Deep Effective k-mer representation learning for polyadenylation signal prediction via co-occurrence embedding

The polyadenylation process begins as the transcription of a gene terminates, and polyadenylation signal prediction by the genomic sequence is the key to understanding the mechanism of mRNA metabolism. Computational algorithms are widely proposed for polyadenylation signal prediction, but they mainly rely on sequences’ statistical and compositional characteristics. In this work, we conduct adaptively context-aware feature learning for polyadenylation signal prediction via a deep model and co-occurrence embedding. Specifically, we devise an activation function with dynamic parameters to control the scale of features channel-wisely for the positive and negative part value of neuron outputs, and then use the statistical information of k -mer occurrences as features of polyadenylation data, which is pre-trained by an unsupervised learning algorithm. Next, by combining multiscale convolutions with adaptive coefficients, long short-term memory networks jointly extract spatial and temporal contextual patterns. The patterns are fused to obtain contextual information by an identity skip connection and addition strategy. Experimental results demonstrate that our proposed model achieves more remarkable performance than state-of-the-art methods across polyadenylation signal benchmarks, and an ablation study shows the effectiveness of the model, and k -mer embedding is a good feature representation for polyadenylation signals.