New Paper: DeepGRP: engineering a software tool for predicting genomic repetitive elements using Recurrent Neural Networks with attention

23. August 2021

Repetitive elements contribute up to 50% of eukaryotic genomes.
So identifying and classifying repeats is an important step in genome
annotation. In collaboration with Fabian Hausmann (Inst. for Medical Systems
Biology, University Medical Center Hamburg-Eppendorf)
the Genome Informatics group published a paper on a new software tool
DeepGRP to annotate repetitive elements. It combines recurrent
neural networks with techniques developed for neural
machine translation. An evaluation on the human genome shows that DeepGRP
achieves 20% improvement in prediction quality and improved running
times, compared to previous similar methods. It is in particular able to
transfer annotations learned from human data to the mouse genome.

For details see https://doi.org/10.1186/s13015-021-00199-0

Hausmann, F., Kurtz, S. DeepGRP: engineering a software tool for predicting genomic repetitive elements using Recurrent Neural Networks with attention. Algorithms Mol Biol 16, 20 (2021)