The Organellar DNA Detector (ODNA) can identify mitochondrially or plastid DNA sequences inside genome assembly files. Technically, it annotates multiple genetic features and performs machine learning models to classify whether the sequence originated from organellar DNA.
The assembly genome annotation will be performed internally with a minimalized version of the Modular Open-Source Genome Annotator (MOSGA), which is optimized in terms of execution time and pre-selected options.
The source code is freely available on Gitlab.com and processed data for machine learning and comparison on Zenodo.com (DOI: 10.5281/zenodo.7506483). We recommend building a new docker container from the available Dockerfile in the linked Gitlab repository. For any questions or comments, please contact us: roman.martin@uni-marburg.de. We are happy to receive feedback.
Whenever you use ODNA please cite us:
Roman Martin 
,
Minh Kien Nguyen ,
Nick Lowack ,
Dominik Heider
(2023). ODNA: Identification of Organellar DNA by Machine Learning. BioRxiv 2023.01.10.523051 . doi: 10.1101/2023.01.10.523051.
Roman Martin ,
Hagen Dreßler ,
Georges Hattab ,
Thomas Hackl ,
Matthias Fischer ,
Dominik Heider
(2021). MOSGA 2: Comparative genomics and validation tools. Computational and Structural Biotechnology Journal. 19. 5504-5509. doi: 10.1016/j.csbj.2021.09.024.
This ODNA instance is hosted on our demonstration with 10 AMD Ryzen threads provided by the University of Marburg.
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