Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments.
View Article and Find Full Text PDFMotivation: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations.
View Article and Find Full Text PDFGene silencing mediated by dsRNA (RNAi) can persist for multiple generations in (termed RNAi inheritance). Here we describe the results of a forward genetic screen in that has identified six factors required for RNAi inheritance: GLH-1/VASA, PUP-1/CDE-1, MORC-1, SET-32, and two novel nematode-specific factors that we term here (heritable RNAi defective) HRDE-2 and HRDE-4 The new RNAi inheritance factors exhibit mortal germline (Mrt) phenotypes, which we show is likely caused by epigenetic deregulation in germ cells. We also show that HRDE-2 contributes to RNAi inheritance by facilitating the binding of small RNAs to the inheritance Argonaute (Ago) HRDE-1 Together, our results identify additional components of the RNAi inheritance machinery whose conservation provides insights into the molecular mechanism of RNAi inheritance, further our understanding of how the RNAi inheritance machinery promotes germline immortality, and show that HRDE-2 couples the inheritance Ago HRDE-1 with the small RNAs it needs to direct RNAi inheritance and germline immortality.
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