Doblin: Inferring dominant clonal lineages from DNA barcoding time-series

The lineage dynamics and history of cells in a population reflect the interplay of evolutionary forces they experience, namely 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 ecological interaction of the population 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 the accuracy of Doblin using lineage data from evolutionary simulations, showing that it recovers the identity of clones and relative fitness in the simulation. Subsequently, we applied Doblin to analyze clonal dynamics in laboratory evolutions of E. 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.