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

uhrigs · 02-15-2018, 12:53 PM

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:sy...89/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 · 02-21-2018, 02:56 AM

Hi Sebastian,

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

P

uhrigs · 02-21-2018, 02:37 PM

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

uhrigs · 04-18-2019, 06:24 AM

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 · 11-12-2020, 08:41 AM

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/

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02-15-2018, 12:53 PM	#1
uhrigs Junior Member Location: Heidelberg Join Date: Feb 2018 Posts: 4	Arriba: Fast and accurate gene fusion detection from RNA-Seq data 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:sy...89/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

02-21-2018, 02:56 AM	#2
pli Junior Member Location: Holland Join Date: Jan 2013 Posts: 2	Hi Sebastian, has, or will this method be published? Would be nice. Cheers, P

02-21-2018, 02:37 PM	#3
uhrigs Junior Member Location: Heidelberg Join Date: Feb 2018 Posts: 4	Yes, the method will certainly be published. I have just started working on the manuscript. Stay tuned ...

04-18-2019, 06:24 AM	#4
uhrigs Junior Member Location: Heidelberg Join Date: Feb 2018 Posts: 4	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

11-12-2020, 08:41 AM	#5
uhrigs Junior Member Location: Heidelberg Join Date: Feb 2018 Posts: 4	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/