TransposonUltimate

TransposonUltimate: a holistic bundle of tools for transposon identification

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This is the official GitHub page of TransposonUltimate. You will find four pages with powerful tools for transposon identification here:

• Transposon Annotation Tools This page contains conda packaged versions of different transposon annotation tools, including MUSTv2, HelitronScanner, SineFinder, MiteTracker, MiteFinderII, SineScan, TirVish, LtrHarvest, TransposonPSI, TransposonNCBICDD1000.

• Transposon Classifier "RFSB" This page contains the transposon classification module "RFSB" (Random Forest Selective Binary), that can classify any given DNA sequence into a hierarchical taxonomic scheme. Also, you can use this software to train models for other databases on your own.

• Transposon Annotator "reasonaTE" This page contains the transposon annotation pipeline "reasonaTE", that is an ensemble of 13 annotation tools and combines the knowledge of different annotation approaches.

• Transposition Event Detector "deTEct" This page contains the transposition event detection module "deTEct", that uses structural variants discovered by Sniffles and PBSV to match these against transposon annotations. As a result, it allows for the observation of potential transposition events.

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Materials

Please find all related materials here:

• https://cellgeni.cog.sanger.ac.uk/browser.html?shared=transposonultimate/

Please find the TransposonDB as fasta file here:

• https://doi.org/10.5281/zenodo.5484069

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Citations

Please cite our paper if you find TransposonUltimate useful:

Kevin Riehl, Cristian Riccio, Eric A Miska, Martin Hemberg, TransposonUltimate: software for transposon classification, annotation and detection, Nucleic Acids Research, 2022; gkac136, https://doi.org/10.1093/nar/gkac136

@article{riehl2022transposonultimate,
  title={TransposonUltimate: software for transposon classification, annotation and detection},
  author={Riehl, Kevin and Riccio, Cristian and Miska, Eric and Hemberg, Martin},
  journal={Nucleic Acids Research},
  year={2022}
}

Acknowledgements

We would like to thank Sarah Buddle, Simone Procaccia, Fu Xiang Quah and Alexandra Dallaire for their assistance with testing and debugging the software.