Introduction

This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

Alignment

Mapping

Bwa-mem2

Bwa-mem2 used to map the reads to a reference genome. The aligned reads are coordinate sorted with samtools sort. These files are treated as intermediates and are not placed in the output folder by default.

Sentieon bwa mem

Sentieon’s bwa mem is the software accelerated version of the bwa-mem algorithm. It is used to efficiently perform the alignment using BWA. Aligned reads are then coordinate sorted using Sentieon’s sort utility. These files are treated as intermediates and are not placed in the output folder by default. It is not the default aligner, but it can be chosen over bwamem2 by setting --aligner option to sentieon.

Duplicate marking

Picard’s MarkDuplicates

Picard MarkDuplicates is used for marking PCR duplicates that can occur during library amplification. This is essential as the presence of such duplicates results in false inflated coverages, which in turn can lead to overly-confident genotyping calls during variant calling. Only reads aligned by Bwa-mem2 are processed by this tool.

Output files from Alignment
  • {outputdir}/alignment/
    • *.bam: Bam file containing report containing quality metrics.
    • *.bai: Zip archive containing the FastQC report, tab-delimited data file and plot images.
    • *.txt: Text file containing the dedup metrics.
Sentieon Dedup

Sentieon Dedup is the algorithm used by Sentieon’s driver to remove duplicate reads. Only reads aligned by Sentieon’s implementation of bwa are processed by this algorithm.

Output files from Alignment
  • {outputdir}/alignment/
    • *.bam: Bam file containing report containing quality metrics.
    • *.bai: Zip archive containing the FastQC report, tab-delimited data file and plot images.
    • *.txt: Text file containing the dedup metrics.

Quality control and reporting

Quality control

FastQC

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

Output files
  • {outputdir}/fastqc/{sampleid}_T*/
    • *_fastqc.html: FastQC report containing quality metrics.
    • *_fastqc.zip: Zip archive containing the FastQC report, tab-delimited data file and plot images.
Mosdepth

Mosdepth is used to report quality control metrics such as coverage, and GC content from alignment files. The global distribution file, generated by this program is passed to MultiQC for generating the following plots,

  • Cumulative coverage distribution
  • Coverage distribution
  • Average coverage per contig
Output files
  • {outputdir}/qc_bam/
    • <sampleid>_mosdepth.global.dist.txt: This file contains a cumulative distribution indicating the proportion of total bases that were covered for at least a given coverage value across each chromosome and the whole genome.
    • <sampleid>_mosdepth.per-base.d4: This file contains a coverage for each base in the genome in d4 format.
    • <sampleid>_mosdepth.summary.txt: This file contains summary statistics, such as mean, minimum and maximum coverage per genomic contig.
Picard tools

Picard’s CollectMutipleMetrics, CollectHsMetrics, and CollectWgsMetrics We use Picardtools’ CollectWgsMetrics and CollectHsMetrics utilities to calculate metrics about coverage and performance of WGS & WES experiments. In addition to those metrics, we use CollectMultipleMetrics to gather information about alignment summary, insert size, GC content etc., The metrics generated by these three utilites are passed along to MultiQC to generate several plots as well.

Output files
  • {outputdir}/qc_bam/<sampleid>_qualimap/
    • <sampleid>_hsmetrics.CollectHsMetrics.coverage_metrics:
    • <sampleid>_multiplemetrics.CollectMultipleMetrics.alignment_summary_metrics:
    • <sampleid>_multiplemetrics.CollectMultipleMetrics.base_distribution_by_cycle_metrics:
    • <sampleid>_multiplemetrics.CollectMultipleMetrics.insert_size_metrics:
    • <sampleid>_multiplemetrics.CollectMultipleMetrics.quality_by_cycle_metrics:
    • <sampleid>_multiplemetrics.CollectMultipleMetrics.quality_distribution_metrics:
    • <sampleid>_wgsmetrics.CollectWgsMetrics.coverage_metrics:
    • <sampleid>_wgsmetrics_y.CollectWgsMetrics.coverage_metrics:
Qualimap

Qualimap also allows you to assess the alignment coverage. Qualimap results are used by MultiQC to generate the following plots.

  • Coverage histogram
  • Cumulative genome coverage
  • Insert size histogram
  • GC content distribution
Output files
  • {outputdir}/qc_bam/<sampleid>_qualimap/ this directory includes a qualimap report and associated raw statistic files. You can open the .html file in your internet browser to see the in-depth report.
Sention WgsMetricsAlgo

Sentieon’s WgsMetricsAlgo is the Sentieon’s equivalent of Picard’s CollectWgsMetrics.

Output files
  • {outputdir}/qc_bam/
    • <sampleid>_wgsmetrics.txt:
TIDDIT’s cov and UCSC WigToBigWig

TIDDIT’s cov is used to analyse the read depth of a bam file and generates a coverage report in wig format. This file is later passed to UCSC WigToBigWig to convert the file into a bigwig.

