Sequencing analysis pipeline (aqueduct) for validating plasmids and DNA assembly constructs, using long reads.

Install Nextflow and Docker.

Pull the Nextflow pipeline:

nextflow pull edinburgh-genome-foundry/Sequeduct -r v0.3.0

Pull the Docker image that contains the required software (requires access to EGF's container repo):

docker pull ghcr.io/edinburgh-genome-foundry/sequeduct:0.3.0

Alternatively, build the image locally from the cloned repo:

docker build . -f containers/Dockerfile --tag sequeduct_local

Create a directory for your project and copy (or link) the FASTQ directories from your Nanopore run (e.g. into fastq). Specify this together with a sample sheet in your commands:

# Preview
nextflow run edinburgh-genome-foundry/Sequeduct -r v0.3.0 -entry preview --fastq_dir='fastq_pass' \
    --reference_dir='genbank' \
    --sample_sheet='sample_sheet.csv' \
    -profile docker
# Analysis
nextflow run edinburgh-genome-foundry/Sequeduct -r v0.3.0 -entry analysis --fastq_dir='fastq_pass' \
    --reference_dir='genbank' \
    --sample_sheet='sample_sheet.csv' \
    --projectname='EGF project' \
    -profile docker
# Review
nextflow run edinburgh-genome-foundry/Sequeduct -r v0.3.0 -entry review --reference_dir='genbank' \
    --results_csv='results_sheet.csv' \
    --projectname='EGF project review' \
    --all_parts='parts_fasta/part_sequences.fasta' \
    --assembly_plan='assembly_plan.csv' \
    -profile docker
# De novo assembly
nextflow run edinburgh-genome-foundry/Sequeduct -r v0.3.0 -entry assembly --fastq_dir='fastq_pass' \
    --results_csv='assembly_sheet.csv' \
    -profile docker 

The above commands each output a directory within a created results directory. Similarly, Nextflow creates and uses a directory named work, so ensure that your project directory doesn't have a directory with the same name. Specify revision of the project with -r (a git branch or tag), and choose a configuration profile (with -profile). Profiles are specified in the Nextflow config files. The Review pipeline utilises the output files of the Analysis pipeline, but otherwise the pipelines are independent. Please find example sheets in the examples directory.

A more detailed example and demonstration data are available at the Sequeduct demo site.

Use -with-docker sequeduct_local to use a locally built Docker image (instead of -profile docker).

For simplicity, the names in the sample sheet are used for finding the reference Genbank files, therefore sample names must match filenames with a ".gb" extension.

Note that canu v2.2 requires minimum 100 reads, otherwise it returns an error. A fix has been posted, but it's not released yet.

For convenience, a script is included to collect plot files from the result directories (bin/collect_plots.py).

The pipeline was designed to work with data from one or more barcodes (FASTQ subdirectories). It has been tested on a desktop machine running Ubuntu 20.04.6 LTS (Memory: 15.5 GiB; CPU: Intel® Core™ i5-6500 CPU @ 3.20GHz × 4), and confirmed to work with up to 96 barcodes. The largest tested dataset was 1.5 GB Nanopore FASTQ data, resulting in 1.1 GB filtered data (100k filtered reads) with up to 55 MB individual filtered FASTQ files (i.e. per sample). If the dataset is much larger, then it may return an error at the variant call or another step. A recommended solution is to increase the quality cutoff (with parameter --quality_cutoff), and optionally the minimum length cutoff (--min_length), to work with fewer but better reads.

Copyright 2021 Edinburgh Genome Foundry, University of Edinburgh

Sequeduct was written at the Edinburgh Genome Foundry by Peter Vegh, and is released under the GPLv3 license.