nf-core/viralrecon

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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!

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.

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 calling and consensus sequence generation routes (iVar variants and consensus; default for amplicon data || BCFTools, BEDTools; default for metagenomics data)
      • 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)
   8. Intersect variants across callers (BCFTools)
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)
8. Present QC, visualisation and custom reporting for sequencing, raw reads, alignment and variant calling results (MultiQC)

Quick Start

1. Install Nextflow (>=21.04.0).

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud 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/podman/shifter/charliecloud/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.
   • If you are using singularity then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able 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!

   • Typical command for Illumina shotgun analysis:

     nextflow run nf-core/viralrecon \
         --input samplesheet.csv \
         --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 \
         --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 \
         --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.

     ~/.nextflow/assets/nf-core/viralrecon/bin/fastq_dir_to_samplesheet.py <FASTQ_DIR> samplesheet.csv

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

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)*:

* 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.