Introduction

nf-core/circdna is a bioinformatics best-practice analysis pipeline for the identification of circular DNAs in eukaryotic cells. The pipeline is able to process WGS, ATAC-seq data or Circle-Seq data generated from short-read sequencing technologies.

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

1. Merge re-sequenced FastQ files (cat)
2. Read QC (FastQC)
3. Adapter and quality trimming (Trim Galore!)
4. Map reads using BWA-MEM (BWA)
5. Sort and index alignments (SAMtools)
6. Choice of multiple circular DNA identification routes
   1. Circle-Map ReadExtractor -> Circle-Map Realign
   2. Circle-Map ReadExtractor -> Circle-Map Repeats
   3. CIRCexplorer2
   4. Samblaster -> Circle_finder
   5. Identification of circular amplicons AmpliconArchitect
   6. DeNovo Assembly of circular DNAs Unicycler -> Minimap2
7. Present QC for raw reads (MultiQC)

Functionality Overview

A graphical view of the pipeline and its diverse branches can be seen below.

Quick Start

1. Install Nextflow (>=21.10.3)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines 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/circdna -profile test,YOURPROFILE --outdir <OUTDIR>

   To test the ampliconarchitect branch functionality, please use the following command:

   nextflow run nf-core/circdna -profile test_AA,YOURPROFILE --outdir <OUTDIR>

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • 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, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you 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!

   nextflow run nf-core/circdna --input samplesheet.csv --outdir <OUTDIR> --genome GRCh38 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Available circular DNA identifiers

Please specify the parameter circle_identifier depending on the pipeline branch used for ecDNA identifaction. Please note that some branches/software are only tested with specific NGS data sets.

Identification of putative ecDNA junctions with ATAC-seq or Circle-seq data

circle_finder uses Circle_finder

circexplorer2 uses CIRCexplorer2

circle_map_realign uses Circle-Map Realign

circle_map_repeats uses Circle-Map Repeats for the identification of repetetive circular DNA

Identification of circular amplicons with WGS data

ampliconarchitect uses AmpliconArchitect

De novo assembly of circular DNAs with Circle-Seq data

unicycler uses Unicycler for de novo assembly of circular DNAs and Minimap2 for accurate mapping of the identified circular sequences.

Example Usage

The user can specify either one or multiple circle_identifier in a comma-separated string (see below).

nextflow run nf-core/circdna --input samplesheet.csv --outdir <OUTDIR> --genome GRCh38 -profile docker --circle_identifier circle_map_realign,unicycler

Documentation

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

Credits

Main authors:

• Daniel Schreyer, University of Glasgow, Institute of Cancer Sciences, Peter Bailey Lab

Funding

Daniel Schreyer received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No 861196 designated for PRECODE.

nf-core/circdna was originally written by Daniel Schreyer.

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 #circdna channel (you can join with this invite).

Citations

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.