dnadna: a deep learning framework for population genetics inference - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Journal Articles Bioinformatics Year : 2023

dnadna: a deep learning framework for population genetics inference


Motivation: We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination, and reusability of neural networks designed for population genetic data. Results: dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pretrained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pretrained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. Availability and Implementation: dnadna is a Python (≥ 3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.
Fichier principal
Vignette du fichier
btac765.pdf (1.01 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03352910 , version 1 (23-09-2021)
hal-03352910 , version 2 (17-11-2021)
hal-03352910 , version 3 (04-11-2022)
hal-03352910 , version 4 (05-12-2022)



Théophile Sanchez, Erik Madison Bray, Pierre Jobic, Jérémy Guez, Anne-Catherine Letournel, et al.. dnadna: a deep learning framework for population genetics inference. Bioinformatics, 2023, 39 (1), ⟨10.1093/bioinformatics/btac765⟩. ⟨hal-03352910v4⟩
835 View
499 Download



Gmail Facebook X LinkedIn More