Hello nf-core/viralrecon team,
First, thank you for maintaining such a robust and well-documented pipeline. Viralrecon has been extremely useful in our laboratory for viral genomic surveillance using Illumina short-read data.
I would like to ask for clarification — and possibly guidance — on how to properly adapt the pipeline for use with non–SARS-CoV-2 viruses, specifically arboviruses such as Chikungunya virus (CHIKV) and Dengue virus (DENV).
Context
I am working with clinical samples sequenced on Illumina MiSeq (paired-end), using both amplicon-based and metagenomic approaches. My goal is to use Viralrecon for:
Read QC and primer trimming
Reference-based alignment
Variant calling and consensus generation
Coverage analysis and reporting
Questions
Is Viralrecon fully compatible with non–SARS-CoV-2 genomes, such as CHIKV or DENV, when providing a custom reference FASTA?
For amplicon-based protocols:
Can we supply custom primer BED/FASTA files in place of ARTIC primer schemes?
Are there any limitations or assumptions in the current modules that are specific to SARS-CoV-2 primer sets or genome structure?
Are there recommended adjustments for variant-calling parameters when working with non-segmented viruses (like CHIKV) or segmented ones (like DENV, if applicable to future updates)?
Is there an example or documentation on running Viralrecon with other reference genomes, especially for viruses with different genome sizes, complexities, or amplicon strategies?
Feature Request
Would it be possible to include:
A generic amplicon mode, allowing any virus to be processed as long as the user provides reference + primers?
Example configuration files or templates for other viral genomes?
(If feasible) a small tutorial or documentation section explaining how to adapt the pipeline for other viruses commonly processed in public health and research laboratories (e.g., CHIKV, DENV, MAYV, OROV, etc.).
This would greatly benefit groups working with multiple emerging pathogens, especially in regions where arbovirus outbreaks are frequent.
If the team is open to it, I would be glad to assist with testing and validating the adaptation for CHIKV and DENV. We can provide datasets, reference genomes, and primer sets for benchmarking.
Thank you very much for your time and for all your work on nf-core.
Best regards,
Jean Nascimento
Hello nf-core/viralrecon team,
First, thank you for maintaining such a robust and well-documented pipeline. Viralrecon has been extremely useful in our laboratory for viral genomic surveillance using Illumina short-read data.
I would like to ask for clarification — and possibly guidance — on how to properly adapt the pipeline for use with non–SARS-CoV-2 viruses, specifically arboviruses such as Chikungunya virus (CHIKV) and Dengue virus (DENV).
Context
I am working with clinical samples sequenced on Illumina MiSeq (paired-end), using both amplicon-based and metagenomic approaches. My goal is to use Viralrecon for:
Read QC and primer trimming
Reference-based alignment
Variant calling and consensus generation
Coverage analysis and reporting
Questions
Is Viralrecon fully compatible with non–SARS-CoV-2 genomes, such as CHIKV or DENV, when providing a custom reference FASTA?
For amplicon-based protocols:
Can we supply custom primer BED/FASTA files in place of ARTIC primer schemes?
Are there any limitations or assumptions in the current modules that are specific to SARS-CoV-2 primer sets or genome structure?
Are there recommended adjustments for variant-calling parameters when working with non-segmented viruses (like CHIKV) or segmented ones (like DENV, if applicable to future updates)?
Is there an example or documentation on running Viralrecon with other reference genomes, especially for viruses with different genome sizes, complexities, or amplicon strategies?
Feature Request
Would it be possible to include:
A generic amplicon mode, allowing any virus to be processed as long as the user provides reference + primers?
Example configuration files or templates for other viral genomes?
(If feasible) a small tutorial or documentation section explaining how to adapt the pipeline for other viruses commonly processed in public health and research laboratories (e.g., CHIKV, DENV, MAYV, OROV, etc.).
This would greatly benefit groups working with multiple emerging pathogens, especially in regions where arbovirus outbreaks are frequent.
If the team is open to it, I would be glad to assist with testing and validating the adaptation for CHIKV and DENV. We can provide datasets, reference genomes, and primer sets for benchmarking.
Thank you very much for your time and for all your work on nf-core.
Best regards,
Jean Nascimento