Arriba: Fast and accurate gene fusion detection from RNA-Seq data

[uhrigs]

Dear all,

We developed an algorithm called "Arriba" to detect gene fusions from RNA-Seq data of tumor samples. It is based on the ultrafast STAR aligner (https://github.com/alexdobin/STAR) and the post-alignment runtime is typically just ~2 minutes. Hence, fusion detection comes at virtually no cost, since the alignment of FastQ reads is a task that needs to be done anyway in a typical RNA-Seq workflow.

But Arriba is not only fast, it is also very accurate: It is currently the best-performing algorithm in the ongoing ICGC-TCGA DREAM SMC Challenge about gene fusion algorithms (final results pending):

https://www.synapse.org/#!Synapse:syn2813589/wiki/423306

Some more highlights:
- ability to detect intergenic and intronic breakpoints
- ability to detect exon duplications/inversions
- utilization of structural variants obtained from whole-genome sequencing
- filtering of transcript variants observed in healthy tissue
- comprehensive manual available at http://arriba.readthedocs.io/
- simple installation routine; especially, if you already use STAR

We would be glad, if you could give it a try, and are happy to receive feedback!
Please visit the homepage to download the code or in case you need help:

https://github.com/suhrig/arriba/

Best regards,
Sebastian

[pli]

Hi Sebastian,

has, or will this method be published? Would be nice. Cheers,

P

[uhrigs]

Yes, the method will certainly be published. I have just started working on the manuscript. Stay tuned ...

[uhrigs]

We are happy to announce that Arriba won first place in the DREAM SMC-RNA Challenge! The final results can be viewed here (requires a free Synapse account): https://www.synapse.org/#!Synapse:sy...89/wiki/588511 As a result, Arriba will be presented at the DREAM Challenge satellite workshop of the RECOMB conference in Washington, D.C. beginning of next month.

In addition, since our first announcement on this forum a year ago, many improvements have been made to Arriba:

- streamlined workflow, which makes Arriba even faster and easier to implement
- installation via Docker, Singularity, and Bioconda
- automatic generation of publication-quality figures
- prediction of peptide sequences and retained protein domains
- CRAM support

[uhrigs]

Version 2 of our gene fusion detection algorithm Arriba is available. It comes with a number of new features and enhancements:

- detect viral integration sites
- detect fusions supported by multi-mapping reads (e.g., CIC-DUX4, NPM1-ALK)
- detect internal tandem duplications (e.g., FLT3, BCOR, ERBB2)
- support for mouse (mm10)
- more comprehensive annotation
- speed improvements
- accuracy enhancements

As usual, the code is available on GitHub: https://github.com/suhrig/arriba/releases

Documentation and installation instructions are available on ReadTheDocs: https://arriba.readthedocs.io/en/latest/quickstart/

[anjukanda]

Hi Sebastian,

I have recently installed Arriba v 2.1.0 and struggling with the statistics about the number of supporting reads in my pdfs that are being generated. Could you please suggest me what to do in order to get the number of split reads in gene1 and no of split reads in gene 2?

Thanks in advance!
AK

[uhrigs]

Hi Anju,

As discussed via mail, since Arriba version 2, the split read counts are not reported separately for gene1 and gene2 anymore in the visualization PDF. This change was made to be compatible with STAR-Fusion output, which does not report the numbers separately for gene1 and gene2.

If you have further questions, feel free to reach out to me via mail or the issue tracker on GitHub: https://github.com/suhrig/arriba/issues

Kind regards,
Sebastian

[anjukanda]

Hi Sebastian,

Thank you for your response. I wasn't sure whether you would reply to my email. Hencewhy I tried on seqanswers. I have send the job for running and it still in the queue. I will let you know when its done on the email.

Thanks again!

Best regards,
Anju

[uhrigs]

We are proud to announce that our manuscript about Arriba has been published in this month's issue of the Genome Research journal. From now on, please cite the following article if you use Arriba for published research:

Sebastian Uhrig, Julia Ellermann, Tatjana Walther, Pauline Burkhardt, Martina Fröhlich, Barbara Hutter, Umut H. Toprak, Olaf Neumann, Albrecht Stenzinger, Claudia Scholl, Stefan Fröhling and Benedikt Brors: Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Research. March 2021 31: 448-460; Published in Advance January 13, 2021. doi: 10.1101/gr.257246.119

[uhrigs]

After almost a year of further development of enhancements, new features, and bug fixes, the next version of our gene fusion detection tool Arriba is finally out (version 2.2.0). The code and user manual are available on Github: https://github.com/suhrig/arriba/

The most notable enhancements are:

improved detection of viruses and viral integration sites
improved detection of internal tandem duplications
support for mm39/GRCm39
utility scripts which facilitate common tasks related to fusion detection
polishing of fusion visualizations

More details can be found in the release notes: https://github.com/suhrig/arriba/releases

[JonasBehr]

Hi uhrigs, thanks for sharing updates on you fusion detection tool here. I was wondering if there is any official nomenclature regarding gene fusions. I am only aware of this: https://www.nature.com/articles/s41375-021-01436-6 , but this is only on gene level like EML4::ALK. Intuitively it is also quite clear what EML4-exon13::ALK-exon20 means. But what would such a description technically mean? "BAG4-intron 1::FGFR1-intron 1" Would be really nice to agree on a nomenclature here and have a proper definition for it...

[uhrigs]

Hi JonasBehr

Standardization of the nomenclature is in preparation. The Variant Interpretation for Cancer Consortium (VICC) is working on it. You can find links to related resources and discussions here: https://cancervariants.org/projects/fusions/

Regards,
Sebastian