Rachel Eder
4 Papers
Rachel Eder is an academic researcher. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 2, co-authored 3 publications.
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Papers
Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch
Grant Kinsler,Kara Schmidlin,Rachel Eder,Sam Apodaca,Dmitri A. Petrov,Kerry Geiler-Samerotte +5 more
TL;DR: The authors found that fitness measurements vary substantially from replicate to replicate and that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements, and concluded how fitness measurements should be interpreted given their extreme environment dependence.
Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes.
Leandra M. Brettner,Wei-Chin Ho,Kara Schmidlin,Sam Apodaca,Rachel Eder,Kerry Geiler-Samerotte +5 more
TL;DR: In this article , the authors present a review of recent literature in yeast and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models.
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High-throughput single-cell transcriptomics data informs mechanisms underlying cell-to-cell differences in stress responses
Rachel Eder,Leandra M. Brettner,K. Geiler-Samerotte +2 more
TL;DR: High-throughput single-cell transcriptomics reveals cell-to-cell heterogeneity in stress responses, with condition-specific gene sets and a stable signature of stress-repressed programs distinguishing stressed from unstressed cells, challenging average views of stress responses.
An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts
TL;DR: In this article , the authors proposed a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at one time, which can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches.