# ![nf-core/rnafusion](docs/images/nf-core-rnafusion_logo_light.png#gh-light-mode-only) ![nf-core/rnafusion](docs/images/nf-core-rnafusion_logo_dark.png#gh-dark-mode-only)

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## Introduction

**nf-core/rnafusion** is a bioinformatics best-practice analysis pipeline for RNA sequencing analysis pipeline with curated list of tools for detecting and visualizing fusion genes.

The pipeline is built using [Nextflow](https://www.nextflow.io), 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](https://www.nextflow.io/docs/latest/dsl2.html) 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](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

> **IMPORTANT: conda is not supported currently.** Run with singularity or docker.

> GRCh38 is the only supported reference

| Tool                                                      |  Single-end reads  | Version  |
| --------------------------------------------------------- | :----------------: | :------: |
| [Arriba](https://github.com/suhrig/arriba)                |        :x:         | `2.2.1`  |
| [FusionCatcher](https://github.com/ndaniel/fusioncatcher) | :white_check_mark: |  `1.33`  |
| [Fusion-report](https://github.com/matq007/fusion-report) |         -          | `2.1.5`  |
| [Pizzly](https://github.com/pmelsted/pizzly)              |        :x:         | `0.37.3` |
| [Squid](https://github.com/Kingsford-Group/squid)         |        :x:         |  `1.5`   |
| [STAR-Fusion](https://github.com/STAR-Fusion/STAR-Fusion) | :white_check_mark: | `1.10.1` |

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](https://nf-co.re/rnafusion/results).

In rnafusion the full-sized test includes reference building and fusion detection. The test dataset is taken from [here](https://github.com/nf-core/test-datasets/tree/rnafusion/testdata/human).

## Pipeline summary

#### Build references

`--build_references` triggers a parallel workflow to build all references

1. Download ensembl fasta and gtf files
2. Create STAR index
3. Download arriba references
4. Download fusioncatcher references
5. Download pizzly references (kallisto index)
6. Download and build STAR-fusion references
7. Download fusion-report DBs

#### Main workflow

1. Input samplesheet check
2. Concatenate fastq files per sample
3. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))
4. Arriba subworkflow
   - [STAR](https://github.com/alexdobin/STAR) alignment
   - [Samtool](https://github.com/samtools/samtools) sort
   - [Samtool](https://github.com/samtools/samtools) index
   - [Arriba](https://github.com/suhrig/arriba) fusion detection
   - [Arriba](https://github.com/suhrig/arriba) visualisation
5. Pizzly subworkflow
   - [Kallisto](https://pachterlab.github.io/kallisto/) quantification
   - [Pizzly](https://github.com/pmelsted/pizzly) fusion detection
6. Squid subworkflow
   - [STAR](https://github.com/alexdobin/STAR) alignment
   - [Samtools view](http://www.htslib.org/): convert sam output from STAR to bam
   - [Samtools sort](http://www.htslib.org/): bam output from STAR
   - [Squid](https://github.com/Kingsford-Group/squid) fusion detection
   - [Squid](https://github.com/Kingsford-Group/squid) annotate
7. STAR-fusion subworkflow
   - [STAR](https://github.com/alexdobin/STAR) alignment
   - [STAR-fusion](https://github.com/STAR-Fusion/STAR-Fusion) fusion detection
8. Fusioncatcher subworkflow
   - [FusionCatcher](https://github.com/ndaniel/fusioncatcher) fusion detection
9. Fusion-report subworkflow
   - Merge all fusions detected by the different tools
   - [Fusion-report](https://github.com/matq007/fusion-report)
10. FusionInspector subworkflow
    - [FusionInspector](https://github.com/FusionInspector/FusionInspector)
11. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))
12. QC for mapped reads ([`QualiMap: BAM QC`](https://kokonech.github.io/qualimap/HG00096.chr20_bamqc/qualimapReport.html))
13. Index mapped reads ([samtools index](http://www.htslib.org/))
14. Collect metrics ([`picard CollectRnaSeqMetrics`](https://gatk.broadinstitute.org/hc/en-us/articles/360037057492-CollectRnaSeqMetrics-Picard-) and ([`picard MarkDuplicates`](https://gatk.broadinstitute.org/hc/en-us/articles/360037052812-MarkDuplicates-Picard-))

## Quick Start

1. Install [`Nextflow`](https://www.nextflow.io/docs/latest/getstarted.html#installation) (`>=21.10.3`)

2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) (you can follow [this tutorial](https://singularity-tutorial.github.io/01-installation/)), [`Podman`](https://podman.io/), [`Shifter`](https://nersc.gitlab.io/development/shifter/how-to-use/) or [`Charliecloud`](https://hpc.github.io/charliecloud/) for full pipeline reproducibility _(you can use [`Conda`](https://conda.io/miniconda.html) both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles))_.

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

   ```console
   nextflow run nf-core/rnafusion -profile test,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](https://github.com/nf-core/configs#documentation) 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`](https://nf-co.re/tools/#downloading-pipelines-for-offline-use) command to download images first, before running the pipeline. Setting the [`NXF_SINGULARITY_CACHEDIR` or `singularity.cacheDir`](https://www.nextflow.io/docs/latest/singularity.html?#singularity-docker-hub) 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`](https://www.nextflow.io/docs/latest/conda.html) settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

```console
nextflow run nf-core/rnafusion --input samplesheet.csv --outdir <OUTDIR> --genome GRCh38 --all -profile <docker/singularity/podman/shifter/charliecloud/institute>
```

> Note that paths need to be absolute and that runs with conda are not supported.

## Documentation

The nf-core/rnafusion pipeline comes with documentation about the pipeline [usage](https://nf-co.re/rnafusion/usage), [parameters](https://nf-co.re/rnafusion/parameters) and [output](https://nf-co.re/rnafusion/output).

## Credits

nf-core/rnafusion was written by Martin Proks ([@matq007](https://github.com/matq007)), Maxime Garcia ([@maxulysse](https://github.com/maxulysse)) and Annick Renevey ([@rannick](https://github.com/rannick))

We thank the following people for their help in the development of this pipeline:

- [Phil Ewels](https://github.com/ewels)
- [Rickard Hammarén](https://github.com/Hammarn)
- [Alexander Peltzer](https://github.com/apeltzer)
- [Praveen Raj](https://github.com/praveenraj2018)

## Contributions and Support

If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).

For further information or help, don't hesitate to get in touch on the [Slack `#rnafusion` channel](https://nfcore.slack.com/channels/rnafusion) (you can join with [this invite](https://nf-co.re/join/slack)).

## Citations

If you use nf-core/rnafusion for your analysis, please cite it using the following doi: [10.5281/zenodo.3946477](https://doi.org/10.5281/zenodo.3946477)

An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](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](https://dx.doi.org/10.1038/s41587-020-0439-x).
