A New Genetic Tool for Predicting Regulatory Sequences

 

    A project conducted by members of the UC San Diego Department of Medicine resulted in the creation of EUGENe, a tool designed to assist in the prediction of regulatory sequences. The team lists a lack of deep-learning methods for regulatory genetics that follow the FAIR(findable, accessible, interoperable, reusable) principle, and created EUGENe to fill that gap in technology.

    The team states that EUGENe is  "a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses." The team discusses the lack of software that can effectively tackle the problems faced in deep-learning related genetics and goes into detail about the failures that each of the current major packages has in regards to a comprehensive package that can handle an end-to-end workflow while integrating with other available packages.

Article on EUGENe

Klie, A., Laub, D., Talwar, J.V. et al. Predictive analyses of regulatory sequences with EUGENe. Nat Comput Sci 3, 946–956 (2023). https://doi.org/10.1038/s43588-023-00544-w

Article on Janggu, a major software package that handles deep-learning in genomics

Kopp, W., Monti, R., Tamburrini, A. et al. Deep learning for genomics using Janggu. Nat Commun 11, 3488 (2020). https://doi.org/10.1038/s41467-020-17155-y