Output files
  • {outputdir}/qc_bam/
    • <sampleid>_tidditcov.wig:
    • <sampleid>.bw:

Reporting

MultiQC

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Output files
  • multiqc/
    • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
    • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
    • multiqc_plots/: directory containing static images from the report in various formats.

Variant calling - SNV

DeepVariant

DeepVariant is a deep learning-based variant caller that takes aligned reads, produces pileup image tensors from them, classifies each tensor using a convolutional neural network and finally reports the results in a standard VCF or gVCF file. Variant calls generated by DeepVariant are joint genotyped using GLnexus, and then normalized using bcftools norm. Only the normalized vcfs are placed in the output folder by default.

NB: In case you are running the separate mitochondrial analysis, mitochondrial calls are filtered from the normalized vcfs before they are published using GATK SelectVariants.

Output files
  • call_snv/
    • <case_id>_split_rmdup.vcf.gz: normalized vcf file containing MT variants. Only published when --skip_mt_analysis is set.
    • <case_id>_split_rmdup.vcf.gz.tbi: index of the normalized vcf file containing MT variants. Only published when --skip_mt_analysis is set.
    • <case_id>_nomito.selectvariants.vcf.gz: normalized vcf file containing no MT variants.
    • <case_id>_nomito.selectvariants.vcf.gz.tbi: index of the vcf file containing no MT variants.

Sentieon DNAscope

The pipeline performs variant calling using Sentieon DNAscope with a machine learning model. This approach identifies the candidate sites with a higher accuracy, and calculates genotypes for each sample at that site. These files are treated as intermediates and are not placed in the output folder by default. DNAscope is not run by default. To use DNAscope instead of DeepVariant, set --variant_caller to sentieon.

Output files
  • call_snv/
    • <case_id>_split_rmdup.vcf.gz: normalized vcf file containing MT variants. Only published when --skip_mt_analysis is set.
    • <case_id>_split_rmdup.vcf.gz.tbi: index of the normalized vcf file containing MT variants. Only published when --skip_mt_analysis is set.
    • <case_id>_nomito.selectvariants.vcf.gz: normalized vcf file containing no MT variants.
    • <case_id>_nomito.selectvariants.vcf.gz.tbi: index of the vcf file containing no MT variants.

Variant calling - SV

Manta

Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It combines paired and split-read evidence during SV discovery and scoring to improve accuracy, but does not require split-reads or successful breakpoint assemblies to report a variant in cases where there is strong evidence otherwise. Output vcf files are treated as intermediates and are not placed in the output folder by default.

TIDDIT sv

TIDDIT’s sv is used to identify chromosomal rearrangements using sequencing data. TIDDIT identifies intra and inter-chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs. TIDDIT searches for discordant reads and split reads (supplementary alignments). Output vcf files are treated as intermediates and are not placed in the output folder by default.

SVDB merge

SVDB merge is used to merge the variant calls from both Manta and TIDDIT. Output files are published in the output folder.

Output files
  • call_sv/
    • <case_id>_sv_merge.vcf.gz: file containing the merged variant calls.
    • <case_id>_sv_merge.vcf.gz.tbi: index of the file containing the merged variant calls.

Variant calling - repeat expansions

Expansion Hunter

Expansion Hunter aims to estimate sizes of repeat sequences by performing a targeted search through alignments that span, flank, and are fully contained in each repeat.

Output files
  • repeat_expansions/
    • <sample_id>_repeat_expansion.vcf.gz: file containing variant calls.
    • <sample_id>_repeat_expansion.vcf.gz.tbi: index of the file containing variant calls.

Stranger

Stranger annotates output files from Expansion Hunter with the pathologic implications of the repeat sizes.

Output files
  • repeat_expansions/
    • <case_id>_repeat_expansion.vcf.gz: file containing variant calls.
    • <case_id>_repeat_expansion.vcf.gz.tbi: index of the file containing variant calls.

Annotation - SNV

bcftools roh

bcftools roh is a program for detecting runs of homo/autozygosity.from only bi-allelic sites. The output files are not published in the output folder by default, and is passed to vcfanno for further annotation.

vcfanno

vcfanno allows you to quickly annotate your VCF with any number of INFO fields from any number of VCFs. It uses a simple conf file to allow the user to specify the source annotation files and fields and how they will be added to the info of the query VCF. Values are pulled by name from the INFO field with special-cases of ID and FILTER to pull from those VCF columns. The output files are not published in the output folder by default, and is passed to vep for further annotation.

VEP

VEP determines the effect of your variants on genes, transcripts, and protein sequence, as well as regulatory regions. We recommend annotating with the following plugins:

  • LoFtool
  • pLI
  • SpliceAI
  • MaxEntScan

Based on VEP annotations, custom scripts used by the pipeline further annotate each record with the most severe consequence, and pli scores.

NB: Output files described below include mitochondrial annotations only if —skip_mt_analysis is set to true.

