Usage

Pipeline parameters

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.

Samplesheet format

Illumina

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:

sample,fastq_1,fastq_2
SAMPLE_1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
SAMPLE_1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
SAMPLE_2,AEG588A2_S4_L003_R1_001.fastq.gz,

NB: Dashes (-) and spaces in sample names are automatically converted to underscores (_) to avoid downstream issues in the pipeline.

Nanopore

You have the option to provide a samplesheet to the pipeline that maps sample ids to barcode ids. This allows you to associate barcode ids to clinical/public database identifiers that can be used to QC or pre-process the data with more appropriate sample names.

--input '[path to samplesheet file]'

It has to be a comma-separated file with 2 columns. A final samplesheet file may look something like the one below:

sample,barcode
21X983255,1
70H209408,2
49Y807476,3
70N209581,4

NB: Dashes (-) and spaces in sample names are automatically converted to underscores (_) to avoid downstream issues in the pipeline.

Nanopore input format

For Nanopore data the pipeline only supports amplicon-based analysis obtained from primer sets created and maintained by the ARTIC Network. The artic minion tool from the ARTIC field bioinformatics pipeline is used to align reads, call variants and to generate the consensus sequence.

Artic minion requires that you provide *.fastq files as input to the pipeline. These files can typically be obtained after demultiplexing and basecalling the sequencing data using Guppy (see ARTIC SOP docs). This pipeline requires that the files are organised in the format outlined below and gzip compressed files are also accepted:

.
└── fastq_pass
    └── barcode01
        ├── FAP51364_pass_barcode01_97ca62ca_0.fastq
        ├── FAP51364_pass_barcode01_97ca62ca_1.fastq
        ├── FAP51364_pass_barcode01_97ca62ca_2.fastq
        ├── FAP51364_pass_barcode01_97ca62ca_3.fastq
        ├── FAP51364_pass_barcode01_97ca62ca_4.fastq
        ├── FAP51364_pass_barcode01_97ca62ca_5.fastq
    <TRUNCATED>

The command to run the pipeline would then be:

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

Illumina primer sets

The Illumina processing mode of the pipeline has been tested on numerous different primer sets. Where possible we are trying to collate links and settings for standard primer sets to make it easier to run the pipeline with standard parameter keys. If you are able to get permissions from the vendor/supplier to share the primer information then we would be more than happy to support it within the pipeline.

For SARS-CoV-2 data we recommend using the “MN908947.3” genome because it is supported out-of-the-box by the most commonly used primer sets available from the ARTIC Network. For ease of use, we are also maintaining a version of the “MN908947.3” genome along with the appropriate links to the ARTIC primer sets in the genomes config file used by the pipeline. The genomes config file can be updated independently from the main pipeline code to make it possible to dynamically extend this file for other viral genomes/primer sets on request.

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

ARTIC primer sets

An example command using v3 ARTIC primers with “MN908947.3”:

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>

SWIFT primer sets

The SWIFT amplicon panel is another commonly used method used to prep and sequence SARS-CoV-2 samples. We haven’t been able to obtain explicit permission to host standard SWIFT primer sets but you can obtain a masterfile which is freely available from their website that contains the primer sequences as well as genomic co-ordinates. You just need to convert this file to BED6 format and provide it to the pipeline with --primer_bed swift_primers.bed. Be sure to check the values provided to --primer_left_suffix and --primer_right_suffix match the primer names defined in the BED file as highlighted in this issue. For an explanation behind the usage of the --ivar_trim_offset 5 for SWIFT primer sets see this issue.

An example command using SWIFT primers with “MN908947.3”:

nextflow run nf-core/viralrecon \
    --input samplesheet.csv \
    --outdir <OUTDIR> \
    --platform illumina \
    --protocol amplicon \
    --genome 'MN908947.3' \
    --primer_bed swift_primers.bed \
    --primer_left_suffix '_F' \
    --primer_right_suffix '_R' \
    --ivar_trim_offset 5 \
    --skip_assembly \
    -profile <docker/singularity/podman/conda/institute>

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/viralrecon --input samplesheet.csv --outdir <OUTDIR> --genome 'MN908947.3' -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/viralrecon -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/viralrecon

Reproducibility

It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/viralrecon releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to check if your system is supported, please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

Freyja

Freyja relies on a dataset of barcodes that use lineage defining mutations (see UShER). By default the most recent barcodes will be downloaded and used. However, if analyses need to be compared across multiple datasets, it might be of interest to re-use the same barcodes, or to rerun all Freyja analyses with the most recent dataset. To do this, specify the barcodes and lineages using the --freyja_barcodes, --freyja_lineages parameters, respectivly. The boostrapping of Freyja can be skipped by specifying --skip_freyja_boot.

