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Time Course Regulatory Analysis Based on Paired Expression and Chromatin Accessibility Data Published by Genome Research Online
Update time: 2020-10-20

Genome Research published Time course regulatory analysis based on paired expression and chromatin accessibility data written by Prof.WANG Yong from AMSS and researchers from Stanford University online.

In this paper, they propose time course regulatory analysis (TimeReg) as a method for the analysis of gene regulatory networks based on paired gene expression and chromatin accessibility data from a time course. TimeReg can be used to prioritize regulatory elements, to extract core regulatory modules at each time point, to identify key regulators driving changes of the cellular state, and to causally connect the modules across different time points. We applied the method to analyze paired chromatin accessibility and gene expression data from a retinoic acid (RA)–induced mouse embryonic stem cells (mESCs) differentiation experiment. The analysis identified 57,048 novel regulatory elements regulating cerebellar development, synapse assembly, and hindbrain morphogenesis, which substantially extended our knowledge of cis-regulatory elements during differentiation. Using single-cell RNA-seq data, they showed that the core regulatory modules can reflect the properties of different subpopulations of cells. Finally, the driver regulators are shown to be important in clarifying the relations between modules across adjacent time points. As a second example, our method on Ascl1-induced direct reprogramming from fibroblast to neuron time course data identified Id1/2 as driver regulators of early stage of reprogramming.

Genome Research is an international, continuously published, peer-reviewed journal that features outstanding original research providing novel insights into the genome biology of all organisms, including significant advances in genomic medicine. The journal also provides high-quality reviews and perspectives written by respected leaders in the field and reports cutting-edge computational biology and high-throughput methodologies.

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