The relationship between cell density and cell count differs among Saccharomyces yeast species.

There is a recent push to develop wild and non-domesticated Saccharomyces yeast strains into useful model systems for research in ecology and evolution. Yet, the variation between species and strains in important population parameters remains largely undescribed. Here, we investigated the relationship between two commonly used measures in microbiology to estimate growth rate - cell density and cell count - in 23 strains across all eight Saccharomyces species . We found that the slope of this relationship significantly differs among species and a given optical density (OD) does not translate into the same number of cells across species. We provide a cell number calculator based on our OD measurements for each strain used in this study. Surprisingly, we found a slightly positive relationship between cell size and the slope of the cell density-cell count relationship. Our results show that the strain- and species-specificity of the cell density and cell count relationship should be taken into account, for instance when running competition experiments requiring equal starting population sizes or when estimating the fitness of strains with different genetic backgrounds in experimental evolution studies.

Shepherd: accurate clustering for correcting DNA barcode errors.

MOTIVATION: DNA barcodes are short, random nucleotide sequences introduced into cell populations to track the relative counts of hundreds of thousands of individual lineages over time. Lineage tracking is widely applied, e.g. to understand evolutionary dynamics in microbial populations and the progression of breast cancer in humans. Barcode sequences are unknown upon insertion and must be identified using next-generation sequencing technology, which is error prone. In this study, we frame the barcode error correction task as a clustering problem with the aim to identify true barcode sequences from noisy sequencing data. We present Shepherd, a novel clustering method that is based on an indexing system of barcode sequences using k-mers, and a Bayesian statistical test incorporating a substitution error rate to distinguish true from error sequences. RESULTS: When benchmarking with synthetic data, Shepherd provides barcode count estimates that are significantly more accurate than state-of-the-art methods, producing 10-150 times fewer spurious lineages. For empirical data, Shepherd produces results that are consistent with the improvements seen on synthetic data. These improvements enable higher resolution lineage tracking and more accurate estimates of biologically relevant quantities, e.g. the detection of small effect mutations. AVAILABILITY AND IMPLEMENTATION: A Python implementation of Shepherd is freely available at: https://www.github.com/Nik-Tavakolian/Shepherd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.