Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks.

ABSTRACT: Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m⁶A, m¹A, m¹G, m²G, m⁵C, m

SUBMITTER: Salekin S 

PROVIDER: S-EPMC7710330 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature