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

ABSTRACT:

Background

Repetitive elements contribute a large part of eukaryotic genomes. For example, about 40 to 50% of human, mouse and rat genomes are repetitive. So identifying and classifying repeats is an important step in genome annotation. This annotation step is traditionally performed using alignment based methods, either in a de novo approach or by aligning the genome sequence to a species specific set of repetitive sequences. Recently, Li (Bioinformatics 35:4408-4410, 2019) developed a novel software tool dna-brnn to annotate repetitive sequences using a recurrent neural network trained on sample annotations of repetitive elements.

Results

We have developed the methods of dna-brnn further and engineered a new software tool DeepGRP. This combines the basic concepts of Li (Bi

SUBMITTER: Hausmann F 

PROVIDER: S-EPMC8381506 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature