Showing papers by "Daniel Tranchina published in 2019"

Water impacts nutrient dose responses genome-wide to affect crop production.

Abstract: Changes in nutrient dose have dramatic effects on gene expression and development. One outstanding question is whether organisms respond to changes in absolute nutrient amount (moles) vs. its concentration in water (molarity). This question is particularly relevant to plants, as soil drying can alter nutrient concentration, without changing its absolute amount. To compare the effects of amount vs. concentration, we expose rice to a factorial matrix varying the dose of nitrogen (N) and water (W) over a range of combinations, and quantify transcriptome and phenotype responses. Using linear models, we identify distinct dose responses to either N-moles, W-volume, N-molarity (N/W), or their synergistic interaction (N×W). Importantly, genes whose expression patterns are best explained by N-dose and W interactions (N/W or N×W) in seedlings are associated with crop outcomes in replicated field trials. Such N-by-W responsive genes may assist future efforts to develop crops resilient to increasingly arid, low nutrient soils.

A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory.

Abstract: A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study.