A Nextflow pipeline for running the ARTIC network's fieldbioinformatics tools (https://github.com/artic-network/fieldbioinformatics), with a focus on ncov2019
WARNING - THIS REPO IS UNDER ACTIVE DEVELOPMENT AND ITS BEHAVIOUR MAY CHANGE AT ANY TIME.
PLEASE ENSURE THAT YOU READ BOTH THE README AND THE CONFIG FILE AND UNDERSTAND THE EFFECT OF THE OPTIONS ON YOUR DATA!
This Nextflow pipeline automates the ARTIC network nCoV-2019 novel coronavirus bioinformatics protocol. It is being developed to aid the harmonisation of the analysis of sequencing data generated by the COG-UK project. It will turn SARS-COV2 sequencing data (Illumina or Nanopore) into consensus sequences and provide other helpful outputs to assist the project's sequencing centres with submitting data.
nextflow run connor-lab/ncov2019-artic-nf [-profile conda,singularity,docker,slurm,lsf] --illumina --prefix "output_file_prefix" --directory /path/to/reads
You can also use cram file input by passing the --cram flag. You can also specify cram file output by passing the --outCram flag.
For production use at large scale, where you will run the workflow many times, you can avoid cloning the scheme repository, creating an ivar bed file and indexing the reference every time by supplying both --ivarBed /path/to/ivar-compatible.bed and --alignerRefPrefix /path/to/bwa-indexed/ref.fa.
Alternatively you can avoid just the cloning of the scheme repository to remain on a fixed revision of it over time by passing --schemeRepoURL /path/to/own/clone/of/github.com/artic-network/artic-ncov2019. This removes any internet access from the workflow except for the optional upload steps.
nextflow run connor-lab/ncov2019-artic-nf [-profile conda,singularity,docker,slurm,lsf] --nanopolish --prefix "output_file_prefix" --basecalled_fastq /path/to/directory --fast5_pass /path/to/directory --sequencing_summary /path/to/sequencing_summary.txt
nextflow run connor-lab/ncov2019-artic-nf [-profile conda,singularity,docker,slurm,lsf] --medaka --prefix "output_file_prefix" --basecalled_fastq /path/to/directory --fast5_pass /path/to/directory --sequencing_summary /path/to/sequencing_summary.txt
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.
This repo contains both Singularity and Dockerfiles. You can build the Singularity containers locally by running
scripts/build_singularity_containers.sh and use them with
-profile singularity The containers will be available from Docker/Singularityhub shortly.
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.
By default, the pipeline just runs on the local machine. You can specify
-profile slurm to use a SLURM cluster, or
-profile lsf to use an LSF cluster. In either case you may need to also use one of the COG-UK institutional config profiles (phw or sanger), or provide queue names to use in your own config file.
You can use multiple profiles at once, separating them with a comma. This is described in the Nextflow documentation
Common configuration options are set in
conf/base.config. Workflow specific configuration options are set in
conf/illumina.config They are described and set to sensible defaults (as suggested in the nCoV-2019 novel coronavirus bioinformatics protocol)
--outdirsets the output directory.
--bwato swap to bwa for mapping (nanopore only).
--medaka to run these workflows.
--basecalled_fastq should point to a directory created by
guppy_basecaller (if you ran with no barcodes), or
guppy_barcoder (if you ran with barcodes). It is imperative that the following
guppy_barcoder command be used for demultiplexing:
guppy_barcoder --require_barcodes_both_ends -i run_name -s output_directory --arrangements_files "barcode_arrs_nb12.cfg barcode_arrs_nb24.cfg"
The Illumina workflow leans heavily on the excellent ivar for primer trimming and consensus making. This workflow will be updated to follow ivar, as its also in very active development! Use
--illumina to run the Illumina workflow. Use
--directory to point to an Illumina output directory usually coded something like:
<date>_<machine_id>_<run_no>_<some_zeros>_<flowcell>. The workflow will recursively grab all fastq files under this directory, so be sure that what you want is in there, and what you don't, isn't!
Important config options are:
|allowNoprimer||Allow reads that don't have primer sequence? Ligation prep = false, nextera = true|
|illuminaKeepLen||Length of illumina reads to keep after primer trimming|
|illuminaQualThreshold||Sliding window quality threshold for keeping reads after primer trimming (illumina)|
|mpileupDepth||Mpileup depth for ivar|
|ivarFreqThreshold||ivar frequency threshold for variant|
|ivarMinDepth||Minimum coverage depth to call variant|
A script to do some basic COG-UK QC is provided in
bin/qc.py. This currently tests if >50% of reference bases are covered by >10 reads (Illumina) or >20 reads (Nanopore), OR if there is a stretch of more than 10 Kb of sequence without N - setting qc_pass in
<outdir>/<prefix>.qc.csv to TRUE.
bin/qc.py can be extended to incorporate any QC test, as long as the script outputs a csv file a "qc_pass" last column, with samples TRUE or FALSE.
A subdirectory for each process in the workflow is created in
qc_pass_climb_upload subdirectory containing files important for COG-UK is created.