Probabilistic model based on circular statistics for quantifying coverage depth dynamics originating from DNA replication

Abstract

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.

Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth rates

“… Ma et al (2021) provide theoretical criteria for when such an approach is optimal. Finally, other estimators have focused on PTR estimation for specific strains ( Emiola et al 2020 ) or on estimation using circular statistics ( Suzuki and Yamada 2020 ).…”

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

“…Therefore, it may be useful to estimate gLVE parameters considering the trajectory noise. Second, it is also possible to use growth rate estimation methods based on the peak-to-trough ratio (PTR), which has been proposed recently (Korem et al , 2015; Brown et al , 2016; Emiola and Oh, 2018; Suzuki and Yamada, 2020). The PTR-based growth rate estimation methods require a metagenomic shotgun sequencing dataset and are based on the fact that the higher the growth rate, the more DNA is mapped around the replication origin (Cooper and Helmstetter, 1968; Bremer and Churchward, 1977).…”

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

“…Therefore, it may be useful to estimate gLVE parameters considering the trajectory noise. Second, it is also possible to use growth rate estimation methods based on the peak-to-trough ratio (PTR), which has been proposed recently ( Brown et al , 2016 ; Emiola and Oh, 2018 ; Korem et al , 2015 ; Suzuki and Yamada, 2020 ). The PTR-based growth rate estimation methods require a metagenomic shotgun sequencing dataset and are based on the fact that the higher the growth rate, the more DNA is mapped around the replication origin ( Bremer and Churchward, 1977 ; Cooper and Helmstetter, 1968 ).…”