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Nucleotide-time alignment for molecular recorders

机译:分子记录仪的核苷酸时间比对

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摘要

Using a DNA polymerase to record intracellular calcium levels has been proposed as a novel neural recording technique, promising massive-scale, single-cell resolution monitoring of large portions of the brain. This technique relies on local storage of neural activity in strands of DNA, followed by offline analysis of that DNA. In simple implementations of this scheme, the time when each nucleotide was written cannot be determined directly by post-hoc DNA sequencing; the timing data must be estimated instead. Here, we use a Dynamic Time Warping-based algorithm to perform this estimation, exploiting correlations between neural activity and observed experimental variables to translate DNA-based signals to an estimate of neural activity over time. This algorithm improves the parallelizability of traditional Dynamic Time Warping, allowing several-fold increases in computation speed. The algorithm also provides a solution to several critical problems with the molecular recording paradigm: determining recording start times and coping with DNA polymerase pausing. The algorithm can generally locate DNA-based records to within <10% of a recording window, allowing for the estimation of unobserved incorporation times and latent neural tunings. We apply our technique to an in silico motor control neuroscience experiment, using the algorithm to estimate both timings of DNA-based data and the directional tuning of motor cortical cells during a center-out reaching task. We also use this algorithm to explore the impact of polymerase characteristics on system performance, determining the precision of a molecular recorder as a function of its kinetic and error-generating properties. We find useful ranges of properties for DNA polymerase-based recorders, providing guidance for future protein engineering attempts. This work demonstrates a useful general extension to dynamic alignment algorithms, as well as direct applications of that extension toward the development of molecular recorders, providing a necessary stepping stone for future biological work.
机译:已经提出使用DNA聚合酶记录细胞内钙水平作为一种新型的神经记录技术,有望对大脑的大部分进行大规模的单细胞分辨率监测。此技术依赖于DNA链中神经活动的局部存储,然后对该DNA进行离线分析。在该方案的简单实现中,无法通过事后DNA测序直接确定每个核苷酸的写入时间。必须估算时序数据。在这里,我们使用基于动态时间规整的算法执行此估算,利用神经活动与观察到的实验变量之间的相关性将基于DNA的信号转换为随时间变化的神经活动的估算值。该算法提高了传统动态时间规整的并行性,从而使计算速度提高了几倍。该算法还为分子记录范式的几个关键问题提供了解决方案:确定记录开始时间并应对DNA聚合酶暂停。该算法通常可以将基于DNA的记录定位在记录窗口的<10%以内,从而可以估算未观察到的合并时间和潜在的神经调节。我们将我们的技术应用于计算机模拟运动控制神经科学实验,使用该算法来估计基于DNA的数据的计时以及在中心向外到达任务期间运动皮质细胞的方向性调整。我们还使用此算法来探索聚合酶特性对系统性能的影响,确定分子记录仪的精度取决于其动力学和产生错误的特性。我们发现基于DNA聚合酶的记录仪的有用属性范围,为将来的蛋白质工程尝试提供指导。这项工作展示了对动态比对算法的有用的一般扩展,以及该扩展对分子记录仪开发的直接应用,为将来的生物学工作提供了必要的垫脚石。

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