bayroot: Bayesian sampling of HIV-1 integration dates by root-to-tip regression.
bayroot: Bayesian sampling of HIV-1 integration dates by root-to-tip regression.
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潜伏的人类免疫缺陷病毒1型（HIV-1）储库的组成是由前病毒整合到宿主基因组时形成的。这些整合日期可以通过系统发育方法如根到尖（RTT）回归来估计。然而，RTT不适应突变数量随时间的变化，估计分子钟的不确定性，或根在树中的位置。为了解决这些局限性，我们将RTT的贝叶斯扩展作为R包（bayroot）实现，这使用户能够将有关感染时间和开始抗逆转录病毒治疗的先验信息纳入其中。以无根最大似然树作为输入，我们使用Metropolis-Hastings算法从三个参数的联合后验分布（序列进化速率，即，分子钟;根的位置;以及与根相关联的时间）。接下来，我们将拒绝抽样应用于模型参数的后验样本，以模拟HIV前病毒序列的整合日期。为了验证这种方法，我们使用R包treeswithintrees（twt）来模拟与来自单个宿主的活跃和潜伏感染的T细胞样本相关的时间尺度树。我们发现，bayroot收益率显着更准确的估计整合日期比传统的RTT下的一系列模型设置。
The composition of the latent human immunodeficiency virus 1 (HIV-1) reservoir is shaped by when proviruses integrated into host genomes. These integration dates can be estimated by phylogenetic methods like root-to-tip (RTT) regression. However, RTT does not accommodate variation in the number of mutations over time, uncertainty in estimating the molecular clock, or the position of the root in the tree. To address these limitations, we implemented a Bayesian extension of RTT as an R package (bayroot), which enables the user to incorporate prior information about the time of infection and start of antiretroviral therapy. Taking an unrooted maximum likelihood tree as input, we use a Metropolis–Hastings algorithm to sample from the joint posterior distribution of three parameters (the rate of sequence evolution, i.e., molecular clock; the location of the root; and the time associated with the root). Next, we apply rejection sampling to this posterior sample of model parameters to simulate integration dates for HIV proviral sequences. To validate this method, we use the R package treeswithintrees (twt) to simulate time-scaled trees relating samples of actively and latently infected T cells from a single host. We find that bayroot yields significantly more accurate estimates of integration dates than conventional RTT under a range of model settings.
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影响因子：
3.3
作者：
Alizon S;Fraser C
通讯作者：
Fraser C
影响因子：
4
作者：
Stirrup OT;Dunn DT
通讯作者：
Dunn DT
影响因子：
4.3
作者：
Rong L;Perelson AS
通讯作者：
Perelson AS
DOI：
10.1073/pnas.1802028115
发表时间：
2018-09-18
影响因子：
11.1
作者：
Jones BR;Kinloch NN;Horacsek J;Ganase B;Harris M;Harrigan PR;Jones RB;Brockman MA;Joy JB;Poon AFY;Brumme ZL
通讯作者：
Brumme ZL
影响因子：
5.2
作者：
Siliciano, JD;Siliciano, RF
通讯作者：
Siliciano, RF