NeuroTIS+: An Improved Method for Translation Initiation Site Prediction in Full-Length mRNA Sequence via Structural Information
Wenqiu Xiao, Chao Wei · Preprints.org · 2025
Translation initiation site (TIS) prediction in mRNA sequences constitutes an essential component of transcriptome annotation, playing a crucial role in deciphering gene expression and regulation mechanisms. Numerous computational methods have been proposed and achieved acceptable prediction accuracy. In our previous work, we developed NeuroTIS, a novel method for TIS prediction based on a hybrid dependency network combined with a deep learning framework that explicitly models label dependencies both within coding sequences (CDS), and between CDS and TIS. However, this method has limitations in fully exploiting structural information within mRNA sequences. First, it only captures label dependency within three neighboring codon labels. Second, it neglects the heterogeneity of negative TIS originating from different reading frames, which exhibit distinct coding features in their vicinity. In this paper, under the framework of NeuroTIS, we propose its enhanced version, NeuroTIS+, which allows for more sophisticated codon label dependency modeling via temporal convolution and homogenous feature building through an adaptive grouping strategy. Tests on transcriptome-wide human and mouse datasets demonstrate that the proposed method yields an excellent prediction performance, significantly surpassing existing state-of-the-art methods.