Cutadapt

According to Cutadapt’s documentation regarding adapter types, you can have:

• Regular 3’ adapter: -a ADAPTER
  • Set --skip_noninternal_primers to true
  • Set --threeprime_adapters to true
• Regular 5’ adapter: -g ADAPTER
  • Set --skip_noninternal_primers to true
• Non-internal 3’ adapter: -a ADAPTERX:
  • Change modules_illumina.config > PREPARE_PRIMER_FASTA > ext.args to use $ instead of ^ to add the X at the end of the sequence.
  • Set --threeprime_adapters to true
• Non-internal 5’ adapter: -g XADAPTER: This is the option by default.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Enterovirus

nf-core/viralrecon has been extended to support Enterovirus typing through optimized BLASTN searches with taxonomic ID filtering.

The goal of this addition is to enable rapid and accurate Enterovirus strain/genotype identification directly from consensus sequences in the de novo assembly track, reducing BLASTN runtime while maintaining high typing accuracy.

Enterovirus profile and recommended params

The Enterovirus profile introduces specific adjustments compared to a standard viralrecon run, primarily to optimize strain/genotype typing.

By default, variant calling is deactivated for enterovirus typing because de novo assembly performs better through assembled contigs and BLAST comparison.

De novo assembly

De novo assembly is performed using spades and/or unicycler to generate contigs for BLASTN typing. Note that minia is not a default assembler for enterovirus.

BLASTN

Typing through blastn is performed through supplying a blast database with taxid mapping and a taxidlist, to improve typing specificity and reduce runtime. The new optional parameter, --taxidlist, allows users to provide a list of NCBI Taxonomy IDs corresponding to Enterovirus taxa of interest.

Taxonomy IDs related to enterovirus can be retriewed through // Add command here

Example usage:

--blastdb path/to/blastdb
--taxidlist path/to/taxidlist_taxids.txt
--profile test_ev

Reference genome

The default reference used in the enterovirus config of nf-core/viralrecon is the REFSEQ genome NC_002058.3 from NCBI of Enterovirus C. Given that a blast database is supplied, this reference is not significant for the de novo assembly. However, if no blast database is supplied, the reference genome will be used as reference for blast searches.

Users may provide their own custom reference genomes using the parameters:

--fasta <path_to_reference.fasta>
--gff   <path_to_annotation.gff>

HIV

nf-core/viralrecon has been optimized to support HIV genome reconstruction and resistance detection.

This implementation takes inspiration from the parameterization used in the HIVdb pipeline at Stanford University.

The goal is to adapt the viralrecon framework to ensure accurate minor variant calling, reliable consensus sequence generation, and compatibility with resistance analysis tools for HIV.

HIV profile and recomended params

The HIV profile (-profile hiv) introduces specific adjustments compared to a standard viralrecon run, mainly in the variant calling and consensus generation steps, to align with the requirements of HIV resistance interpretation pipelines.

⚠️ By default, de novo assembly is deactivated for HIV as the resistance detection is only performed for the consensus genome through mapping approach, which has been already benchmarked agains Stanford’s HIVdb results.

Reference genome

The default reference used in the HIV config of nf-core/viralrecon is the one derived from the codfreq JSON profile. This reference has been specifically generated from the codfreq .json profile. By aligning to this codfreq-based reference, the resulting codon frequencies and codon coverages are directly comparable to those produced by HIVdb, ensuring accurate interpretation of resistance data.

However, users may provide their own custom reference genome using the parameters:

--fasta <path_to_reference.fasta>
--gff   <path_to_annotation.gff>

When a custom reference is supplied, all variant calling, consensus generation, and resistance detection steps will use this user-defined reference.

