The time spent by a single RNA polymerase (RNAP) at specific locations along the DNA, termed "residence time", reports on the initiation, elongation and termination stages of transcription. At the single molecule level, this information can be obtained from dual ultra-stable optical trapping experiments, revealing a transcriptional elongation of RNAP interspersed with residence times of variable duration. Successfully discriminating between long and short residence times was used by previous approaches to learn about RNAP's transcription elongation dynamics. Here, we propose an approach based on the Bayesian sticky hidden Markov models that treats all residence times, for an E. Coli RNAP, on an equal footing without a priori discriminating between long and short residence times. In addition, our method has two additional advantages, we provide: full distributions around key point statistics; and directly treat the sequence-dependence of RNAP's elongation rate. By applying our approach to experimental data, we find: no emergent separation between long and short residence times warranted by the data; force dependent average residence time transcription elongation dynamics; limited effects of GreB on average backtracking durations and counts; and a slight drop in the average residence time as a function of applied force in RNaseA's presence.

中文翻译：

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RNA聚合酶转录动力学的停留时间分析：贝叶斯粘性HMM方法

单个RNA聚合酶（RNAP）在沿DNA的特定位置所花费的时间称为“停留时间”，报告了转录的起始，延伸和终止阶段。在单分子水平上，该信息可从双重超稳定光学捕获实验中获得，揭示了RNAP的转录延伸点缀着可变持续时间的停留时间。先前的方法成功地区分了长停留时间和短停留时间，从而了解了RNAP的转录延伸动力学。在这里，我们提出了一种基于贝叶斯粘性隐马尔可夫模型的方法，该方法在没有先验的情况下平等地对待大肠杆菌RNAP的所有停留时间区分停留时间长短。此外，我们的方法还具有两个额外的优点：围绕关键点统计信息的完整分布；并直接处理RNAP延伸率的序列依赖性。通过将我们的方法应用于实验数据，我们发现：数据不能保证长和短停留时间之间出现紧急分离；力依赖性平均停留时间转录延伸动力学；GreB对平均回溯持续时间和计数的影响有限；在RNaseA存在的情况下，平均停留时间随施加的力而略有下降。