通过深度学习从宏基因组数据中识别病毒
Identifying viruses from metagenomic data by deep learning
摘要：
宏基因组测序的最新发展使得对环境样品中包括病毒在内的微生物基因组进行测序成为可能。从宏基因组数据中识别病毒序列对于下游病毒分析至关重要。现有的基于参考和基于基因同源性的方法在鉴定未知病毒或短病毒序列方面效率不高。在这里，我们开发了一种无参考和无约束的机器学习方法DeepVirginia，用于使用深度学习技术预测宏基因组数据中的病毒序列。DeepVirginia是基于2015年5月之前发现的大量病毒序列进行训练的。在该日期之后的序列上进行评估，DeepVirgin在所有重叠群长度上都优于最先进的方法Virgin。通过添加来自环境元病毒组样本的数百万个纯化病毒序列来扩大训练数据，可以显著提高预测代表性不足的病毒的准确性。将DeepVirginia应用于来自结直肠癌（CRC）患者的真实人类肠道宏基因组样本，鉴定出属于175个箱的51，138个病毒序列。10个箱与癌症状态相关，表明它们可能用于CRC的非侵入性诊断。综上所述，DeepVirginia大大提高了病毒识别的查准率和查全率，将显著加快病毒的发现速度。
英文摘要：
The recent development of metagenomic sequencing makes it possible to sequence microbial genomes including viruses in an environmental sample. Identifying viral sequences from metagenomic data is critical for downstream virus analyses. The existing reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences. Here we have developed a reference-free and alignment-free machine learning method, DeepVirFinder, for predicting viral sequences in metagenomic data using deep learning techniques. DeepVirFinder was trained based on a large number of viral sequences discovered before May 2015. Evaluated on the sequences after that date, DeepVirFinder outperformed the state-of-the-art method VirFinder at all contig lengths. Enlarging the training data by adding millions of purified viral sequences from environmental metavirome samples significantly improves the accuracy for predicting under-represented viruses. Applying DeepVirFinder to real human gut metagenomic samples from patients with colorectal carcinoma (CRC) identified 51,138 viral sequences belonging to 175 bins. Ten bins were associated with the cancer status, indicating their potential use for non-invasive diagnosis of CRC. In summary, DeepVirFinder greatly improved the precision and recall rates of viral identification, and it will significantly accelerate the discovery rate of viruses.
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。
