This paper proposes a new algorithm for prophage loci prediction in bacteria. Prophages are defined in Bioinformatics as viral nucleotide sequences that are found intermixed with host nucleotide sequences in bacteria. The proposed algorithm uses machine learning patterns and processing methodologies in order to provide a highly efficient system for loci prediction, thereby reducing the time-space complexity required unlike others of its class. In the training phase, a pattern database is constructed from raw nucleotide sequences of both bacteria and viruses obtained from a training set. In the prediction phase, the aforementioned database is used along with Particle Swarm Optimization (PSO) to predict the probable loci of prophages in a test set of bacterial nucleotide sequences. Testing this method on raw sequences consisting of both partial and complete nucleotide sequences of various bacteria has yielded good results in predicting the loci of prophages in them. This algorithm and connected processes compare favorably in terms of predictive performance with others of its class such as PhiSpy and ProphET, while outperforming others in terms of raw processing speed, suggesting that a data-centric approach can yield comparable results while using a fraction of the resources.

中文翻译：

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细菌基因组的噬菌体基因座预测因子

本文提出了一种新的细菌中原噬菌体位点预测算法。噬菌体在生物信息学中被定义为与细菌中的宿主核苷酸序列混合的病毒核苷酸序列。所提出的算法使用机器学习模式和处理方法，以便为位点预测提供高效的系统，从而降低所需的时空复杂度，这与同类算法不同。在训练阶段，模式数据库由从训练集中获得的细菌和病毒的原始核苷酸序列构建。在预测阶段，上述数据库与粒子群优化 (PSO) 一起用于预测细菌核苷酸序列测试集中的前噬菌体的可能位点。在由各种细菌的部分和完整核苷酸序列组成的原始序列上测试这种方法，在预测它们中原噬菌体的位点方面取得了很好的结果。该算法和连接的过程在预测性能方面与其他同类产品（如 PhiSpy 和 ProphET）相比具有优势，同时在原始处理速度方面优于其他方法，这表明以数据为中心的方法可以在使用一小部分数据的同时产生可比较的结果资源。