Artic Network SARS-CoV-2 Analysis

Run the ARTIC SARS-CoV-2 methodology on multiplexed MinION, GridION, and PromethION data.

Introduction

The wf-artic workflow implements a slightly modified ARTIC FieldBioinformatics workflow for the purpose of preparing consensus sequences from SARS-CoV-2 genomes that have been DNA sequenced using a pooled tiling amplicon strategy.

The workflow consumes a folder containing demultiplexed sequence reads as prepared by either MinKNOW or Guppy. The workflow needs to know the primer scheme that has been used during genome amplification and library preparation e.g. ARTIC/V3 or ONT_Midnight/V1. Other parameters can be specified too e.g. assign sample names to the barcodes or to adjust the length distribution of acceptable amplicon sequences. The Medaka variant model is selected based on the provided basecaller configuration (using the parameter --basecaller_cfg), or alternatively the Medaka model can be provided directly via the --medaka_variant_model parameter.

Compute requirements

Recommended requirements:

• CPUs = 4
• Memory = 8GB

Minimum requirements:

• CPUs = 2
• Memory = 4GB

Approximate run time: 5 minutes per sample

ARM processor support: False

Install and run

These are instructions to install and run the workflow on command line. You can also access the workflow via the EPI2ME application.

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

The workflow can currently be run using either Docker or singularity to provide isolation of the required software. Both methods are automated out-of-the-box provided either docker or singularity is installed. This is controlled by the -profile parameter as exemplified below.

It is not required to clone or download the git repository in order to run the workflow. More information on running EPI2ME workflows can be found on our website.

The following command can be used to obtain the workflow. This will pull the repository in to the assets folder of nextflow and provide a list of all parameters available for the workflow as well as an example command:

nextflow run epi2me-labs/wf-artic -–help

A demo dataset is provided for testing of the workflow. It can be downloaded using:

wget https://ont-exd-int-s3-euwst1-epi2me-labs.s3.amazonaws.com/wf-artic/wf-artic-demo.tar.gz
tar -xzvf wf-artic-demo.tar.gz

The workflow can be run with the demo data using:

nextflow run epi2me-labs/wf-artic \ 
--fastq test_data/reads.fastq.gz \
-profile standard

For further information about running a workflow on the cmd line see https://labs.epi2me.io/wfquickstart/

Related protocols

This workflow is designed to take input sequences that have been produced from Oxford Nanopore Technologies devices.

The Midnight protocol for sample preparation and sequencing can be found in the Nanopore community.

Input example

This workflow accepts FASTQ files as input.

The FASTQ input parameters for this workflow accept one of three cases: (i) the path to a single FASTQ; (ii) the path to a top-level directory containing FASTQ files; (iii) the path to a directory containing one level of sub-directories which in turn contain FASTQ files. In the first and second cases (i and ii), a sample name can be supplied with --sample. In the last case (iii), the data is assumed to be multiplexed with the names of the sub-directories as barcodes. In this case, a sample sheet can be provided with --sample_sheet.

(i)                     (ii)                 (iii)    
input_reads.fastq   ─── input_directory  ─── input_directory
                        ├── reads0.fastq     ├── barcode01
                        └── reads1.fastq     │   ├── reads0.fastq
                                             │   └── reads1.fastq
                                             ├── barcode02
                                             │   ├── reads0.fastq
                                             │   ├── reads1.fastq
                                             │   └── reads2.fastq
                                             └── barcode03
                                              └── reads0.fastq

Input parameters

Input Options

Primer Scheme Selection

Sample Options

Output Options

Reporting Options

Advanced Options

Miscellaneous Options

Outputs

Output files may be aggregated including information for all samples or provided per sample. Per-sample files will be prefixed with respective aliases and represented below as {{ alias }}.

Pipeline overview

The pipeline is largely a wrapper around the Artic Network Field Bioinformatics analysis package.

1. Concatenates input files and generate per read stats.

The fastcat/bamstats tool is used to concatenate multifile samples to be processed by the workflow. It will also output per read stats including average read lengths and qualities. Reads are additionally filtered for sequence length and quality characteristics.

2. Mapping and primer trimming (Artic)

Concatenated reads are mapped to the reference SARS-CoV-2 genome using minimap2. A primer scheme-specific BED file is used to identify the regions of the mapped sequences that correspond to synthetic sequences (primers) - these regions are clipped to ensure that sequences are entirely of biological origin.

3. Variant calling and consensus generation (Artic)

The retained sequences are used to prepare a consensus sequence that is then polished using Medaka and variant calling is performed to produce a VCF file of genetic differences relative to the reference genome.

4. Lineage/clade assignment

The consensus sequence is annotated for virus clade information using NextClade, and strain assignment is performed using Pangolin.

Troubleshooting

• If the workflow fails please run it with the demo data set to ensure the workflow itself is working. This will help us determine if the issue is related to the environment, input parameters or a bug.
• See how to interpret some common nextflow exit codes here.

FAQ’s

If your question is not answered here, please report any issues or suggestions on the github issues page or start a discussion on the community.

Related blog posts

• SARS-CoV-2 Midnight Analysis
• Midnight Scheme Update

See the EPI2ME website for lots of other resources and blog posts.