TSSUNet-MB – ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning

Chung En Ni, Duy Phuong Doan, Yen Jung Chiu, Yen Hua Huang*

*此作品的通信作者

研究成果: Article同行評審

摘要

Motivation: Computational promoter prediction (CPP) tools designed to classify prokaryotic promoter regions usually assume that a transcription start site (TSS) is located at a predefined position within each promoter region. Such CPP tools are sensitive to any positional shifting of the TSS in a windowed region, and they are unsuitable for determining the boundaries of prokaryotic promoters. Results: TSSUNet-MB is a deep learning model developed to identify the TSSs of σ70 promoters. Mononucleotide and bendability were used to encode input sequences. TSSUNet-MB outperforms other CPP tools when assessed using the sequences obtained from the neighborhood of real promoters. TSSUNet-MB achieved a sensitivity of 0.839 and specificity of 0.768 on sliding sequences, while other CPP tool cannot maintain both sensitivities and specificities in a compatible range. Furthermore, TSSUNet-MB can precisely predict the TSS position of σ70 promoter-containing regions with a 10-base accuracy of 77.6%. By leveraging the sliding window scanning approach, we further computed the confidence score of each predicted TSS, which allows for more accurately identifying TSS locations. Our results suggest that TSSUNet-MB is a robust tool for finding σ70 promoters and identifying TSSs.

原文English
文章編號107904
期刊Computational Biology and Chemistry
105
DOIs
出版狀態Published - 8月 2023

指紋

深入研究「TSSUNet-MB – ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning」主題。共同形成了獨特的指紋。

引用此