REVICA

Revica is a reference-based viral consensus genome assembly pipeline for some of the most common respiratory viruses. Revica currently supports genome assembly of:

• Enterovirus (EV)
• Seasonal human coronavirus (HCOV)
• Human metapneumovirus (HMPV)
• Human respiratory syncytial virus (HRSV)
• Human parainfluenza virus (HPIV)
• Measles morbillivirus (MeV)
• Influenza A virus (IAV)
• Influenza B virus (IBV)
• Human adenovirus (HAdV)

Workflow

Usage

Install Nextflow

Install Docker

To run Revica:

nextflow run asereewit/revica -r main -latest --input example_samplesheet.csv --output example_output -profile docker

on AWS:

nextflow run asereewit/revica -r main -latest --input example_samplesheet.csv --output example_output -profile docker -c your_nextflow_aws.config

Options

Option	Explanation
--input	samplesheet in csv format with fastq information
--output	output directory (default: revica_output)
--db	(multi)fasta file to overwrite the bundled viral database
--run_name	name for the summary tsv file (default: 'run')
--skip_fastqc	skip quality control using FastQC (default: false)
--skip_fastp	skip adapters and reads trimming using fastp (default: false)
--run_kraken2	run Kraken2 for classifying reads (default: false)
--kraken2_db	Kraken2 database for reads classification, needs to be specified when using --run_kraken2
--kraken2_variants_host_filter	use reads that didn't map to the kraken2 database for downstream consensus calling
--save_kraken2_unclassified_reads	save reads that didn't map to the specified kraken2 database
--save_kraken2_classified_reads	save reads that map to the specified kraken2 database
--trim_len	minimum read length to keep (default:50)
--save_trimmed_reads	save trimmed fastq
--sample	downsample fastq to a certain fraction or number of reads
--ref_min_median_cov	minimum median coverage on a reference for consensus assembly (default: 3)
--ref_min_genome_cov	minimum reference coverage percentage for consensus assembly (default: 60%)
--ivar_consensus_t	minimum frequency threshold to call consensus (default: 0.6)
--ivar_consensus_q	minimum quality score threshold to call consensus (default: 20)
--ivar_consensus_m	minimum depth to call consensus (default: 5)

Usage notes

• Samplesheet example: assets/samplesheet.csv
• You can create a samplesheet using the bundled python script: python bin/fastq_dir_samplesheet.py fastq_dir samplesheet_name.csv
• Memory and CPU usage for pipeline processes can be adjusted in conf/base.config
• Process arguments can be adjusted in conf/modules.config
• You can use your own reference(s) for consensus genome assembly by specifying the --db parameter followed by your fasta file.
  • reference header format: >reference_accession reference_tag reference_header_info
  • it's important to tag the fasta sequences for the same species or gene segments with the same name or abbreviation in the header section, otherwise the pipeline will generate a consensus genome for every reference where the median coverage of the first alignment exceed the specified threshold (default 3).
  • Revica works with segmented viral genomes, just keep the different gene segments separated and tag them in the reference fasta file
• If you are using Docker on Linux, check out these post-installation steps (especially cgroup swap limit capabilities support) for configuring Linux to work better with Docker.
• By default, Docker has full access to full RAM and CPU resources of the host, but if you are using MacOS, go to Settings -> Resources in Docker Desktop to make sure enough resources are allocated to docker containers.

Contact

For bug reports please email [email protected] or raise an issue on Github.