i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites of Drosophila RNA Using Sequence Feature Embeddings

Abstract

5-Hydroxymethylcytosine (5hmC), one of the most important RNA modifications, plays an important role in many biological processes. Accurately identifying RNA modification sites helps understand the function of RNA modification. In this work, we propose a computational method for identifying 5hmC-modified regions using machine learning algorithms. We applied a sequence feature embedding method based on the dna2vec algorithm to represent the RNA sequence. The results showed that the performance of our model is better that of than state-of-art methods. All dataset and source codes used in this study are available at: https://github.com/liu-h-y/5hmC_model.

Keywords: 5-hydroxymethylcytosine; cross-validation; dna2vec; i5hmcVec; machine learning.