PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks

文献类型： 外文期刊

第一作者： Wang, Ji

作者： Wang, Ji;Zhang, Han;Zeng, Tong;Ai, Xiaohua;Wu, Keliang;Chen, Nanzhu

作者机构：

关键词： enhancer; convolutional neural networks; sequence classification

期刊名称：ANIMALS （ 影响因子：3.0； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2023 年 13 卷 18 期

页码：

收录情况： SCI

摘要： Simple Summary This study develops a deep learning framework called PorcineAI-enhancer to predict enhancer sequences in pigs. Enhancers play a key role in regulating gene expression. However, identifying enhancers experimentally remains challenging. This study constructs a reliable pig enhancer dataset by integrating multiple data sources. The PorcineAI-enhancer model employs convolutional neural networks to extract features from DNA sequences and classify them into enhancers or non-enhancers. Evaluation on an independent test set shows the model has excellent performance. It also demonstrates strong predictive capability on tissue-specific enhancers from human and pig. This tool facilitates research on gene regulation mechanisms in pigs. It provides valuable resources to understand complex traits related to agriculture and biomedicine.Abstract Understanding the mechanisms of gene expression regulation is crucial in animal breeding. Cis-regulatory DNA sequences, such as enhancers, play a key role in regulating gene expression. Identifying enhancers is challenging, despite the use of experimental techniques and computational methods. Enhancer prediction in the pig genome is particularly significant due to the costliness of high-throughput experimental techniques. The study constructed a high-quality database of pig enhancers by integrating information from multiple sources. A deep learning prediction framework called PorcineAI-enhancer was developed for the prediction of pig enhancers. This framework employs convolutional neural networks for feature extraction and classification. PorcineAI-enhancer showed excellent performance in predicting pig enhancers, validated on an independent test dataset. The model demonstrated reliable prediction capability for unknown enhancer sequences and performed remarkably well on tissue-specific enhancer sequences.The study developed a deep learning prediction framework, PorcineAI-enhancer, for predicting pig enhancers. The model demonstrated significant predictive performance and potential for tissue-specific enhancers. This research provides valuable resources for future studies on gene expression regulation in pigs.

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