CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences
Year: 2025
Type: article
Abstract: Resistance (R) genes play a critical role in defending against pathogenic infections, and identifying novel R genes is a key component of resistance breeding. However, current computational methods face significant limitations when predicting R genes from ultra-long, highly repetitive genomic sequences. In this study, we propose a deep learning framework, CLAP-HMM, which employs a CNN-LSTM-Attention fusion strategy to extract gene expression patterns from long DNA sequences and accurately predict R genes. Our approach introduces protein function hints as biological priors to guide the HMM module in structural state modeling, enabling precise identification of R gene boundaries. Comparative experiments demonstrate that CLAP-HMM outperforms existing methods in discovering more potential novel R genes, offering promising prospects for uncovering biologically meaningful functional genes and advancing our understanding of disease resistance mechanisms.
Source: BMC Biotechnology
Authors: Liuyan Wang, Yingfan Xu, Xuemei Guan, Shanchun Yan
Institution: Northeast Forestry University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W4417260725