SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck
SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck
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SharpTNI：弱瓶颈下简约传输网络的计数和采样
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. El
中科院分区：
文献类型：
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作者：
P. Sashittal;M. El
背景 基因组测序的技术进步正在促进重建传染病爆发期间的传播历史。然而，由于宿主内病原体多样性和弱传播瓶颈（多种遗传上不同的致病菌株共同传播），使用这些数据进行准确的疾病传播推断受到了许多挑战的阻碍。结果我们根据给定的时间系统发育，制定了弱瓶颈下传输网络推理的组合优化问题，并建立了硬度结果。我们提出 SharpTNI，一种从解空间中近似计数和几乎均匀采样的方法。使用模拟数据，我们表明 SharpTNI 可以从简约传输网络的解决方案空间中准确量化和均匀采样，并扩展到大型数据集。我们证明 SharpTNI 可以识别 2014 年埃博拉疫情期间的共同传播，并得到之前研究收集的流行病学信息的证实。结论 考虑到薄弱的传播瓶颈对于准确推断疫情期间的传播历史至关重要。 SharpTNI 是一种基于简约的方法，用于在给定时间系统发育的情况下重建潜伏时间长和接种量大的疾病的传播网络。本文的模型和理论工作为新的最大似然方法在弱瓶颈下共同估计时间系统发育和传输网络铺平了道路。
Background Technological advances in genomic sequencing are facilitating the reconstruction of transmission histories during outbreaks in the fight against infectious diseases. However, accurate disease transmission inference using this data is hindered by a number of challenges due to within-host pathogen diversity and weak transmission bottlenecks, where multiple genetically-distinct pathogenic strains co-transmit. Results We formulate a combinatorial optimization problem for transmission network inference under a weak bottleneck from a given timed phylogeny and establish hardness results. We present SharpTNI, a method to approximately count and almost uniformly sample from the solution space. Using simulated data, we show that SharpTNI accurately quantifies and uniformly samples from the solution space of parsimonious transmission networks, scaling to large datasets. We demonstrate that SharpTNI identifies co-transmissions during the 2014 Ebola outbreak that are corroborated by epidemiological information collected by previous studies. Conclusions Accounting for weak transmission bottlenecks is crucial for accurate inference of transmission histories during outbreaks. SharpTNI is a parsimony-based method to reconstruct transmission networks for diseases with long incubation times and large inocula given timed phylogenies. The model and theoretical work of this paper pave the way for novel maximum likelihood methods to co-estimate timed phylogenies and transmission networks under a weak bottleneck.
影响因子：
5.8
作者：
Aguse, Nuraini;Qi, Yuanyuan;El-Kebir, Mohammed
通讯作者：
El-Kebir, Mohammed
通过 Fenton 体系中 PA-Fe2 的预络合高度增强植酸脱磷酸：高活性、宽 pH 范围和基于 Ferryl 的机制
影响因子：
15.1
作者：
Qian，Yiguang;Pan，Weijie;Yao，Jun
通讯作者：
Yao，Jun