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- W4313426682 abstract "Abstract Chronic disease processes are marked by cell-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We developed a regularized regression approach, RENIN, ( Re gulatory N etwork In ference) to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generated a single nucleus multiomic dataset from seven adult human kidney biopsies and applied RENIN to study drivers of a failed injury response associated with kidney disease. We demonstrate that RENIN is highly effective tool at predicting key cis- and trans- regulatory elements." @default.
- W4313426682 created "2023-01-06" @default.
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- W4313426682 date "2022-12-30" @default.
- W4313426682 modified "2023-10-17" @default.
- W4313426682 title "Predicting regulators of epithelial cell state through regularized regression analysis of single cell multiomic sequencing" @default.
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- W4313426682 doi "https://doi.org/10.1101/2022.12.29.522232" @default.
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