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Wattenberg, E. S.

Publications and source records attributed to Wattenberg, E. S..

2 recordsLinked to original sources

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions1. We have expanded the Registry to include 2.35 million human and 927 thousand mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded Registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays like STARR-seq, MPRA, CRISPR perturbation, and transgenic mouse assays now cover over 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer/silencer roles in different cellular contexts. Integrating the Registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by discovering KLF1 as a novel causal gene for red blood cell traits. This expanded Registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

genomics↗

chronODE: A framework to integrate time-series multi-omics data based on ordinary differential equations combined with machine learning

Most functional genomic studies are conducted in steady-state conditions, therefore providing a description of molecular processes at a particular moment of cell differentiation or organismal development. Longitudinal studies can offer a deeper understanding of the kinetics underlying epigenetic events and their contribution to defining cell-type-specific transcriptional programs. Here we develop chronODE, a mathematical framework based on ordinary differential equations that uniformly models the kinetics of temporal changes in gene expression and chromatin features. chronODE employs biologically interpretable parameters that capture tissue-specific kinetics of genes and regulatory elements. We further integrate this framework with a neural-network architecture that can link and predict changes across different data modalities by solving multivariate time-series regressions. Next, we apply this framework to investigate region-specific kinetics of epigenome rewiring in the developing mouse brain, and we demonstrate that changes in chromatin accessibility within regulatory elements can accurately predict changes in the expression of putative target genes over the same time period. Finally, by integrating single-cell ATAC-seq data generated during the same time course, we show that regulatory elements characterized by fast activation kinetics in bulk measurements are active in early-appearing cell types, such as radial glial and other neural progenitors, whereas elements characterized by slow activation kinetics are specific to more differentiated cell types that emerge at later stages of brain development.

bioinformatics↗