Selene: a PyTorch-based deep learning library for sequence data

Kathleen M. Chen, Evan M. Cofer, Jian Zhou, Olga G. Troyanskaya

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


To enable the application of deep learning in biology, we present Selene (, a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for any biological sequence data. We demonstrate on DNA sequences how Selene allows researchers to easily train a published architecture on new data, develop and evaluate a new architecture, and use a trained model to answer biological questions of interest.

Original languageEnglish (US)
Pages (from-to)315-318
Number of pages4
JournalNature Methods
Issue number4
StatePublished - Apr 1 2019

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Biochemistry
  • Biotechnology
  • Cell Biology


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