Bayesian inference of origin firing time distributions, origin interference and licencing probabilities from Next Generation Sequencing data
Bayesian inference of origin firing time distributions, origin interference and licencing probabilities from Next Generation Sequencing data
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DOI：
10.1093/nar/gkz094
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发表时间：
2019-03-18
影响因子：
14.9
通讯作者：
Burroughs, Nigel J.
中科院分区：
文献类型：
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作者：
Bazarova, Aline;Nieduszynski, Conrad A.;Burroughs, Nigel J.
DNA复制是一个随机过程，复制分叉来自多个复制起点。起始点必须在G1中许可，复制体在许可的起始点激活，以便在S阶段产生双向复制分叉。不同的激发时间会导致起始点干扰，来自一个起始点的复制叉子可以通过相邻的起始点进行复制并使其失活(起始点模糊)。我们开发了一种贝叶斯算法来表征来自冈崎片段(OF)测序数据的起源激发统计数据。我们的算法推导出三个连续来源的发射时间和许可概率的分布。我们证明了我们的算法可以区分酿酒酵母和人类细胞类型测序数据中的部分起源许可和起源模糊。我们用我们的方法分析了酿酒酵母中Rat1活性丧失后起始效率的下降，表明许可减少和遮蔽增加都有贡献。此外，我们还表明，仅使用局部数据(跨越三个相邻来源)就可以进行稳健分析，而不需要对整个染色体进行分析。我们的算法利用了近似似然和可逆跳跃抽样技术，这种方法可以扩展到分析通过下一代测序数据可测量的其他机械过程。
DNA replication is a stochastic process with replication forks emanating from multiple replication origins. The origins must be licenced in G1, and the replisome activated at licenced origins in order to generate bi-directional replication forks in S-phase. Differential firing times lead to origin interference, where a replication fork from an origin can replicate through and inactivate neighbouring origins (origin obscuring). We developed a Bayesian algorithm to characterize origin firing statistics from Okazaki fragment (OF) sequencing data. Our algorithm infers the distributions of firing times and the licencing probabilities for three consecutive origins. We demonstrate that our algorithm can distinguish partial origin licencing and origin obscuring in OF sequencing data from Saccharomyces cerevisiae and human cell types. We used our method to analyse the decreased origin efficiency under loss of Rat1 activity in S. cerevisiae, demonstrating that both reduced licencing and increased obscuring contribute. Moreover, we show that robust analysis is possible using only local data (across three neighbouring origins), and analysis of the whole chromosome is not required. Our algorithm utilizes an approximate likelihood and a reversible jump sampling technique, a methodology that can be extended to analysis of other mechanistic processes measurable through Next Generation Sequencing data.