Introduction

nf-core/bacass is a bioinformatics best-practice analysis pipeline for simple bacterial assembly and annotation. The pipeline is able to assemble short reads, long reads, or a mixture of short and long reads (hybrid assembly).

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

Short Read Assembly

This pipeline is primarily for bacterial assembly of next-generation sequencing reads. It can be used to quality trim your reads using Skewer and performs basic sequencing QC using FastQC. Afterwards, the pipeline performs read assembly using Unicycler. Contamination of the assembly is checked using Kraken2 to verify sample purity.

Long Read Assembly

For users that only have Nanopore data, the pipeline quality trims these using PoreChop and assesses basic sequencing QC utilizing NanoPlot and PycoQC. The pipeline can then perform long read assembly utilizing Unicycler, Miniasm in combination with Racon, or Canu. Long reads assembly can be polished using Medaka or NanoPolish with Fast5 files.

Hybrid Assembly

For users specifying both short read and long read (NanoPore) data, the pipeline can perform a hybrid assembly approach utilizing Unicycler, taking the full set of information from short reads and long reads into account.

Assembly QC and annotation

In all cases, the assembly is assessed using QUAST. The resulting bacterial assembly is furthermore annotated using Prokka or DFAST.

Quick Start

1. Install Nextflow (>=21.04.0)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/bacass -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>

   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   Default: Short read assembly with Unicycler, --kraken2db can be any compressed database (.tar.gz/.tgz):

   nextflow run nf-core/bacass -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.tsv --kraken2db "https://genome-idx.s3.amazonaws.com/kraken/k2_standard_8gb_20210517.tar.gz"

   Long read assembly with Miniasm:

   nextflow run nf-core/bacass -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.tsv --assembly_type 'long' --assembler 'miniasm' --kraken2db "https://genome-idx.s3.amazonaws.com/kraken/k2_standard_8gb_20210517.tar.gz"

Documentation

The nf-core/bacass pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

nf-core/bacass was initiated by Andreas Wilm, originally written by Alex Peltzer (DSL1) and rewritten by Daniel Straub (DSL2).

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #bacass channel (you can join with this invite).

Citations

If you use nf-core/bacass for your analysis, please cite it using the following doi: 10.5281/zenodo.2669428

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.