Work

Identifying centromeric satellites with dna-brnn

Year: 2019

Type: article

Abstract: SUMMARY: Human alpha satellite and satellite 2/3 contribute to several percent of the human genome. However, identifying these sequences with traditional algorithms is computationally intensive. Here we develop dna-brnn, a recurrent neural network to learn the sequences of the two classes of centromeric repeats. It achieves high similarity to RepeatMasker and is times faster. Dna-brnn explores a novel application of deep learning and may accelerate the study of the evolution of the two repeat classes. AVAILABILITY AND IMPLEMENTATION: https://github.com/lh3/dna-nn.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btz264

Author: Heng Li

Institutions: Harvard University, Dana-Farber Cancer Institute

Language: English

Topic: Chromosomal and Genetic Variations

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Human Genome Research Institute

Awards: G2031934813, Advanced computational methods in analyzing high-throughput sequencing data