A Finetuning Deep Learning Framework for Pan-Species Promoters Identification With Pseudo Time Series Analysis on Time and Frequency Space.

Abstract

Promoters are genomic sequences harbouring specific motifs, such as the TATA- box for eukaryotes and the Pribnow box for prokaryotes, which are known as regulatory elements. Accurate identification of these regulatory elements is essential for deciphering transcriptional regulation mechanisms. However, the heterogeneity of promoters across different species poses a significant challenge in this task. In our study, we introduce two deep learning methods, ProTriCNN and TransPro, designed for promoter identification. Based on promoter representation, ProTriCNN treats promoters as pseudo-time series, utilizing this approach to capture the intricate heterogeneity of promoter elements. TransPro is a ProTriCNN-based Fine-tuning framework to improve identification performance across different species. TransPro lies in utilizes elements and species evolutionary trees to represent the locality difference between source and target species across various levels and time-frequency space, respectively. With systematic experiments using real datasets, we demonstrate that ProTriCNN outperfroms state-of-the-art methods across all species, achieving an average accuracy improvement of 2.1% and a 20% enhancement in the Matthews coefficient. TransPro further attains accuracy improvement of the highest 8% and a 25% enhancement in the Matthews coefficient compared to ProTriCNN.