viralassembly

A generic viral assembly and QC pipeline which utilises a re-implementation of the artic pipeline to separate out the individual steps allowing greater control on tool versions along with how data is run through the processes. This pipeline can be used as a starting point for analyses on viruses without dedicated workflows already available.

This pipeline is intended to be run on either Nanopore Amplicon Sequencing data or Basic Nanopore NGS Sequencing data that can utilize a reference genome for read mapping, variant calling, and other downstream analyses. It generates variant calls, consensus sequences, and quality control information based on the reference. To do this, there are three different variant callers that can be utilized which includes: clair3, medaka, and nanopolish (For R9.4.1 flowcells and below only). By default, clair3 is used and highly recommended over the other two (as of December 2025). Their inclusion being for completeness with eventual removal from the workflow.

The goals of this pipeline are:

1. Provide a generic viral pipeline for the NML Surviellance Platform IRIDA-Next
2. Provide detailed and useful Run and Sample level final reports
3. Allow the pipeline to be used on other viruses with or without amplicon schemes
   • Due to the QC steps there is unfortunately a current limitation at working with segmented viruses
     • The pipeline will automatically exit after assembly and not generate QC and Reports for these at this time
     • This will hopefully be fully implemented at some point in the future

Index

• Installation
• Base Run Commands
  • Nanopore - Clair3
  • Nanopore - Medaka
  • Nanopore - Nanopolish
• Outputs
• Limitations
• Citations
• Contributing
• Legal

Installation

1. Download and install nextflow

   1. Download and install with conda
      • Conda command: conda create on nextflow -c conda-forge -c bioconda nextflow
   2. Install with the instructions at https://www.nextflow.io/

2. Run the pipeline with one of the following profiles to handle dependencies (or use your own ``-profile` if you have one!):

   • conda
   • mamba
   • singularity
   • docker

Running Commands

Simple commands to run input data. Data can be ingested by the pipeline in three different ways:

1. Passing --fastq_pass </PATH/TO/fastq_pass> where fastq_pass is a directory containing barcode## subdirectories with fastq files
   • This is the format that matches data which has been basecalled with dorado
   • Samples can be renamed by passing in the --metadata parameter with a TSV file mapping the barcode to the sample name
2. Passing --fastq_pass </PATH/TO/fastqs> where fastqs is a directory containing named *.fastq* files
   • Sample names are based off of the file names
3. Passing --input <samplesheet.csv> where samplesheet.csv is a CSV file with two columns
   1. sample - The name of the sample
   2. fastq_1 - Path to one fastq file per sample in .fastq* format

The basic examples will show how to run the pipeline using the --fastq_pass input but it could be subbed in for the --input CSV file if wanted.

All detailed running information is available in the usage docs

Nanopore - Clair3 - Default

Running the pipeline with Clair3 (default and recommended variant caller) requires fastq files and a Clair3 model. When running with --fastq_pass, the pipeline will either:

• Look for subdirectories off of the input "--fastq_pass" directory called barcode## to be used in the pipeline
• Look for fastq files in the input "--fastq_pass" directory called *.fastq* to be used in the pipeline

This pipeline utilizes the same steps as the artic fieldbioinformatics minion pipeline but with each step run using nextflow to allow Clair3 to be easily slotted in. See the Clair3 Section of the usage docs for more information

Basic command:

nextflow run /PATH/TO/artic-generic-nf/main.nf \
    -profile <PROFILE(s)> \
    --fastq_pass </PATH/TO/fastq_pass> \
    --reference <REF.fa> \
    --clair3_model <MODEL> \
    <OPTIONAL INPUTS>

Optional inputs could include:

• Amplicon scheme instead of just a reference fasta file
• Metadata
• Filtering options
• Running SnpEff for variant consequence prediction
• Output reporting options

The pipeline could also be run with --input CSV to pass in an input CSV file with the sample names and fastq pathes

Nanopore - Medaka

Running the pipeline with medaka for variant calling requires fastq files and a medaka model. When running the pipeline will either:

• Look for subdirectories off of the input "--fastq_pass" directory called barcode## to be used in the pipeline
• Look for fastq files in the input "--fastq_pass" directory called *.fastq* to be used in the pipeline

See the medaka section of the usage docs for more information

Basic command:

nextflow run /PATH/TO/artic-generic-nf/main.nf \
    -profile <PROFILE(s)> \
    --variant_caller 'medaka' \
    --fastq_pass </PATH/TO/fastq_pass> \
    --medaka_model <Medaka Model> \
    --reference <REF.fa> \
    <OPTIONAL INPUTS>

Optional inputs could include:

• Amplicon scheme instead of just a reference fasta file
• Metadata
• Filtering options
• Running SnpEff for variant consequence prediction
• Output reporting options

Medaka model information can be found here

Nanopore - Nanopolish

Running the pipeline with nanopolish for variant calls requires fastq files, fast5 files, and the sequencing summary file instead of providing a model. As such, nanopolish requires that the read ids in the fastq files are linked by the sequencing summary file to their signal-level data in the fast5 files. This makes it a lot easier to run using barcoded directories but it can be run with individual read files

See the nanopolish section of the usage docs for more information

Basic command:

nextflow run /PATH/TO/artic-generic-nf/main.nf \
    -profile <PROFILE(s)> \
    --variant_caller 'nanopolish' \
    --fastq_pass </PATH/TO/fastq_pass> \
    --fast5_pass </PATH/TO/fast5_pass> \
    --sequencing_summary </PATH/TO/sequencing_summary.txt> \
    --reference <REF.fa>
    <OPTIONAL INPUTS>

Optional inputs could include:

• Amplicon scheme instead of just a reference fasta file
• Metadata
• Filtering options
• Running SnpEff for variant consequence prediction
• Output reporting options

Outputs

Outputs are separated based off of their tool or file format and found in the results/ directory by default.

Outputs include:

• Consensus fasta files
• VCF files
• Bam files
• HTML summary files (either custom or MultiQC)

More output information on pipeline steps and output files can be found in the output docs

Limitations

Current limitations include:

1. Nanopore data only at this time
2. Currently runs for viruses using a reference genome
   • Segmented viruses will exit before the QC section for now while looking into how to best report them
3. SnpEff issues in running and database building/downloading
   • Database building/downloading requires one of three things:
     • The reference ID is in the SnpEff database
       • This allows the database to be downloaded
     • A gff3 file
       • This is used with the reference sequence to build a database
     • A well annotated NCBI genome matching the reference ID
       • This will pull the genbank file and use that to build a database
   • Running SnpEff with singularity sometimes leads to a lock issue which is hopefully fixed

Citations

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x. In addition, references of tools and data used in this pipeline are as follows:

Detailed citations for utilized tools are found in citations.md

Contributing

Contributions are welcome through creating PRs or Issues

Legal

Copyright 2023 Government of Canada

Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License at:

https://opensource.org/license/mit/

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.