PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants

Chromatin interactions establish spatial proximity between distant regulatory elements and their target genes, significantly influencing gene expression, and phenotypic traits. In this study, we present a plant chromatin interaction prediction model called PlantCTCIP based on Convolutional Neural Networks and Transformer. PlantCTCIP demonstrated superior performance compared to the conventional models. Specifically, PlantCTCIP improved the average AUC of chromatin interaction predictions by 14.56% across the four species in PPI (proximal promoter interaction) mode. Similarly, PlantCTCIP improved the average AUC of chromatin interaction predictions by 9.6% in the PDI (distal promoter interaction) mode. We constructed genome-wide chromatin interaction maps for four plants (maize, rice, cotton and wheat) through PlantCTCIP, further used the Hi-C experiment to validate correctness of the predicted PPIs and PDIs. Some key motifs that influence chromatin interactions are identified, and they are significantly enriched in expression quantitative trait loci (eQTLs) and open chromatin regions. We also analysed the enrichment and species specificity of the transcription factors (TF) and synergistic network of TFs that affect PPIs and PDIs of four crops. Using cloned genes (ZmRAVL1, ZmRPG, ZmRap2.7 and GaFZ) of maize and cotton as examples, PlantCTCIP can assist in identifying target genes regulated by distal elements and mining functional sites combined with chromatin conformation capture (3C) experiments. This research helps to analyse the regulatory mechanism of gene expression and provides novel perspectives for intelligent design breeding of diverse crops. PlantCTCIP is available at http://www.plantctcip.com.