Doblin: inferring dominant clonal lineages from high-resolution DNA barcoding time series

TL;DR

Doblin is a tool that identifies dominant cell lineages in populations using DNA barcoding data, helping track evolutionary dynamics and competition.

Contribution

Introduces Doblin, a new R package for analyzing clonal lineage dynamics from high-resolution DNA barcoding time series.

Findings

Doblin accurately recovers clone identities and relative fitness from simulated lineage data.

Doblin successfully analyzed clonal dynamics in E. coli under antibiotic treatment and gut microbial colonization.

The tool is versatile and applicable across various experimental setups involving lineage tracking.

Abstract

The lineage dynamics and history of cells in a population reflect the interplay of evolutionary forces they experience, including mutation, drift, and selection. When the population is polyclonal, lineage dynamics also manifest the extent of clonal competition among co-existing mutational variants. If the population exists in a community of other species, the lineage dynamics could also reflect the population’s ecological interaction with the rest of the community. Recent advances in high-resolution lineage tracking via DNA barcoding, coupled with next-generation sequencing of bacteria, yeast, and mammalian cells, allow for precise quantification of clonal dynamics in these organisms.

In this work, we introduce Doblin, an R suite for identifying dominant barcode lineages based on high-resolution lineage tracking data. We first benchmarked Doblin’s accuracy using lineage data from evolutionary simulations, showing that it recovers the clones’ identity and relative fitness in the simulation. Next, we applied Doblin to analyze clonal dynamics in laboratory evolutions of Escherichia coli populations undergoing antibiotic treatment and in colonization experiments of the gut microbial community. Doblin’s versatility allows it to be applied to lineage time-series data across different experimental setups.

Doblin is available on CRAN (https://CRAN.R-project.org/package=doblin) and Github (https://github.com/dagagf/doblin).

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