Introduction

nf-core/viralrecon is a bioinformatics analysis pipeline used to perform assembly and intra-host/low-frequency variant calling for viral samples. The pipeline supports both Illumina and Nanopore sequencing data. For Illumina short-reads the pipeline is able to analyse metagenomics data typically obtained from shotgun sequencing (e.g. directly from clinical samples) and enrichment-based library preparation methods (e.g. amplicon-based: ARTIC SARS-CoV-2 enrichment protocol; or probe-capture-based). For Nanopore data the pipeline only supports amplicon-based analysis obtained from primer sets created and maintained by the ARTIC Network.

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from running the full-sized tests individually for each --platform option can be viewed on the nf-core website and the output directories will be named accordingly i.e. platform_illumina/ and platform_nanopore/.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

Low-frequency variant calling

This pipeline includes support for detecting low-frequency variants, which can be important for studying viral evolution, identifying minor variants in mixed infections, or detecting emerging mutations in a viral population. To enable low-frequency variant calling using iVar, you can use the following parameters:

• --min_allele_frequency - Sets the minimum allele frequency threshold for variant calling with iVar (default: 0.25). Lower values such as 0.0025 (0.25%) allow detection of very rare variants.
• --min_alt_dp - Minimum number of reads supporting the alternative allele (default: 5). This helps filter out sequencing errors.
• --filter_indels - Whether to remove indels from iVar variant calls (default: true) and focus only on SNPs.

Example command for low-frequency variant detection:

nextflow run viralrecon -profile docker \
--input samplesheet.csv \
--outdir Output1 \
--platform illumina \
--protocol metagenomic \
--genome NC_063383.1 \
--variant_caller ivar \
--min_allele_frequency 0.0025 \
--min_alt_dp 5 \
--filter_indels 

APOBEC3 variant analysis

The pipeline also includes support for APOBEC3-specific variant analysis. APOBEC3 is a family of cytidine deaminases that can induce G-to-A and C-to-T mutations in viral genomes. These specific mutation signatures can provide insights into host-pathogen interactions and viral evolution.

Two types of APOBEC3 analysis are supported:

1. APOBEC3 summary statistics: The pipeline generates a report with mutation counts, percentages of APOBEC3-type mutations, and APM (APOBEC3 Mutations Per Million reads) metrics.

2. APOBEC3 variant extraction: The pipeline automatically extracts all APOBEC3-type mutations (G-to-A and C-to-T) from variant calling results into separate TSV files. This makes it easier to focus specifically on these mutations without having to filter through larger variant files. These extracted variants are available in the variants/ivar/apobec3/ directory of the pipeline output.

The same parameters used for low-frequency variant calling (--min_allele_frequency and --min_alt_dp) are also applied to APOBEC3 variant filtering, allowing you to focus on mutations above specific frequency and depth thresholds.

Pipeline summary

The pipeline has numerous options to allow you to run only specific aspects of the workflow if you so wish. For example, for Illumina data you can skip the host read filtering step with Kraken 2 with --skip_kraken2 or you can skip all of the assembly steps with the --skip_assembly parameter. See the usage and parameter docs for all of the available options when running the pipeline.

The SRA download functionality has been removed from the pipeline (>=2.1) and ported to an independent workflow called nf-core/fetchngs. You can provide --nf_core_pipeline viralrecon when running nf-core/fetchngs to download and auto-create a samplesheet containing publicly available samples that can be accepted directly by the Illumina processing mode of nf-core/viralrecon.

A number of improvements were made to the pipeline recently, mainly with regard to the variant calling. Please see Major updates in v2.3 for a more detailed description.

