mpxv-artic-nf

A Nextflow pipeline for running the ARTIC network's fieldbioinformatics tools (https://github.com/artic-network/fieldbioinformatics), with a focus on monkeypox virus (mpxv).

Introduction

This pipeline is based on the BCCDC-PHL/ncov2019-artic-nf pipeline, which is a fork of the connor-lab/ncov2019-artic-nf pipeline. It has been modified to support analysis of monkeypox virus.

flowchart TD
  ref[ref.fa]
  composite_ref[composite_ref.fa]
  primers[primer.bed]
  primer_pairs[primer_pairs.tsv]
  fastq[fastq_dir]
  fastq --> normalizeDepth(normalizeDepth)
  composite_ref --> performHostFilter
  normalizeDepth(normalizeDepth) --> performHostFilter(performHostFilter) 
  performHostFilter(performHostFilter) --> readTrimming(readTrimming)
  readTrimming(readTrimming) --> filterResidualAdapters(filterResidualAdapters)
  filterResidualAdapters --> readMapping(readMapping)
  ref --> readMapping(readMapping)
  readMapping(readMapping) --> trimPrimerSequences(trimPrimerSequences)
  primers --> trimPrimerSequences(trimPrimerSequences)
  trimPrimerSequences(trimPrimerSequences) --> callConsensusFreebayes(callConsensusFreebayes)
  callConsensusFreebayes(callConsensusFreebayes) --> alignConsensusToReference(alignConsensusToReference)
  ref --> alignConsensusToReference
  alignConsensusToReference --> consensusAlignment[consensus.aln.fa]
  trimPrimerSequences --> makeQCCSV(makeQCCSV)
  callConsensusFreebayes --> makeQCCSV
  callConsensusFreebayes --> consensus[consensus.fa]
  callConsensusFreebayes --> variants[variants.vcf]
  ref --> makeQCCSV
  primers --> makeQCCSV
  primer_pairs --> makeQCCSV
  makeQCCSV --> qcCSV(qc.csv)
  makeQCCSV --> depthPNG(depth.png)

Quick-start

nextflow run BCCDC-PHL/mpxv-artic-nf -profile conda \
  --prefix "output_file_prefix" \
  --bed /path/to/primers.bed \
  --ref /path/to/ref.fa \
  --primer_pairs_tsv /path/to/primer_pairs_tsv \
  --composite_ref /path/to/human_and_mpxv_composite_ref \
  --directory /path/to/reads \
  --outdir /path/to/outputs

Installation

An up-to-date version of Nextflow is required because the pipeline is written in DSL2. Following the instructions at https://www.nextflow.io/ to download and install Nextflow should get you a recent-enough version.

Conda

The repo contains a environment.yml files which automatically build the correct conda env if -profile conda is specifed in the command. Although you'll need conda installed, this is probably the easiest way to run this pipeline.

--cache /some/dir can be specified to have a fixed, shared location to store the conda build for use by multiple runs of the workflow.

Config

Important config options are:

Option	Default	Description
normalizationTargetDepth	200	Target depth of coverage to normalize to prior to alignment
normalizationMinDepth	5	Minimum depth of coverage to normalize to prior to alignment
keepLen	50	Length of reads to keep after primer trimming
qualThreshold	20	Sliding window quality threshold for keeping reads after primer trimming
varMinFreqThreshold	0.25	Allele frequency threshold for ambiguous variant
varFreqThreshold	0.75	Allele frequency threshold for unambiguous variant
varMinDepth	10	Minimum coverage depth to call variant

Depth Normalization

By default, sequence depth will be normalized using bbnorm to the value specified by the --normalizationTargetDepth param (default: 200). To skip depth normalization, add the --skip_normalize_depth flag.

QC

A script to do some basic QC is provided in bin/qc.py. It measures the % of reference bases are covered by varMinDepth, and the longest stretch of consensus sequence with no N bases. This script does not make a QC pass/fail call.

Output

A subdirectory for each process in the workflow is created in --outdir. A nml_upload subdirectory containing dehosted fastq files and consensus sequences is included.