Artic Network MPXV Analysis

Run the ARTIC fieldbioinformatics workflow on multiplexed MPXV ONT data

Introduction

The artic-mpxv-nf workflow implements an ARTIC FieldBioinformatics workflow for the purpose of preparing consensus sequences from MPXV 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, Guppy, or Dorado. The workflow needs to know the primer scheme that has been used during genome amplification and library preparation e.g. yale-mpox/v1.0.1 or erasmus/v1.0.0. Other parameters can be specified too e.g. assign sample names to the barcodes or to adjust the length distribution of acceptable amplicon sequences.

Credits / Acknowledgements

This pipeline only works due to the ongoing efforts of many people performing the often thankless job of developing and maintaining bioinformatics software, including but not limited to:

• Minimap2 - Heng Li et al, citation: Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100. doi:10.1093/bioinformatics/bty191
• Samtools - Heng Li et al, citation: Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li. GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008
• Bcftools - Heng Li et al, citation: Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li. GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008
• Bwa - Heng Li, et al, citation: Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997v2 [q-bio.GN]. (if you use the BWA-MEM algorithm or the fastmap command, or want to cite the whole BWA package)
• Muscle - Robert Edgar, et al, citation: Edgar RC., Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny. Nature Communications 13.1 (2022): 6968.
• Longshot - Peter Edge et al, citation: Edge, P. and Bansal, V., 2019. Longshot enables accurate variant calling in diploid genomes from single-molecule long read sequencing. Nature communications, 10(1), pp.1-10.
• cyvcf2 - Brent Pederson et al, citation: Brent S Pedersen, Aaron R Quinlan, cyvcf2: fast, flexible variant analysis with Python, Bioinformatics, Volume 33, Issue 12, June 2017, Pages 1867–1869, https://doi.org/10.1093/bioinformatics/btx057
• Pysam - Anreas Heger et al, https://github.com/pysam-developers/pysam
• Clair3 - Zhenxian Zheng et al, citation: Zheng, Z., Li, S., Su, J. et al. Symphonizing pileup and full-alignment for deep learning-based long-read variant calling. Nat Comput Sci 2, 797–803 (2022). https://doi.org/10.1038/s43588-022-00387-x
• Medaka - Oxford Nanopore Technologies, Chris Wright et al, https://github.com/nanoporetech/medaka

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](https://www.docker.com/products/docker-desktop 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 artic-network/artic-mpxv-nf --help

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

nextflow pull artic-network/artic-mpxv-nf

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	MPXV
scheme_version	string	Primer scheme version.	This is the version of the primer scheme to use.	artic-inrb-mpox/v1.0.0-cladeib
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.

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

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
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).
normalise	integer	Depth ceiling for depth of coverage normalisation		200
override_model	string	Override auto-detected clair3 model. 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 models is available here.