nf-core/viralrecon

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Introduction

nfcore/viralrecon is a bioinformatics analysis pipeline used to perform assembly and intra-host/low-frequency variant calling for viral samples. The pipeline supports short-read Illumina sequencing data from both shotgun (e.g. sequencing directly from clinical samples) and enrichment-based library preparation methods (e.g. amplicon-based: ARTIC SARS-CoV-2 enrichment protocol; or probe-capture-based).

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 that run the pipeline on a full-sized dataset using AWS cloud ensure that the code is stable.

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. Genome-wide and amplicon coverage QC plots (mosdepth)
   7. Choice of multiple variant calling and consensus sequence generation routes (VarScan 2, BCFTools, BEDTools || iVar variants and consensus || BCFTools, BEDTools)
      • Variant annotation (SnpEff, SnpSift)
      • Consensus assessment report (QUAST)
   8. Intersect variants across callers (BCFTools)
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)
      • Blast to reference genome (blastn)
      • Contiguate assembly (ABACAS)
      • Assembly report (PlasmidID)
      • Assembly assessment report (QUAST)
      • Call variants relative to reference (Minimap2, seqwish, vg, Bandage)
      • Variant annotation (SnpEff, SnpSift)
7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)

NB: The pipeline has a number of options to allow you to run only 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 the usage docs for all of the available options when running the pipeline.

Pipeline reporting

Numerous QC and reporting steps are included in the pipeline in order to collate a full summary of the analysis within a single MultiQC report. You can see an example MultiQC report here, generated using the parameters defined in this configuration file. The pipeline was run with these samples, prepared from the ncov-2019 ARTIC Network V1 amplicon set and sequenced on the Illumina MiSeq platform in 301bp paired-end format.

Quick Start

1. Install nextflow

2. Install either Docker or Singularity for full pipeline reproducibility (please only use Conda 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,<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.

4. Start running your own analysis!

   • Typical command for shotgun analysis:

     nextflow run nf-core/viralrecon \
         --input samplesheet.csv \
         --genome 'MN908947.3' \
         -profile <docker/singularity/conda/institute>

   • Typical command for amplicon analysis:

     nextflow run nf-core/viralrecon \
         --input samplesheet.csv \
         --genome 'MN908947.3' \
         --protocol amplicon \
         --amplicon_bed ./nCoV-2019.artic.V3.bed \
         --skip_assembly \
         -profile <docker/singularity/conda/institute>

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

Documentation

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

1. Installation
2. Pipeline configuration
   • Local installation
   • Adding your own system config
   • Reference genomes
3. Running the pipeline
4. Output and how to interpret the results
5. Troubleshooting

Credits

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 #viralrecon channel (you can join with this invite).

Citation

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. ReadCube: Full Access Link