¿Prefieres el español? Puedes encontrar el README en español aquí.

nCoV-2019 Analysis Pipeline (Oxford Nanopore)

Nextflow pipeline for the assembly and analysis of SARS-CoV-2 genomes. Uses the ARTIC bioinformatic protocol, thus, using their sequencing protocol is a prerequisite (more info).

Developed to automate the bioinformatics analysis related to the virus genomic surveillance initiative promoted by the Chilean government and executed by various teams in the country, including us at the University of Magallanes in the city of Punta Arenas. More information about us in catg.cl (link in spanish).

Currently includes:

• Assembly and variant calling using ARTIC
• Variant annotation using SnpEff
• Identification of clades/lineages of Pangolin and Nexstrain (including common variant name)
• Quality metrics and statistics (qc, coverage)
• Generation of final summary in Excel format containing relevant information obtained by the pipeline
• Initial preparation for GISAID submission

Quick Start

1. Install Nextflow and Apptainer (or Docker if you prefer that).

2. Create a CSV file with the columns sample and fastq_file. For this, a single FASTQ file is needed for each sample. If you plan to submit your results to GISAID, it is recommended to have a third column: gisaid_covv_virus_name with the names to use for each sample in the submission (the format must be hCoV-19/<Country>/<ID>/<Year>).

3. Execute the pipeline. Examples: Using Apptainer:

   nextflow run catg-umag/ncov2019-ont-nf -r v2.0.0 -profile apptainer \
       --sample_data <csv_file>

   Using Nanopolish instead of Medaka, with Docker:

   nextflow run catg-umag/ncov2019-ont-nf -r v2.0.0 -profile docker \
       --sample_data <csv_file> --artic_use_nanopolish \
       --fast5_directory <fast5_dir> --sequencing_summary <txt_file>

4. When the execution finishes, you will find your results in the results/ directory.

Requirements

• Nextflow (>= 22.04.0)
• Apptainer or Docker

Nextflow works best in GNU/Linux and MacOS. If you really must use Windows, you need to setup everything on WSL.

Pipeline Utilization

Inputs

This pipeline is designed to run using the data that's already been basecalled and demultiplexed, namely the FASTQ files. If you don't have this yet or want to make basecalling again with higher accuracy, check this pipeline.

Thus, you will need:

• A CSV file the information for each sample (more on that later)
• A single FASTQ file for each sample (compressed or not). If you have the default files generated with Guppy (lots of files), you should concatenate them first.
• (only if you want to use Nanopolish instead of Medaka) The subdirectory pass of the directory with the FAST5 files, and sequencing_summary.txt.

How to prepare your sample information file

This file (in CSV format) must have at least two columns: sample and fastq_file, associating each sample with their corresponding FASTQ data. The values of the sample columns will be used to name files associated to each sample, so it's recommended to keep it simple (ideally only letters and/or numbers). You can also add any additional column you want, these will be included in the final summary. It is recommended to use absolute paths for the FASTQ files, but you can also use relative paths (relative to the execution directory).

Another set of columns you can include in this file relate to the GISAID submission. These can be any of the columns in the submission template (available in data/), indicated by the gisaid_ prefix followed by the column code (the first row of the Excel file). It is recommended to at least include covv_virus_name, because the pipeline will use this column to rename the sequences in the FASTA file, process that you will have to do manually if you don't provide these values. You can find in the Excel file all the available fields and their required formats. Some columns will be filled by the pipeline either way, these are: fn, covv_type, covv_passage, covv_host, covv_seq_technology, covv_assembly_method and covv_coverage.

For example for the following 5 samples we included the extra field city (that will be available in the final summary), and the GISAID fields covv_virus_name, covv_collection_date and covv_location.

sample,fastq_file,city,gisaid_covv_virus_name,gisaid_covv_collection_date,gisaid_covv_location
2101011,./data_fastq/barcode01.fastq.gz,Punta Arenas,hCoV-19/Chile/CADIUMAG-51/2021,2021-05-20,South America / Chile / Magallanes / Punta Arenas
2101018,./data_fastq/barcode02.fastq.gz,Punta Arenas,hCoV-19/Chile/CADIUMAG-52/2021,2021-05-20,South America / Chile / Magallanes / Punta Arenas
2101053,./data_fastq/barcode03.fastq.gz,Punta Arenas,hCoV-19/Chile/CADIUMAG-53/2021,2021-05-20,South America / Chile / Magallanes / Punta Arenas
2101025,./data_fastq/barcode04.fastq.gz,Punta Arenas,hCoV-19/Chile/CADIUMAG-54/2021,2021-05-20,South America / Chile / Magallanes / Punta Arenas
2105001,./data_fastq/barcode05.fastq.gz,Punta Arenas,hCoV-19/Chile/CADIUMAG-55/2021,2021-05-20,South America / Chile / Magallanes / Punta Arenas

