Probabilistic model based on circular statistics for quantifying coverage depth dynamics originating from DNA replication
Probabilistic model based on circular statistics for quantifying coverage depth dynamics originating from DNA replication
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DOI：
10.7717/peerj.8722
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发表时间：
2020-03
期刊：
影响因子：
2.7
通讯作者：
Shinya Suzuki;Takuji Yamada
中科院分区：
文献类型：
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作者：
Shinya Suzuki;Takuji Yamada
背景随着DNA测序技术的发展，微生物群落的静态组学分析，如分类和功能基因组成的确定，已成为可能。此外，最近提出的原位生长速率估计方法允许将当前比较宏基因组学的适用范围扩展到动态分析。然而，利用这种方法，目前可应用的目标范围是有限的。此外，在复制过程中的覆盖深度的特性还没有得到充分的研究。结果我们开发了一个概率模型，模仿覆盖深度动态。该统计模型解释了由于DNA复制和覆盖深度观察引起的误差而在覆盖深度中发生的偏差。虽然我们的方法需要完整的基因组序列，但它涉及稳定到低覆盖深度（>0.01×）。我们还评估了估计使用真实的全基因组序列数据集，并再现了在以前的研究中观察到的生长动力学。通过利用模型中的圆形分布，我们的方法便于量化的不可测量的覆盖深度功能，包括peakedness，偏度，和密度的程度，复制起点周围。当我们将该模型应用于时间序列培养样品时，表示不对称性的偏度参数随时间推移而稳定;然而，表示复制起点浓度水平的密度参数的峰值和程度动态变化。此外，我们证明了在单个染色体中的多个复制起点的活性测量。结论我们设计了一个新的框架，量化覆盖深度动态。我们的研究预计将作为从更广泛的角度使用统计模型的复制活动估计的基础。
Background With the development of DNA sequencing technology, static omics profiling in microbial communities, such as taxonomic and functional gene composition determination, has become possible. Additionally, the recently proposed in situ growth rate estimation method allows the applicable range of current comparative metagenomics to be extended to dynamic profiling. However, with this method, the applicable target range is presently limited. Furthermore, the characteristics of coverage depth during replication have not been sufficiently investigated. Results We developed a probabilistic model that mimics coverage depth dynamics. This statistical model explains the bias that occurs in the coverage depth due to DNA replication and errors that arise from coverage depth observation. Although our method requires a complete genome sequence, it involves a stable to low coverage depth (>0.01×). We also evaluated the estimation using real whole-genome sequence datasets and reproduced the growth dynamics observed in previous studies. By utilizing a circular distribution in the model, our method facilitates the quantification of unmeasured coverage depth features, including peakedness, skewness, and degree of density, around the replication origin. When we applied the model to time-series culture samples, the skewness parameter, which indicates the asymmetry, was stable over time; however, the peakedness and degree of density parameters, which indicate the concentration level at the replication origin, changed dynamically. Furthermore, we demonstrated the activity measurement of multiple replication origins in a single chromosome. Conclusions We devised a novel framework for quantifying coverage depth dynamics. Our study is expected to serve as a basis for replication activity estimation from a broader perspective using the statistical model.