Modeling And Design Of Cell Type Specific Enhancers Using Single Cell Multi Omics And Deep Learning

Seminar Modeling And Design Of Cell Type Specific Enhancers Using We present a gene level regulatory model, single cell atac rna linking (scarlink), which predicts single cell gene expression and links enhancers to target genes using multi ome. Screen of minimalistic enhancers in blood progenitor cells demonstrates widespread dual activator repressor function of transcription factors (tfs) and enables the model guided design of cell state specific enhancers.

Identification Of Cell Type Specific Single Cell Enhancers A A List I will present new computational strategies that take advantage of the joint analysis of scrna seq and scatac seq data, and that derive “enhancer grns” (egrn) with key transcription factors, genomic enhancers, and predicted target genes per cell type. We introduce scmultimap, a statistical method that infers enhancer gene association from sparse multimodal counts using a joint latent variable model. it adjusts for technical confounding, permits fast moment based estimation and provides analytically derived p values. For varying datasets, we demonstrate that crested facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. By comparing multi omics machine learning approaches using in vivo data, they define critical features for enhancer prediction, improving genetic tool design for cell type targeting in the mammalian cortex.

Identification Of Cell Type Specific Single Cell Enhancers A A List For varying datasets, we demonstrate that crested facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. By comparing multi omics machine learning approaches using in vivo data, they define critical features for enhancer prediction, improving genetic tool design for cell type targeting in the mammalian cortex. Here, we characterized 64,400 fully synthetic dna sequences to bottom up dissect design principles of cell state specific enhancers in the context of the differentiation of blood stem cells to seven myeloid lineages. Create excels in identifying cell type specific cres, and provides quantitative and interpretable insights into cre specific features, uncovering the underlying regulatory codes. Recently, single cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer gene pairs in a cell type specific and context specific manner. Here we introduce a novel in vivo in silico method for spatial single cell enhancer reporter assays (spatial scera) designed to reconstruct the spatial activity of candidate enhancer regions in parallel in multicellular organisms.