利用Transformer从宏基因组数据准确鉴定噬菌体
Accurate identification of bacteriophages from metagenomic data using Transformer
摘要：
动机：噬菌体是感染细菌的病毒。作为微生物群落的关键参与者，它们可以通过感染细菌宿主和介导基因转移来调节微生物组的组成/功能。近年来，宏基因组测序技术可以对来自不同微生物组的所有遗传物质进行测序，已成为发现新噬菌体的流行手段。然而，从宏基因组数据中准确和全面地检测p53仍然是困难的。高多样性/丰度和有限的参考基因组对从宏基因组数据中招募噬菌体片段提出了重大挑战。现有的基于记忆或基于学习的模型对宏基因组数据的召回率或准确率较低。结果：在这项工作中，我们采用了最先进的语言模型，Transformer，进行噬菌体重叠群的上下文嵌入。通过构建蛋白质簇词汇表，我们可以将每个重叠群的蛋白质组成和蛋白质位置输入到Transformer中。Transformer可以使用自注意机制学习蛋白质组织和关联，并预测测试重叠群的标签。我们在多个数据集上严格测试了我们开发的名为PhaMer的工具，这些数据集的难度越来越大，包括高质量的RefSeq基因组、短重叠群、模拟宏基因组数据、模拟宏基因组数据和公共IMG/VR数据集。所有的实验结果表明，PhaMer优于国家的最先进的工具。在真实的宏基因组数据实验中，PhaMer将噬菌体检测的F1评分提高了27%。
英文摘要：
Motivation: Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data. Results: In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins' positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data, and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
