Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification...

N⁶-methyladenosine (m⁶A) plays a crucial role in enriching RNA functional and genetic information, and the identification of m⁶A modification sites is therefore an important task to promote the understanding of RNA epigenetics. In the identification process, current studies are mainly concentrated on capturing the short-range dependencies between adjacent nucleotides in RNA sequences, while ignoring the impact of long-range dependencies between non-adjacent nucleotides for learning high-quality representation of RNA sequences. In this work, we propose an end-to-end prediction model, called m⁶ASLD, to improve the identification accuracy of m⁶A modification sites by capturing the short-range and long-range dependencies of nucleotides. Specifically, m⁶ASLD first encodes the type and position information of nucleotides to construct the initial embeddings of RNA sequences. A self-correlation map is then generated to characterize both short-range and long-range dependencies with a designed map generating block for each RNA sequence. After that, m⁶ASLD learns the global and local representations of RNA sequences by using a graph convolution process and a designed dependency searching block respectively, and finally achieves its identification task under a joint training scheme. Extensive experiments have demonstrated the promising performance of m⁶ASLD on 11 benchmark datasets across several evaluation metrics.