⚠️ Note that this may produce results that are not directly comparable to those obtained with HIVdb. Users employing custom references do so at their own discretion and risk, as the chosen reference can affect the INDELs calling and their coverage and frequency.

Important: When performing HIV resistance detection, an annotation file (.gff) is mandatory. The GFF file is required to correctly annotate the pol gene regions (PR, RT and IN), which are essential for both sierra-local and codfreq analyses.

Available genome options

Two HIV genome references are distributed with viralrecon and can be selected using the --genome parameter:

• --genome codfreq: The codfreq-derived reference (DEFAULT), generated from the original codfreq JSON annotation files. This reference ensures that codon-level frequency tables and coverage results are fully compatible with HIVdb and sierra-local resistance prediction outputs.
• --genome 'NC_001802.1': nextclade refeence, based on NC_001802.1 (HXB2 genome, K03455), the reference used by Nextclade for HIV-1 analysis.

HIV-specific parameters

The following parameters have been added to handle HIV resistance detection and related tools.

• General HIV resistance options:
  • perform_hiv_resistance: Whether to perform HIV resistance analysis or not.
• Options related with files required by sierra-local software for resistance detecton in HIV. If not provided, the files included with the software will be used.
  • hivdb_xml: Path to the HIVdb ASI2 XML file. Updated files can be found here, where the different algorithm versions are stored. The one included in the software is the one with HIVDB_9.8.xml name.
  • apobec_drm: Path to the JSON HIVdb APOBEC DRM file. Updated files can be found here with the apobec_drms.json name.
  • apobec_csv: Path to the CSV APOBEC file. Updated files can be found here with the apobecs.csv name.
  • unusual_csv: Path to the CSV file used to determine unusual mutations. The file included with the software can be found here with the name rx-all_subtype-all.csv
  • sdrms_csv: Path to the CSV file used to define SDRM mutations. Updated files can be found here with the sdrms_hiv1.csv name.
  • mutation_csv: Path to the CSV file defining mutation types. Updated files can be found here with the mutation-type-pairs_hiv1.csv name.

Nextclade configuration

The following default parameters are used in the --genome codfreq and --genome 'NC_001802.1' for Nextclade HIV-1 analysis. Make sure to update nextclade_dataset_tag to the latest one so the pipeline can download it.

nextclade_dataset_tag  = '2025-09-09--12-13-13Z'
nextclade_dataset_name = 'neherlab/hiv-1'
nextclade_dataset      = false

HIV-specific configuration

When using --genome 'codfreq' or --genome 'NC_001802.1' or --perform_hiv_resistance, which are activated in the -profile hiv configuration, the following configuration is activated by default.

Variant calling

Variant calling is performed using iVar variants with parameters optimized for HIV intra-host diversity:

• Variant frequency threshold: 0.01: Variants are called if their allele frequency is at least 1%, allowing detection of minority variants relevant for resistance analysis.
• Minimum base quality: 30: Ensures reliability of detected variants.
• Minimum depth of coverage: 50: Matches the minimum coverage used in HIVdb protocols to support robust variant calling.

Consensus generation

Consensus sequences are generated using iVar consensus, which supports ambiguous nucleotide codes, a key feature for HIV resistance interpretation tools.
The parameters are defined as follows:

• Frequency threshold: 0.9: A variant is included in the consensus until a position’s allele frequency is representing at least 90% of the allele frequency. This means that variants with frequencies below 50% may still be incorporated if necessary to achieve the 90% threshold in a given position.
• Minimum base quality: 30
• Minimum depth of coverage: 50
• Low coverage regions:: Positions with fewer than 10 reads are masked as N.

