Title: Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data. Abstract: We introduce Self-GenomeNet, a novel contrastive self-supervised learning method for nucleotide-level genomic data, which substantially improves the quality of the learned representations and performance compared to the current state-of-the-art deep learning frameworks. To the best of our knowledge, Self-GenomeNet is the first self-supervised framework that learns a representation of nucleotide-level genome data, using domain-specific characteristics. Our proposed method learns and parametrizes the latent space by leveraging the reverse-complement of genomic sequences. During the training procedure, we force our framework to capture semantic representations with a novel context network on top of intermediate features extracted by an encoder network. The network is trained with an unsupervised contrastive loss. Extensive experiments show that our method with self-supervised and semi-supervised settings is able to considerably outperform previous deep learning methods on different datasets and a public bioinformatics benchmark. Moreover, the learned representations generalize well when transferred to new datasets and tasks. The source code of the method and all the experiments are available at supplementary.