Our new article has been accepted in Bioinformatics:

Martin R, Nguyen MK, Lowack N, Heider D: ODNA: Identification of Organellar DNA by Machine Learning. Bioinformatics 2023, in press. (Link)

Abstract

Motivation
Identifying 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.

Results
ODNA is a software that classifies organellar DNA sequences within a genome assembly by machine learning based on a pre-defined genome annotation workflow. We trained our model with 829,769 DNA sequences from 405 genome assemblies and achieved high predictive performance (e.g., MCC of 0.61 for mitochondria and 0.73 for chloroplasts) on independent validation data, thus outperforming existing approaches significantly.

Availability
Our software ODNA is freely accessible as a web service at https://odna.mathematik.uni-marburg.de and can also be run in a docker container. The source code can be found at https://gitlab.com/mosga/odna and the processed data at Zenodo (DOI: 10.5281/zenodo.7506483).