nfcore/viralrecon is a bioinformatics analysis pipeline used to perform assembly and intrahost/low-frequency variant calling for viral samples. The pipeline currently supports metagenomics and amplicon sequencing data derived from the Illumina sequencing platform.

This pipeline is a re-implementation of the SARS_Cov2_consensus-nf and SARS_Cov2_assembly-nf pipelines initially developed by Sarai Varona and Sara Monzon from BU-ISCIII. Porting both of these pipelines to nf-core was an international collaboration between numerous contributors and developers, led by Harshil Patel from the The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London. We appreciated the need to have a portable, reproducible and scalable pipeline for the analysis of COVID-19 sequencing samples and so the Avengers Assembled! Please come and join us and add yourself to the contributor list :)

We have integrated a number of options in the pipeline to allow you to run specific aspects of the workflow if you so wish. For example, you can skip all of the assembly steps with the --skip_assembly parameter. See usage docs for all of the available options when running the pipeline.

Please click here to see an example MultiQC report generated using the parameters defined in this configuration file to run the pipeline on samples which were prepared from the ncov-2019 ARTIC Network V1 amplicon set and sequenced on the Illumina MiSeq platform in 301bp paired-end format.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. Furthermore, automated continuous integration tests to run the pipeline on a full-sized dataset are passing on AWS cloud.

Pipeline summary

  1. Download samples via SRA, ENA or GEO ids (ENA FTP, parallel-fastq-dump; if required)
  2. Merge re-sequenced FastQ files (cat; if required)
  3. Read QC (FastQC)
  4. Adapter trimming (fastp)
  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; removal optional)
    5. Alignment-level QC (picard, SAMtools)
    6. Choice of multiple variant calling and consensus sequence generation routes (VarScan 2, BCFTools, BEDTools || iVar variants and consensus || BCFTools, BEDTools)
  6. De novo assembly
    1. Primer trimming (Cutadapt; amplicon data only)
    2. Removal of host reads (Kraken 2)
    3. Choice of multiple assembly tools (SPAdes || metaSPAdes || Unicycler || minia)
  7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)

Quick Start

i. Install nextflow

ii. Install either Docker or Singularity for full pipeline reproducibility (please only use Conda as a last resort; see docs)

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

nextflow run nf-core/viralrecon -profile test,<docker/singularity/conda/institute>

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.

iv. Start running your own analysis!

nextflow run nf-core/viralrecon -profile <docker/singularity/conda/institute> --input samplesheet.csv --genome 'NC_045512.2' -profile docker

See usage docs for all of the available options when running the pipeline.


The nf-core/viralrecon pipeline comes with documentation about the pipeline, found in the docs/ directory:

  1. Installation
  2. Pipeline configuration
  3. Running the pipeline
  4. Output and how to interpret the results
  5. Troubleshooting


These scripts were originally written by Sarai Varona, Miguel Juliá 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 primarily by Harshil Patel from The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London.

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

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 Slack (you can join with this invite).


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

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. ReadCube: Full Access Link

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