nf-core/viralmetagenome

Path to comma-separated file containing information about the samples in the experiment.

The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure.

Sample metadata that is included in the multiqc report

Email address for completion summary.

Skip read preprocessing and use input reads for downstream analysis

Skip read quality statistics summary tool ‘fastqc’

Save reads after the final preprocessing step

Save reads after every preprocessing step

With or without umi detection

With or without umi extraction

Specify at what level UMI deduplication should occur.

Specify the maximum number of mismatches between reads for them to still be considered neighbors.

Specify the strategy for umi-deduplication directional vs cluster

Specify the strategy or method for umi-tools deduplication on mapping level

Discard R1 / R2 if required 0, meaning not to discard

The used trimming tool

Skip read trimming

Use Fastp’s deduplicate option

Define the accuracy used for hashes while deduplicating with faspt

Fasta file of adapters

Specify true to save files that failed to pass trimming thresholds ending in *.fail.fastq.gz

Specify true to save all merged reads to the a file ending in *.merged.fastq.gz

Inputs with fewer than this reads will be filtered out of the “reads” output channel

Skip filtering of low complexity regions in reads

Reference files containing adapter and/or contaminant sequences for sequence kmer matching (used by bbduk)

Skip the removal of host read sequences

Kraken2 database used to remove host and conamination

Kraken2 library(s) required to remove host and contamination

Skip the fastqc step after host & contaminants were removed

Skip determining the metagenomic diversity of the sample

Specify the taxonomic read classifiers, choices are ‘kaiju,kraken2’

Save the used databases

Location of the Kraken2 database

Save classified and unclassified reads as fastq files

Save summary overview of read classifications in a txt file

Save kraken2’s used minimizers

Location of bracken database

Location of Kaiju database

Level of taxa rank that needs to be determined

Skip de novo assembly of reads

The specified tools for denovo assembly, multiple options are possible

specific SPAdes mode to run

File or directory with amino acid HMMs for Spades HMM-guided mode.

Regex pattern to identify contigs that have been made by the assemblers

Skip the refinement/polishing of contigs through reference based scaffolding and read mapping

Save intermediate polishing files

Set of fasta sequences used as potential references for the contigs

Skip the preclustering of assemblies to facilitate downstream processing of assemblies

Keep the contigs that could not be classified with the taxonomic databases (kaiju_db & kraken2_db)

Specify the metagenomic classifiers to use for contig taxonomy classification: ‘kraken2,kaiju’

Taxon conflict resolution mode, must be 1 (Kaiju), 2 (Kraken), lca, or lowest.

Level of taxonomic simplification

Hard constrain for taxa to exclude from the preclustering, if multiple given make sure to enclose with ’”’ and separate with a space.

Taxon ids to exclude along with all their children from the preclustering, if multiple given make sure to enclose with ’”’ and separate with a space.

Taxon ids to exclude along with all their parents from the preclustering, if multiple given make sure to enclose with ’”’ and separate with a space.

Taxon ids to include along with all their children from the preclustering, if multiple given make sure to enclose with ’”’ and separate with a space.

Taxon ids to include along with all their parents from the preclustering, if multiple given make sure to enclose with ’”’ and separate with a space.

Cluster algorithm used for contigs

(only with mash) Algorithm to partition the network.

Skip creation of the hybrid consensus, instead keep the scaffold with ambiguous bases if the depth of scaffolds is not high enough.

Identity threshold value used in clustering algorithms

Minimum allowed contig size

Maximum allowed contig size

Define the maximum percentage of ambiguous bases in a contig

Skip the filtering of contigs that did not cluster together with other contigs

Don’t realign reads to consensus sequences and redefine the consensus through (multiple) iterations

number of iterations

mapping tool used during iterations

variant caller used during iterations

call variants during the iterations

consensus tool used for calling new consensus during iterations

calculate summary statistics during iterations

Skip the analysis of variants for the external reference or contigs

Define which mapping tool needs to be used when mapping reads to reference

Sequence to use as a mapping reference instead of the de novo contigs or scaffolds

deduplicate the reads

Define the variant caller to use: ‘ivar’ or ‘bcftools’

consensus tool used for calling new consensus in final iteration

UMI seperator in fastq header.

Specify the sketch size, the number of (non-redundant) min-hashes that are kept.

Specify the kmer size for mash to create their hashes

Define the minimum number of mapped reads in order to continue the variant and consensus calling

calculate summary statistics in final iteration

Skip the quality measurements on consensus genomes

Skip the use of checkv for quality check

Reference database used by checkv for consensus quality control

Skip the annotation of the consensus constructs

Database used for annotation of the cosensus constructs

Skip the use of QUAST for quality check

Skip the blast search of contigs to the provided reference DB

Skip creating an alignment of each the collapsed clusters and each iterative step

Specify the search algorithm to use for mmseqs. 0: auto 1: amino acid, 2: translated, 3: nucleotide, 4: translated nucleotide alignment

Custom MultiQC yaml file containing HTML including a methods description.

Prefix of all output files followed by [date][pipelineversion][runName]

Global prefix set if you don’t want metadata embedded in the prefix