GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species.

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.

SSCRB: Predicting circRNA-RBP Interaction Sites Using a Sequence and Structural Feature-Based Attention Model.

The prediction of interaction sites between circular RNA (circRNA) and RNA binding proteins (RBPs) is crucial for regulating diseases and discovering new treatment approaches. Computational models have been widely used to predict circRNA-RBP interaction sites due to the availability of genome-wide circRNA binding event data. However, efficiently obtaining multi-scale circRNA features to improve prediction accuracy remains a challenging problem. In this study, we propose SSCRB, a lightweight model for predicting circRNA-RBP interaction sites. Our model extracts both sequence and structural features of circRNA and incorporates multi-scale features through the attention mechanism. Furthermore, we develop an ensemble model by combining multiple submodels to enhance predictive performance and generalizability. We evaluate SSCRB on 37 circRNA datasets and compare it with other state-of-the-art methods. The average AUC of SSCRB is 97.66%, demonstrating its efficiency and robustness. SSCRB outperforms other methods in terms of prediction accuracy while requiring significantly fewer computational resources.