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ELM: enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset

机译:ELM:用于从大序列数据集中检测病原病毒的增强的基于最低共同祖先的方法

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摘要

BackgroundEmerging viral diseases, most of which are caused by the transmission of viruses from animals to humans, pose a threat to public health. Discovering pathogenic viruses through surveillance is the key to preparedness for this potential threat. Next generation sequencing (NGS) helps us to identify viruses without the design of a specific PCR primer. The major task in NGS data analysis is taxonomic identification for vast numbers of sequences. However, taxonomic identification via a BLAST search against all the known sequences is a computational bottleneck.
机译:背景技术新兴的病毒性疾病多数是由病毒从动物传播给人类引起的,对公共健康构成了威胁。通过监视发现病原性病毒是防范这种潜在威胁的关键。下一代测序(NGS)帮助我们无需设计特定的PCR引物即可鉴定病毒。 NGS数据分析的主要任务是对大量序列进行分类识别。但是,通过针对所有已知序列的BLAST搜索进行分类识别是计算的瓶颈。

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