This workflow provides an easy way to call structural variants in human genomic data.

The pipeline performs the following steps:

• Maps reads using minimap2
• Calls variants using sniffles2
• Filters variants by minimum/maximum length, read support, or type (e.g. insertion, deletion, etc.)
• Optionally evaluates yours calls against a truthset using truvari

The workflow uses nextflow to manage compute and software resources, as such nextflow will need to be installed before attempting to run the workflow.

The workflow can currently be run using either Docker or conda to provide isolation of the required software. Both methods are automated out-of-the-box provided either docker of conda is installed.

It is not required to clone or download the git repository in order to run the workflow. For more information on running EPI2ME Labs workflows visit out website.

Workflow options

To obtain the workflow, having installed nextflow, users can run:

nextflow run epi2me-labs/wf-human-sv --help

to see the options for the workflow.

Download demonstration data

A small test dataset is provided for the purposes of testing the workflow software, it can be downloaded using:

wget -O demo_data.tar.gz \
    https://ont-exd-int-s3-euwst1-epi2me-labs.s3.amazonaws.com/wf-human-sv/demo_data.tar.gz
tar -xzvf demo_data.tar.gz

The workflow can be run with the demonstration data using:

OUTPUT=output
nextflow run epi2me-labs/wf-human-sv \
    -w ${OUTPUT}/workspace \
    -profile standard \
    --fastq demo_data/reads.fastq.gz \
    --reference demo_data/chr20_human_g1k_v37_part.fasta.gz \
    --target_bedfile demo_data/target.bed \
    --tr_bedfile demo_data/human_hs37d5.trf.bed \
    --out_dir ${OUTPUT}

Workflow outputs

The primary outputs of the workflow include:

• A sorted, indexed VCF file containing the SV calls made.
• A sorted, indexed BAM file containing the alignments used to make the calls.
• an HTML report document detailing the primary findings of the workflow.

Workflow tips

• Specifying a suitable tandem repeat BED for your reference with --tr_bedfile can improve the accuracy of SV calling.

• nextflow
• docker
• conda