Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications.
Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications.
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DOI：
10.1093/bioinformatics/btz814
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发表时间：
2020-03-01
期刊：
影响因子：
--
通讯作者：
Connor TR
中科院分区：
文献类型：
--
作者：
Southgate JA;Bull MJ;Brown CM;Watkins J;Corden S;Southgate B;Moore C;Connor TR
流感病毒由于每年流行和大流行的可能性而成为全球公共卫生负担。由于快速进化的RNA基因组、种间传播、宿主内变异和短读段数据中的噪声，在作图期间读段可能丢失，并且从头组装可能耗时并导致错误组装。我们评估了定位过程中的读段丢失，并设计了一个基于图的分类器VAPOR，用于选择定位参考、组装验证和检测非人类来源的菌株。标准人类参考病毒不足以在模拟中绘制不同的流感样本。VAPOR检索了257个真实的全基因组测序样品的参考文献，与标准参考文献相比，映射读数的比例增加了13.3%。VAPOR有可能提高生物信息学监测管道的鲁棒性，并可适用于其他RNA病毒。VAPOR可在https://github.com/connor-lab/vapor上获得。 补充数据可在Bioinformatics在线获得。
Influenza viruses represent a global public health burden due to annual epidemics and pandemic potential. Due to a rapidly evolving RNA genome, inter-species transmission, intra-host variation, and noise in short-read data, reads can be lost during mapping, and de novo assembly can be time consuming and result in misassembly. We assessed read loss during mapping and designed a graph-based classifier, VAPOR, for selecting mapping references, assembly validation and detection of strains of non-human origin. Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole-genome sequencing samples with a mean of identity to assemblies, and increased the proportion of mapped reads by up to 13.3% compared to standard references. VAPOR has the potential to improve the robustness of bioinformatics pipelines for surveillance and could be adapted to other RNA viruses. VAPOR is available at https://github.com/connor-lab/vapor. Supplementary data are available at Bioinformatics online.
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发表时间：
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期刊：
The Journal of infectious diseases
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