Pipeline for the identification of extrachromosomal circular DNA (ecDNA) from Circle-seq, WGS, and ATAC-seq data that were generated from cancer and other eukaryotic cells.
nf-core/circdna is a bioinformatics best-practice analysis pipeline for the identification of extrachromosomal circular DNAs (ecDNAs) in eukaryotic cells. The pipeline is able to process WGS, ATAC-seq data or Circle-Seq data generated from short-read sequencing technologies. Depending on the input data and selected analysis branch, nf-core/circdna is able to identify various types of ecDNAs. This includes the detection of smaller ecDNAs, often referred to as eccDNAs or microDNAs, as well as larger ecDNAs that exhibit amplification. These analyses are facilitated through the use of prominent software tools that are widely recognized in the field of ecDNA or circular DNA research.
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.
- Merge re-sequenced FastQ files (
- Read QC (
- Adapter and quality trimming (
- Map reads using BWA-MEM (
- Sort and index alignments (
- Choice of multiple ecDNA identification routes
Circle_finderDoes not use filtered BAM file, specificied with —keep_duplicates false
- Identification of circular amplicons
- De Novo Assembly of ecDNAs
- Present QC for raw reads (
A graphical view of the pipeline and its diverse branches can be seen below.
First, prepare a samplesheet with your input data that looks as follows:
FASTQ input data:
BAM input data:
Each row represents a pair of fastq files (paired end) or a single bam file generated from paired-end reads.
Now, you can run the pipeline using:
Test AmpliconSuite-Pipeline with a test data-set
To test the correct installation of the pipeline and the use of AmpliconArchitect inside the AmpliconSuite-Pipeline, a small WGS data set is uploaded to AWS and can be downloaded and used with the parameter
-profile test_AA_local. You just need to specify your local paths to the
aa_data_repo and the
mosek_license_dir. See AmpliconSuite-Pipeline for information about the data repository and the Mosek license. To note, the Mosek license file needs to be named
mosek.lic inside the
You can test the pipeline using:
Available ecDNA identifiers
Please specify the parameter
circle_identifier depending on the pipeline branch used for circular DNA 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_finderuses Circle_finder >
circexplorer2uses CIRCexplorer2 >
circle_map_realignuses Circle-Map Realign >
circle_map_repeatsuses Circle-Map Repeats for the identification of repetetive ecDNA
Identification of amplified ecDNAs with WGS data
De novo assembly of ecDNAs with Circle-seq data
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;
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/circdna was originally written by Daniel Schreyer, University of Glasgow, Institute of Cancer Sciences, Peter Bailey Lab.
We thank the following people for their extensive assistance in the development of this pipeline:
- Sébastian Guizard: Review and Discussion of Pipeline
- Alex Peltzer: Code Review
- Phil Ewels: Help in setting up the pipeline repository and directing the pipeline development
- nf-core community: Answering all nextflow and nf-core related questions
- Peter Bailey: Discussion of Software and Pipeline Architecture
This pipeline has been developed by Daniel Schreyer as part of the PRECODE project. PRECODE received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No 861196.
Contributions and Support
If you would like to contribute to this pipeline, please see the contributing guidelines.
If you use nf-core/circdna for your analysis, please cite it using the following doi: 10.5281/zenodo.6685250
An extensive list of references for the tools used by the pipeline can be found in the
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.