Output files
  • annotate_snv/
    • <case_id>_rohann_vcfanno_filter_vep.vcf.gz: file containing bcftools roh, vcfanno, and vep annotations.
    • <case_id>_rohann_vcfanno_filter_vep.vcf.gz.tbi: index of the file containing bcftools roh, vcfanno, and vep annotations.
    • <case_id>_vep_csq_pli.vcf.gz: file containing bcftools roh, vcfanno, vep, consequence and pli annotations.
    • <case_id>_vep_csq_pli.vcf.gz.tbi: index of the file containing bcftools roh, vcfanno, vep, consequence and pli annotations.

Annotation - SV

SVDB query

SVDB query allows you to quickly annotate your VCF with data from one or more structural variant databases. The output files are not published in the output folder by default, and is passed to vep for further annotation.

VEP

VEP determines the effect of your variants on genes, transcripts, and protein sequence, as well as regulatory regions. We recommend annotating with pLI plugin, along with any other custom plugins you may want too use. Based on VEP annotations, custom scripts used by the pipeline further annotate each record with the most severe consequence, and pli scores.

Output files
  • annotate_sv/
    • <case_id>_svdbquery_vep.vcf.gz: file containing svdb query, and vep annotations.
    • <case_id>_svdbquery_vep.vcf.gz.tbi: index of the file containing bcftools roh, vcfanno, and vep annotations.
    • <case_id>_vep_csq_pli.vcf.gz: file containing bcftools roh, vcfanno, vep, consequence and pli annotations.
    • <case_id>_vep_csq_pli.vcf.gz.tbi: index of the file containing bcftools roh, vcfanno, vep, consequence and pli annotations.

Mitochondrial analysis

Mitochondrial analysis is run by default, to turn it off set --skip_mt_analysis to true.

Alignment and variant calling

Alignment and variant calling - GATK Mitochondrial short variant discovery pipeline The mitochondrial genome poses several challenges to the identification and understanding of somatic variants. The circularity of the mitochondrial genome means that the breakpoint in the reference genome is at an arbitrary position in the non-coding control region, creating a challenge in analyzing variation. Additionally, insertions of mitochondrial DNA into the nuclear genome (NuMTs) complicate the mapping of the mitochondrial genome and the distinction between NuMTs and the mitochondrial contig of interest. Lastly, mitochondrial variants often have very low heteroplasmy. Such low allele fraction (AF) variants can thus be mistaken for inherent sequencer noise.

The pipeline for mitochondrial variant discovery, using Mutect2, uses a high sensitivity to low AF and separate alignments using opposite genome breakpoints to allow for the tracing of lineages of rare mitochondrial variants.

Annotation:

HaploGrep2

HaploGrep2 allows detecting artificial recombinants and missing variants as well as annotating rare and phantom mutations in mitochondria. Haplogrep generates a text report, which is published by default.

Output files
  • annotate_mt/
    • <case_id>_haplogrep.txt: file containing haplogroup information.
vcfanno

vcfanno allows you to quickly annotate your VCF with any number of INFO fields from any number of VCFs. It uses a simple conf file to allow the user to specify the source annotation files and fields and how they will be added to the info of the query VCF. Values are pulled by name from the INFO field with special-cases of ID and FILTER to pull from those VCF columns. The output files are not published in the output folder by default, and is passed to vep for further annotation.

VEP

VEP determines the effect of your variants on genes, transcripts, and protein sequence, as well as regulatory regions.

Output files
  • annotate_mt/
    • <case_id>_vep_vcfanno_mt.vcf.gz: file containing mitochondrial annotations.
    • <case_id>_vep_vcfanno_mt.vcf.gz.tbi: index of the file containing mitochondrial annotations.

Rank variants and filtering

GENMOD

GENMOD is a simple to use command line tool for annotating and analyzing genomic variations in the VCF file format. GENMOD can annotate genetic patterns of inheritance in vcf

with single or multiple families of arbitrary size. VCF file annotated by GENMOD are further filtered using filter_vep from VEP to separate clinically relevant variants.

Output files
  • rank_and_filter/
    • <case_id>_clinical_snv.ann_filter.vcf.gz: file containing clinically relevant SNVs.
    • <case_id>_clinical_sv.ann_filter.vcf.gz: file containing clinically relevant SVs.
    • <case_id>_ranked_snv.vcf.gz: file containing SNV annotations with their rank scores.
    • <case_id>_ranked_snv.vcf.gz.tbi: file containing SNV annotations with their rank scores.
    • <case_id>_ranked_sv.ann_filter.vcf.gz: file containing SV annotations with their rank scores.
    • <case_id>_ranked_sv.ann_filter.vcf.gz.tbi: file containing SV annotations with their rank scores.

Pipeline information

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.

Output files
  • pipeline_info/
    • Reports generated by Nextflow: execution_report.html, execution_timeline.html, execution_trace.txt and pipeline_dag.dot/pipeline_dag.svg.
    • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_report* files will only be present if the --email / --email_on_fail parameter’s are used when running the pipeline.
    • Reformatted samplesheet files used as input to the pipeline: samplesheet.valid.csv.