Illumina

1. Merge re-sequenced FastQ files (cat)
2. Read QC (FastQC)
3. Adapter trimming (fastp)
4. Removal of host reads (Kraken 2; optional)
5. Variant calling
   1. Read alignment (Bowtie 2)
   2. Sort and index alignments (SAMtools)
   3. Primer sequence removal (iVar; amplicon data only)
   4. Duplicate read marking (picard; optional)
   5. Alignment-level QC (picard, SAMtools)
   6. Genome-wide and amplicon coverage QC plots (mosdepth)
   7. Choice of multiple variant callers (iVar variants; default for amplicon data || BCFTools; default for metagenomics data)
      • Variant annotation (SnpEff, SnpSift)
      • Individual variant screenshots with annotation tracks (ASCIIGenome)
   8. Choice of multiple consensus callers (BCFTools, BEDTools; default for both amplicon and metagenomics data || iVar consensus)
      • Consensus assessment report (QUAST)
      • Lineage analysis (Pangolin)
      • Clade assignment, mutation calling and sequence quality checks (Nextclade)
   9. Create variants long format table collating per-sample information for individual variants (BCFTools), functional effect prediction (SnpSift) and lineage analysis (Pangolin)
6. De novo assembly
   1. Primer trimming (Cutadapt; amplicon data only)
   2. Choice of multiple assembly tools (SPAdes || Unicycler || minia)
      • Blast to reference genome (blastn)
      • Contiguate assembly (ABACAS)
      • Assembly report (PlasmidID)
      • Assembly assessment report (QUAST)
7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)

Nanopore

1. Sequencing QC (pycoQC)
2. Aggregate pre-demultiplexed reads from MinKNOW/Guppy (artic guppyplex)
3. Read QC (NanoPlot)
4. Align reads, call variants and generate consensus sequence (artic minion)
5. Remove unmapped reads and obtain alignment metrics (SAMtools)
6. Genome-wide and amplicon coverage QC plots (mosdepth)
7. Downstream variant analysis:
   • Count metrics (BCFTools)
   • Variant annotation (SnpEff, SnpSift)
   • Consensus assessment report (QUAST)
   • Lineage analysis (Pangolin)
   • Clade assignment, mutation calling and sequence quality checks (Nextclade)
   • Individual variant screenshots with annotation tracks (ASCIIGenome)
   • Create variants long format table collating per-sample information for individual variants (BCFTools), functional effect prediction (SnpSift) and lineage analysis (Pangolin)
8. Present QC, visualisation and custom reporting for sequencing, raw reads, alignment and variant calling results (MultiQC)

Quick Start

1. Install Nextflow (>=22.10.1)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/viralrecon -profile test,YOURPROFILE --outdir <OUTDIR>

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   • Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

   • Typical command for Illumina shotgun analysis:

     nextflow run nf-core/viralrecon \
         --input samplesheet.csv \
         --outdir <OUTDIR> \
         --platform illumina \
         --protocol metagenomic \
         --genome 'MN908947.3' \
         -profile <docker/singularity/podman/conda/institute>

   • Typical command for Illumina amplicon analysis:

     nextflow run nf-core/viralrecon \
         --input samplesheet.csv \
         --outdir <OUTDIR> \
         --platform illumina \
         --protocol amplicon \
         --genome 'MN908947.3' \
         --primer_set artic \
         --primer_set_version 3 \
         --skip_assembly \
         -profile <docker/singularity/podman/conda/institute>

   • Typical command for Nanopore amplicon analysis:

     nextflow run nf-core/viralrecon \
         --input samplesheet.csv \
         --outdir <OUTDIR> \
         --platform nanopore \
         --genome 'MN908947.3' \
         --primer_set_version 3 \
         --fastq_dir fastq_pass/ \
         --fast5_dir fast5_pass/ \
         --sequencing_summary sequencing_summary.txt \
         -profile <docker/singularity/podman/conda/institute>

   • An executable Python script called fastq_dir_to_samplesheet.py has been provided if you are using --platform illumina and would like to auto-create an input samplesheet based on a directory containing FastQ files before you run the pipeline (requires Python 3 installed locally) e.g.

     wget -L https://raw.githubusercontent.com/nf-core/viralrecon/master/bin/fastq_dir_to_samplesheet.py
     ./fastq_dir_to_samplesheet.py <FASTQ_DIR> samplesheet.csv

   • You can find the default keys used to specify --genome in the genomes config file. This provides default options for

     • Reference genomes (including SARS-CoV-2)

     • Genome associates primer sets

     • Nextclade datasets

       The Pangolin and Nextclade lineage and clade definitions change regularly as new SARS-CoV-2 lineages are discovered. For instructions to use more recent versions of lineage analysis tools like Pangolin and Nextclade please refer to the updating containers section in the usage docs.