Pipeline parameters

The pipeline has various parameters to help to suit your needs. These are:

Parameter	Required	Default	Description
sample_data	yes	---	CSV file with each sample related information.
fast5_directory	no	---	FAST5 pass subdirectory, only needed if you want to use Nanopolish.
sequencing_summary	no	---	Summary of the sequencing process, required if you provide fast5_directory.
run_id	no	""	Optional ID that will be used as suffix in the summary files.
artic_primer_scheme	no	SARS-CoV-2/V4.1	ARTIC primer scheme used.
artic_medaka_model	no	r103_hac_g507	Medaka model to use (this needs to match the basecalling model used).
artic_use_nanopolish	no	false	Use Nanopolish instead of Mekada for polishing.
artic_normalise	no	500	Coverage target used by the ARTIC pipeline.
gisaid_template	no	data/20230515_EpiCoV....xlsx	Path to the GISAID upload template (included in the repository).
gisaid_submission_enabled	no	true	Enable (or not) the GISAID submission preparation.
publish_minimum_completion	no	95	Percentage value (0 - 100) indicating the required percentage of covered bases of the reference to call an assembly "valid" for the submission process.
multiqc_config	no	conf/multiqc_config.yaml	Path to a valid MultiQC config to use. The pipeline includes one already.
output_directory	no	results	Directory to store the pipeline results.

These parameters / default values are defined in params.default.yml. You can specify these via command-line options (for example --run_id 20210501A). Alternatively, you can provide them in a YAML file, using the params.default.yml as a template (don't edit or move this file, as it's required for execution).

Execution

The pipeline can be downloaded directly (with git clone for example), and you can specify to Nextflow the path, but you can also indicate just catg-umag/ncov2019-ont-nf and a version with the -r option, and that will download the pipeline automatically. For example:

nextflow run catg-umag/ncov2019-ont-nf -r v2.0.0 -profile apptainer ...

It is mandatory to use either the apptainer or docker profiles. Otherwise, the pipeline will expect all necessary tools to be installed and will likely not work.

Example using parameters by command line:

nextflow run ncov2019-ont-nf/ -profile apptainer --sample_data input/samples.csv --run_id R210505

Example using a YAML file for the parameters:

params.yml:

sample_data: input/samples.csv
run_id: R210505
artic_medaka_model: r941_min_hac_g507
artic_normalise: 1000
gisaid_submission_enabled: false

nextflow run ncov2019-ont-nf/ -profile apptainer -params-file params.yml

By default, the pipeline will execute locally. However, if you are using a Slurm Cluster, use the slurm profile. Also, don't forget the apptainer profile (it must be installed on the cluster): -profile slum,apptainer.

Results

Inside the results directory, you will find the following:

• fastq_data/: FASTQ files for each sample, generated after length filter
• artic/: results of the ARTIC pipeline execution for each of the samples
• qc/: quality and coverage metrics for each of the samples
• vcf/: VCF files annotated by SnpEff
• lineages/: RAW results from the lineage identification tools
• summary/: directory with summaries of the obtained information:
  • all_consensus.fasta: FASTA file with the consensus for each sample (even the incomplete ones)
  • sample_summary.csv: CSV file with the provided info for each sample and also lineages and coverage stats
  • variants_list.csv: CSV with the variants for all the samples with their relevant info
  • summary.xlsx: Excel file with the information of sample_summary.csv as first sheet and a summary of the variant presence on the samples in the second one.
• gisaid_submission/: if you enabled the submission preparation you will find here a FASTA file filtered by coverage and with sequences renamed if you provided the corresponding name, and also the upload template filled with the available information (you could still need to fill some values manually)

How to Cite

If this work was useful to you, you can cite this article:

González-Puelma, J.; Aldridge, J.; Montes de Oca, M.; Pinto, M.; Uribe-Paredes, R.; Fernández-Goycoolea, J.; Alvarez-Saravia, D.; Álvarez, H.; Encina, G.; Weitzel, T.; Muñoz, R.; Olivera-Nappa, Á.; Pantano, S.; Navarrete, M.A. Mutation in a SARS-CoV-2 Haplotype from Sub-Antarctic Chile Reveals New Insights into the Spike’s Dynamics. Viruses 2021, 13, 883. https://doi.org/10.3390/v13050883

Acknowledgements

• ARTIC and all its dependencies
• Python and libraries: biopython, openpyxl, pandas, scipy
• Bioinformatic tools: FastQC, MultiQC, samtools, SnpEff, Nextclade, Pangolin
• Other tools: Nextflow, libreoffice