AbstractMotivationIdentifying organellar DNA, such as mitochondrial or plastid sequences, inside a whole genome assembly, remains challenging and requires biological background knowledge. To address this, we developed ODNA based on genome annotation and machine learning to fulfill.ResultsODNA is a software that classifies organellar DNA sequences within a genome assembly by machine learning based on a predefined genome annotation workflow. We trained our model with 829 769 DNA sequences from 405 genome assemblies and achieved high predictive performance (e.g. matthew's correlation coefficient of 0.61 for mitochondria and 0.73 for chloroplasts) on independent validation data, thus outperforming existing approaches significantly.Availability and implementationOur software ODNA is freely accessible as a web service athttps://odna.mathematik.uni-marburg.deand can also be run in a docker container. The source code can be found athttps://gitlab.com/mosga/odnaand the processed data at Zenodo (DOI: 10.5281/zenodo.7506483).