College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin, 150040, Heilongjiang, China.
National Institute for Data Science in Health and Medicine, Xiamen University, No. 4221 Xiangannan Road, Xiamen, 361102, Fujian, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae377.
Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage-host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features or gene features, leading to low accuracy. A new computational method, DeePhafier, is proposed to improve classification performance on phage lifestyle. Built by several multilayer self-attention neural networks, a global self-attention neural network, and being combined by protein features of the Position Specific Scoring Matrix matrix, DeePhafier improves the classification accuracy and outperforms two benchmark methods. The accuracy of DeePhafier on five-fold cross-validation is as high as 87.54% for sequences with length >2000bp.
噬菌体是感染细菌细胞的病毒。它们是地球上最多样化的生物实体，在微生物组中发挥着重要作用。根据噬菌体的生活方式，噬菌体可以分为烈性噬菌体和温和噬菌体。对烈性噬菌体和温和噬菌体进行分类，对于进一步了解噬菌体-宿主相互作用至关重要。尽管已经设计了几种用于噬菌体生活方式分类的方法，但它们要么仅考虑序列特征，要么仅考虑基因特征，导致准确性较低。提出了一种新的计算方法 DeePhafier，以提高对噬菌体生活方式的分类性能。通过几个多层自注意力神经网络、一个全局自注意力神经网络以及与位置特异性评分矩阵矩阵的蛋白质特征相结合，DeePhafier 提高了分类准确性，优于两种基准方法。对于长度>2000bp 的序列，DeePhafier 在五重交叉验证中的准确率高达 87.54%。