The user can change this configuration by providing a custom config file with -c param. This config file should contain this piece of code with the specific configuration for IVAR_VARIANTS and IVAR_CONSENSUS. RESISTANCE_REPORT’s ext.args2 should be the same as IVAR_CONSENSUS’s ext.args for the final report to contain the appropriate information.

process {
    withName: 'IVAR_CONSENSUS' {
        ext.args = '-t 0.9 -q 30 -m 50 -n N'
    }
    withName: 'IVAR_VARIANTS' {
        ext.args = '-t 0.01 -q 30 -m 50'
    }
    withName: 'RESISTANCE_REPORT' {
        ext.args2 = '-t 0.9 -q 30 -m 50 -n N'
    }
}

HIV resistance detection protocol

The detection of HIV drug resistance in nf-core/viralrecon is performed using the sierra-local software, complemented with codon-level frequency analysis generated by a modified version of codfreq.

This integrated approach ensures that both mutation interpretation and codon frequency information are available for downstream resistance reporting.

1. Sierra-local: HIV resistance prediction

sierra-local is a Python3 implementation of the Stanford University HIV Drug Resistance Database (HIVdb) Sierra web service.

It allows laboratories to generate HIV-1 drug resistance predictions locally, without the need to transmit patient data over a network. This provides full control over data provenance, security, and regulatory compliance.

Sierra-local computes drug resistance predictions directly from consensus HIV-1 sequences, producing JSON-formatted output that includes key resistance-associated mutations and drug susceptibility scores.

Within the HIV module of nf-core/viralrecon, sierra-local is executed using the consensus .FASTA file generated for each sample. The output is a .json file containing the main resistance data.

However, sierra-local operates at the codon level, and its JSON output does not include codon-level frequencies or codon depth information.
These additional metrics are crucial for resistance reporting and neeed consistency with other viralrecon outputs, which are based on nucleotide-level variant calls.

2. Codon-level frequency analysis with codfreq

To obtain codon frequency information, nf-core/viralrecon integrates a modified version of codfreq tool.

The modified version of codfreq produces a codon frequency table in the CodFreq format, which contains seven columns:

This format enables codon-level analysis of intra-sample diversity.

However, codfreq requires a custom JSON file that describes the HIV gene structure (PR, RT, and IN). To generate this JSON dynamically according to the user’s selected reference genome, several preprocessing steps are performed.

3. Generation of codfreq annotation JSON

The generation of the required JSON file for codfreq involves the following steps:

1. Conversion of reference annotations: The original codfreq HIV profile (in JSON format) has been converted into a .fasta and .gff file containing the necessary annotations for PR, RT, and IN genes. These files are included within nf-core/viralrecon assets.

2. Reference genome alignment with Liftoff: Only performed when the provided genome is not the one from codfreq. Using the provided reference genome and the .fasta and .gff from the step 1, annotation files are processed with Liftoff to transfer the HIV gene annotations to the user’s specific reference genome.

3. Custom JSON generation: The annotated .gff file and the reference .fasta are used as input for a custom Python script bin/gff2json.py within nf-core/viralrecon. This script produces the JSON file required by codfreq, ensuring accurate gene coordinates and consistency with the reference genome.

4. Annotation harmonization for variant mapping: Only performed when the provided genome is not the one from codfreq. Once the new .gff file is produced by Liftoff, an additional annotation step is performed. The variant caller outputs (based on nucleotide-level variants) are re-annotated using this updated .gff, generating a secondary annotation layer. This process allows a direct mapping between codon-based resistance results (from sierra-local and codfreq) and nucleotide-based variant calls, ensuring that both analyses can be compared and integrated accurately in downstream reviews.

This automated workflow guarantees that the codfreq JSON file always matches the user-defined HIV reference, maintaining compatibility with both sierra-local and the consensus sequence data.

4. Integration of codfreq with mapped reads

Once the annotation JSON is generated, the mapped BAM files and the codfreq JSON are processed using a modified version of codfreq within the pipeline.
This version has been adapted to work seamlessly with nf-core/viralrecon output formats and file structures.

The result is a set of .codfreq tables containing codon-level read frequencies for PR, RT, and IN genes, harmonized with the annotation used by Sierra-local.

5. Integration and final reporting

Finally, the outputs from sierra-local (.json) and codfreq (.codfreq) are merged to produce custom HIV resistance summary tables.

The resulting integrated reports provide a comprehensive view of HIV drug resistance, maintaining compatibility with established databases and ensuring consistency with the rest of the viralrecon analytical framework.

To know more about the output files generated in the HIV resistance steps, check the output documentation.