GenoJEPA-Pretraining

This dataset provides the pre-training resources used for GenoJEPA, a genomic representation learning framework based on joint-embedding predictive architecture.

GenoJEPA learns semantic representations of DNA sequences by shifting the optimization target from nucleotide-level reconstruction to latent-space semantic alignment. The pre-training data is used to construct global and local sequence views for self-supervised genomic representation learning.

Contents

This resource contains pre-training data and metadata for GenoJEPA, including processed genomic sequence resources used by the pre-training pipeline.

For detailed preprocessing scripts, model configuration, and training commands, please refer to the GitHub repository.

Citation

If you use this resource, please cite GenoJEPA:

@article{
  title   = {From nucleotides to semantics: genomic representation learning via joint-embedding predictive architecture},
  author  = {Wang, Chengsen and Qi, Qi and Sun, Haifeng and Zhuang, Zirui and He, Bo and Liu, Siying and Liao, Jianxin and Wang, Jingyu},
  journal = {bioRxiv},
  year    = {2026}
}