wf-artic documentation

By EPI2ME Labs
3 min read

Artic Network SARS-CoV-2 Analysis

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


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:

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

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 nameTypeDescriptionHelpDefault
fastqstringFASTQ 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_unclassifiedbooleanAnalyse 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
basecaller_cfgstringName of the model that was used to basecall signal data, used to select an appropriate Medaka model.The basecaller configuration is used to automatically select the appropriate Medaka model. The automatic selection can be overridden with the ‘medaka_variant_model’ and ‘medaka_consensus_model’ parameters. The model list only shows models that are compatible with this workflow.dna_r9.4.1_450bps_hac

Primer Scheme Selection

Nextflow parameter nameTypeDescriptionHelpDefault
scheme_namestringPrimer 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/V4.1SARS-CoV-2
scheme_versionstringPrimer scheme version.This is the version of the primer scheme to use, more details about primer shemes can be found here.ARTIC/V3
custom_schemestringPath 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 nameTypeDescriptionHelpDefault
sample_sheetstringA 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.
samplestringA 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 nameTypeDescriptionHelpDefault
out_dirstringDirectory for output of all workflow results.output

Reporting Options

Nextflow parameter nameTypeDescriptionHelpDefault
report_depthintegerMin. depth for percentage coverage. (e.g. 89% genome covered at > report_depth)100
report_cladebooleanShow results of Nextclade analysis in report.True
report_coveragebooleanShow genome coverage traces in report.True
report_lineagebooleanShow results of Pangolin analysis in report.True
report_variant_summarybooleanShow variant information in report.True

Advanced Options

Nextflow parameter nameTypeDescriptionHelpDefault
artic_threadsnumberNumber of CPU threads to use per artic task.The total CPU resource used by the workflow is constrained by the executor configuration.4
pangolin_threadsnumberNumber of CPU threads to use per pangolin task.The total CPU resource used by the workflow is constrained by the executor configuration.4
genotype_variantsstringReport genotyping information for scheme’s known variants of interest, optionally provide file path as argument.
list_schemesbooleanList primer schemes and exit without running analysis.False
min_lennumberMinimum read length (default: set by scheme).
max_lennumberMaximum read length (default: set by scheme).
max_softclip_lengthintegerRemove reads with alignments showing large soft clipping
update_databooleanUpdate Pangolin and Nextclade data at runtime.True
pangolin_optionsstringPass options to Pangolin, for example “—analysis-mode fast —min-length 26000”.
nextclade_data_tagstringThe tag of the nextclade data packet
normaliseintegerDepth ceiling for depth of coverage normalisation200
medaka_variant_modelstringThe name of a Medaka variant model to use. This name will override the model automatically chosen based on the provided basecaller configuration.The workflow will attempt to map the basecalling model used to a suitable Medaka variant model. You can override this by providing a model with this option instead.

Miscellaneous Options

Nextflow parameter nameTypeDescriptionHelpDefault
lab_idstringLaboratory identifier, used in reporting.
testkitstringTest kit identifier, used in reporting.
disable_pingbooleanEnable to prevent sending a workflow ping.False


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 }}.

TitleFile pathDescriptionPer sample or aggregated
Workflow report./wf-artic-report.htmlReport for all samples.aggregated
Consensus sequences./all_consensus.fastaFinal consensus sequences for all samples in the analysis.aggregated
Pangolin results./lineage_report.csvPangolin results for each of the samples in the analysis.aggregated
Nextclade results./nextclade.jsonNextclade results for each of the samples in the analysis.aggregated
Coverage data./all_depth.txtCoverage of the reference genome in 20 base windows in all the samples in the analysis.aggregated
Variants./{{ alias }}.pass.named.vcf.gzA VCF file containing high confidence variants in the sample when compared to the reference.per-sample
Variants index./{{ alias }}.pass.named.vcf.gz.tbiAn index file for the variants.per-sample
Alignments./{{ alias }}.primertrimmed.rg.sorted.bamA BAM file containing the reads for the sample aligned to the reference.per-sample
Alignments index./{{ alias }}.primertrimmed.rg.sorted.bam.baiAn 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.


  • 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.


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.

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




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