Sig_18_tissue	9.1M	9.1M	9.1M	3.1M	0.0M	0.6M	1280.4Mb	0.0Mb	466.9Mb	0.0M	0.0M	3.8M	0.7M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table

Sort	Column	Description	ID	Scale
\|\|	Total sequences	Total sequences	`raw_total_sequences`	read_count
\|\|	Mapped & paired	Paired-end technology bit set + both mates mapped	`reads_mapped_and_paired`	read_count
\|\|	Properly paired	Proper-pair bit set	`reads_properly_paired`	read_count
\|\|	Duplicated	PCR or optical duplicate bit set	`reads_duplicated`	read_count
\|\|	QC Failed	QC Failed	`reads_QC_failed`	read_count
\|\|	Reads MQ0	Reads mapped and MQ=0	`reads_MQ0`	read_count
\|\|	Mapped bases (CIGAR)	Mapped bases (CIGAR)	`bases_mapped__cigar`	base_count
\|\|	Bases Trimmed	Bases Trimmed	`bases_trimmed`	base_count
\|\|	Duplicated bases	Duplicated bases	`bases_duplicated`	base_count
\|\|	Diff chromosomes	Pairs on different chromosomes	`pairs_on_different_chromosomes`	read_count
\|\|	Other orientation	Pairs with other orientation	`pairs_with_other_orientation`	read_count
\|\|	Inward pairs	Inward oriented pairs	`inward_oriented_pairs`	read_count
\|\|	Outward pairs	Outward oriented pairs	`outward_oriented_pairs`	read_count

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

Software Versions

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

Group	Software	Version
AGGREGATE_VARIANT_QC	aggregate_qc_metrics	`2.0.0`
	python	`3.12.6`
Mosdepth	mosdepth	`0.3.8`
SAMTOOLS_STATS	samtools	`1.21`
SOMA_CNV_ALLELE_COUNTS	soma-cnv	`20260604-914d02b`
SOMA_CNV_CALL	soma-cnv	`20260604-914d02b`
SOMA_CNV_NORMALIZE	soma-cnv	`20260604-914d02b`
SOMA_CNV_PROBE_COUNTS	soma-cnv	`20260604-914d02b`
VARIANT_QC_TO_CSV	python	`3.12.6`
	variant_qc_to_csv	`1.0.0`
Workflow	Nextflow	`25.10.2`
	altera/sarek	`v3.5.0`

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 /code/main.nf -output-dir 's3://ai-pipelines-experiment/chnobles/nf_sarek_regression_somacnv_v5/results' -profile test_regression,eks --tools somacnv --outdir 's3://ai-pipelines-experiment/chnobles/nf_sarek_regression_somacnv_v5/results' -name sarek-regression-somacnv-v5

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.

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