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.

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 Desktop 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

To update a workflow to the latest version on the command line use the following command:

nextflow pull epi2me-labs/wf-artic

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

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 then be run with the downloaded demo data using:

nextflow run epi2me-labs/wf-artic \
	--fastq 'wf-artic-demo/fastq' \
	--sample_sheet 'wf-artic-demo/sample_sheet.csv' \
	--scheme_name 'SARS-CoV-2' \
	--scheme_version 'Midnight-ONT/V3' \
	-profile standard

For further information about running a workflow on the command 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

Nextflow parameter name	Type	Description	Help	Default
fastq	string	FASTQ files to use in the analysis.	This accepts one of three cases: (i) the path to a single FASTQ file; (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 case, a sample name can be supplied with --sample. In the last case, 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.
analyse_unclassified	boolean	Analyse unclassified reads from input directory. By default the workflow will not process reads in the unclassified directory.	If selected and if the input is a multiplex directory the workflow will also process the unclassified directory.	False

Primer Scheme Selection

Nextflow parameter name	Type	Description	Help	Default
scheme_name	string	Primer scheme name.	This should be set to SARS-CoV-2, or spike-seq or your custom scheme name. This affects the choice of scheme versions you can use. The only scheme versions compatible with spike-seq are ONT/V1 and ONT/V4.1	SARS-CoV-2
scheme_version	string	Primer scheme version.	This is the version of the primer scheme to use, more details about primer shemes can be found here.	ARTIC/V3
custom_scheme	string	Path to a custom scheme.	If you have a custom primer scheme you can enter the details here. This must be the full path to the directory containing your appropriately named scheme bed and fasta files; <SCHEME_NAME>.bed and <SCHEME_NAME>.fasta. More details here.

Sample Options

Nextflow parameter name	Type	Description	Help	Default
sample_sheet	string	A CSV file used to map barcodes to sample aliases. The sample sheet can be provided when the input data is a directory containing sub-directories with FASTQ files.	The sample sheet is a CSV file with, minimally, columns named barcode and alias. Extra columns are allowed. A type column is required for certain workflows and should have the following values; test_sample, positive_control, negative_control, no_template_control.
sample	string	A single sample name for non-multiplexed data. Permissible if passing a single .fastq(.gz) file or directory of .fastq(.gz) files.

Output Options

Nextflow parameter name	Type	Description	Help	Default
out_dir	string	Directory for output of all workflow results.		output

Reporting Options

Nextflow parameter name	Type	Description	Help	Default
report_depth	integer	Min. depth for percentage coverage. (e.g. 89% genome covered at > report_depth)		100
report_clade	boolean	Show results of Nextclade analysis in report.		True
report_coverage	boolean	Show genome coverage traces in report.		True
report_lineage	boolean	Show results of Pangolin analysis in report.		True
report_variant_summary	boolean	Show variant information in report.		True

Advanced Options

Nextflow parameter name	Type	Description	Help	Default
artic_threads	number	Number of CPU threads to use per artic task.	The total CPU resource used by the workflow is constrained by the executor configuration.	4
pangolin_threads	number	Number of CPU threads to use per pangolin task.	The total CPU resource used by the workflow is constrained by the executor configuration.	4
genotype_variants	string	Report genotyping information for scheme's known variants of interest, optionally provide file path as argument.
list_schemes	boolean	List primer schemes and exit without running analysis.		False
min_len	number	Minimum read length (default: set by scheme).
max_len	number	Maximum read length (default: set by scheme).
max_softclip_length	integer	Remove reads with alignments showing large soft clipping
update_data	boolean	Update Pangolin and Nextclade data at runtime.		True
pangolin_options	string	Pass options to Pangolin, for example "--analysis-mode fast --min-length 26000".
nextclade_data_tag	string	The tag of the nextclade data packet
normalise	integer	Depth ceiling for depth of coverage normalisation		200
override_basecaller_cfg	string	Override auto-detected basecaller model that processed the signal data; used to select an appropriate Medaka model.	Per default, the workflow tries to determine the basecall model from the input data. This parameter can be used to override the detected value (or to provide a model name if none was found in the inputs). However, users should only do this if they know for certain which model was used as selecting the wrong option might give sub-optimal results. A list of recent models can be found here: https://github.com/nanoporetech/dorado#DNA-models.

Miscellaneous Options

Nextflow parameter name	Type	Description	Help	Default
lab_id	string	Laboratory identifier, used in reporting.
testkit	string	Test kit identifier, used in reporting.

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 }}.

Title	File path	Description	Per sample or aggregated
Workflow report	./wf-artic-report.html	Report for all samples.	aggregated
Consensus sequences	./all_consensus.fasta	Final consensus sequences for all samples in the analysis.	aggregated
Pangolin results	./lineage_report.csv	Pangolin results for each of the samples in the analysis.	aggregated
Nextclade results	./nextclade.json	Nextclade results for each of the samples in the analysis.	aggregated
Coverage data	./all_depth.txt	Coverage of the reference genome in 20 base windows in all the samples in the analysis.	aggregated
Variants	./{{ alias }}.pass.named.vcf.gz	A VCF file containing high confidence variants in the sample when compared to the reference.	per-sample
Variants index	./{{ alias }}.pass.named.vcf.gz.tbi	An index file for the variants.	per-sample
Alignments	./{{ alias }}.primertrimmed.rg.sorted.bam	A BAM file containing the reads for the sample aligned to the reference.	per-sample
Alignments index	./{{ alias }}.primertrimmed.rg.sorted.bam.bai	An index file for the alignments.	per-sample

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.