     Where possible we are trying to collate links and settings for standard primer sets to make it easier to run the pipeline with standard keys; see usage docs.

Documentation

The nf-core/viralrecon pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

These scripts were originally written by Sarai Varona, Miguel Juliá, Erika Kvalem and Sara Monzon from BU-ISCIII and co-ordinated by Isabel Cuesta for the Institute of Health Carlos III, Spain. Through collaboration with the nf-core community the pipeline has now been updated substantially to include additional processing steps, to standardise inputs/outputs and to improve pipeline reporting; implemented and maintained primarily by Harshil Patel (@drpatelh) from Seqera Labs, Spain.

The key steps in the Nanopore implementation of the pipeline are carried out using the ARTIC Network's field bioinformatics pipeline and were inspired by the amazing work carried out by contributors to the connor-lab/ncov2019-artic-nf pipeline originally written by Matt Bull for use by the COG-UK project. Thank you for all of your incredible efforts during this pandemic!

Many thanks to others who have helped out and contributed along the way too, including (but not limited to)*:

Name	Affiliation
Aengus Stewart	The Francis Crick Institute, UK
Alexander Peltzer	Boehringer Ingelheim, Germany
Alison Meynert	University of Edinburgh, Scotland
Anthony Underwood	Centre for Genomic Pathogen Surveillance
Anton Korobeynikov	Saint Petersburg State University, Russia
Artem Babaian	University of British Columbia, Canada
Dmitry Meleshko	Saint Petersburg State University, Russia
Edgar Garriga Nogales	Centre for Genomic Regulation, Spain
Erik Garrison	UCSC, USA
Gisela Gabernet	QBiC, University of Tübingen, Germany
Joao Curado	Flomics Biotech, Spain
Jerome Nicod	The Francis Crick Institute, UK
Jose Espinosa-Carrasco	Centre for Genomic Regulation, Spain
Katrin Sameith	DRESDEN-concept Genome Center, Germany
Kevin Menden	QBiC, University of Tübingen, Germany
Lluc Cabus	Flomics Biotech, Spain
Marta Pozuelo	Flomics Biotech, Spain
Maxime Garcia	Seqera Labs, Spain
Michael Heuer	UC Berkeley, USA
Phil Ewels	SciLifeLab, Sweden
Richard Mitter	The Francis Crick Institute, UK
Robert Goldstone	The Francis Crick Institute, UK
Simon Heumos	QBiC, University of Tübingen, Germany
Stephen Kelly	Memorial Sloan Kettering Cancer Center, USA
Thanh Le Viet	Quadram Institute, UK

* Listed in alphabetical order

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #viralrecon channel (you can join with this invite).

Citations

If you use nf-core/viralrecon for your analysis, please cite it using the following doi: 10.5281/zenodo.3901628

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

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.

Pipeline parameters

Parameter	Description
--mask	Optional. Specify which masking file to use for filtering variants from repetitive regions. Accepts either 'cladeI' or 'cladeII'. When specified, variants falling within the regions defined in the corresponding masking file will be filtered out.

Masking Files

The pipeline includes masking files for filtering out variants in repetitive regions:

• assets/mask_files/to_mask.cladei.csv: Contains regions to mask for Clade I
• assets/mask_files/to_mask.cladeii.csv: Contains regions to mask for Clade II

These files contain the following columns:

• Minimum: Start position of the region to mask
• Maximum: End position of the region to mask

When using the --mask parameter with either 'cladeI' or 'cladeII', variants falling within these regions will be filtered out before the final output.