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 FastP and performs basic sequencing QC using FastQC. Afterwards, the pipeline performs read assembly using Unicycler. Contamination of the assembly is checked using Kraken2 and Kmerfinder 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. Contamination of the assembly is checked using Kraken2 and Kmerfinder to verify sample purity.

The pipeline can then perform long read assembly utilizing Unicycler, Miniasm in combination with Racon, Canu or Flye by using the Dragonflye(*) pipeline. Long reads assembly can be polished using Medaka or NanoPolish with Fast5 files.


Dragonflye is a comprehensive pipeline designed for genome assembly of Oxford Nanopore Reads. It facilitates the utilization of Flye (default), Miniasm, and Raven assemblers, along with Racon (default) and Medaka polishers. For more information, visit the Dragonflye GitHub repository.

Hybrid Assembly

For users specifying both short read and long read (NanoPore) data, the pipeline can perform a hybrid assembly approach utilizing Unicycler (short read assembly followed by gap closing with long reads) or Dragonflye (long read assembly followed by polishing with short reads), 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, Bakta or DFAST.

If Kmerfinder is invoked, the pipeline will group samples according to the Kmerfinder-estimated reference genomes. Afterwards, two QUAST steps will be carried out: an initial (‘general’) QUAST of all samples without reference genomes, and subsequently, a ‘by reference genome’ QUAST to aggregate samples with their reference genomes.


This scenario is supported when Kmerfinder analysis is performed only.



If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:


ID      R1                            R2                            LongFastQ                    Fast5    GenomeSize
shortreads      ./data/S1_R1.fastq.gz       ./data/S1_R2.fastq.gz       NA                            NA      NA
longreads       NA                          NA                          ./data/S1_long_fastq.gz      ./data/FAST5  2.8m
shortNlong      ./data/S1_R1.fastq.gz       ./data/S1_R2.fastq.gz       ./data/S1_long_fastq.gz      ./data/FAST5  2.8m

Each row represents a fastq file (single-end) or a pair of fastq files (paired end).

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 ""

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 ""
nextflow run nf-core/bacass \
  -profile <docker/singularity/.../institute> \
  --input samplesheet.tsv \
  --outdir <OUTDIR>

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters;

see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.


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

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).


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 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.