metaViraVerse

MGnify Nextflow pipeline to generate viral catalogue from assemblies.

Usage

Note

This pipeline is based on results provided by emg-viral-pipeline (VIRify) and mobilome-annotation-pipeline (MAP). In order to generate a catalogue you need to launch VIRify on each sequence file in advance and then use VIRify GFF in MAP execution.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

id,gff,fna,faa,type,biome
unique_identifier,viral.gff,viral.fna,viral.faa,metagenome/genome,biome

id (mandatory) - unique identifier (It is recommended to use ERZ accession if your MAG or assembly was taken ENA)
gff (mandatory) - GFF file containing records in types: viral_sequence, plasmid, prophage. It might also contain CDS records for chosen regions
fna (mandatory) - FASTA file with nucleotide sequences corresponding to chosen regions from GFF
faa (optional) - FASTA file with protein sequences corresponding to CDS regions from GFF
type (mandatory) - string value genome or metagenome describing initial sequence
biome (optional) - metadata describing environmental area of sequence (for example, marine, soil)

Run

nextflow run EBI-Metagenomics/metaviraverse \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

Output

├── plasmids
├── viral_sequences
├── pipeline_info

Folder plasmids:

├── cluster_reps
 ──── plasmids_reps.fasta.gz    # cluster rep compressed fasta

├── clustering
 ──── plasmids_clusters.tsv     # clusters (representative \t members)
 ──── plasmids_pairani.tsv      # blastn pairani table

├── plasmids.fasta              # all sequences

Folder viral_sequences:

├── cluster_reps

 ──── crisprcasfinder
 ──────── viral_sequences_crisprcasfinder.gff
 ──────── viral_sequences_crisprcasfinder.tsv
 ──────── viral_sequences_crisprcasfinder_hq.gff

 ──── taxonomy_plot                          # ViPhOG taxonomy
 ──────── viral_sequences_krona.html         # krona plot
 ──────── viral_sequences_krona.tsv          # taxonomy table with counts (count \t taxonomy tav-separated)
 ──────── viral_sequences_sankey.html        # sankey plot

 ──── taxonomy_vitap                         # ICTV taxonomy
 ──────── viral_sequences_vitap_best.tsv     # taxonomy table with counts (count \t taxonomy tav-separated)
 ──────── viral_sequences_vitap_sankey.html  # sankey plot

 ──── viral_sequences_reps.fasta.gz          # cluster rep compressed fasta
 ──── viral_sequences_reps_stats.tsv         # basic statistics calculated per each cluster representative

├── clustering
 ──── viral_sequences_clusters.tsv           # clusters (representative \t members)
 ──── viral_sequences_pairani.tsv            # blastn pairani table

├── viral_sequences.fasta                    # all sequences

Under review:

VITAP (https://www.nature.com/articles/s41467-025-57500-7) Database: https://figshare.com/articles/dataset/The_database_of_VITAP_2024_03_18_/25426159/3?file=49682337 taxonomy + sankey plot

PLSDB (https://academic.oup.com/nar/article/53/D1/D189/7905312) plasmids only screening with mash

Citations

If you use this pipeline please make sure to cite all used software. This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

MGnify: the microbiome sequence data analysis resource in 2023

Richardson L, Allen B, Baldi G, Beracochea M, Bileschi ML, Burdett T, et al.

Vol. 51, Nucleic Acids Research. Oxford University Press (OUP); 2022. p. D753–9. Available from: http://dx.doi.org/10.1093/nar/gkac1080

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.