Rui Jiang

1 month ago | nature.com | Xuejian Cui |Qijin Yin |Zijing Gao |Zhen Li |Xiaoyang Chen |Shengquan Chen | +4 more

Cis-regulatory elements (CREs), including enhancers, silencers, promoters and insulators, play pivotal roles in orchestrating gene regulatory mechanisms that drive complex biological traits. However, current approaches for CRE identification are predominantly sequence-based and typically focus on individual CRE types, limiting insights into their cell-type-specific functions and regulatory dynamics. Here, we present CREATE, a multimodal deep learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE identification and characterization. CREATE integrates genomic sequences, chromatin accessibility, and chromatin interaction data to generate discrete CRE embeddings, enabling accurate multi-class classification and robust characterization of CREs. CREATE excels in identifying cell-type-specific CREs, and provides quantitative and interpretable insights into CRE-specific features, uncovering the underlying regulatory codes. By facilitating large-scale prediction of CREs in specific cell types, CREATE enhances the recognition of disease- or phenotype-associated biological variabilities of CREs, thus advancing our understanding of gene regulatory landscapes and their roles in health and disease. Cui et al. present CREATE, a platform for the identification of multi-class cell-type-specific CREs by integrating multi-omics data. CREATE interpretably extracts discrete CRE embeddings, quantitatively unveils CRE-specific features, and effectively enables large scale prediction of CREs.

Dec 30, 2024 | genomebiology.biomedcentral.com | Xiaoyang Chen |Keyi LI |Xiaoqing Wu |Zhen Li |Qun Jiang |Xuejian Cui | +3 more

To capture spatial accessibility pattern of each peak, Descart construct a spatial graph based on spatial locations of spots (can also be replaced by cells).

Dec 18, 2024 | genomebiology.biomedcentral.com | Zijing Gao |Qiao Liu |Wanwen Zeng |Rui Jiang

We used three different datasets in the experiments. For chromatin accessible data, we downloaded DNase bam files across 129 human biosamples from ENCODE [21] project (Additional file 2: Table S6). We divided the human hg19 genome into 200-bp non-overlapping bins, and we assigned the label for each bin in each cell type. For the regression design, we pooled the bam files of multiple replicates for a cell type, and obtain the raw read count \({n}_{lk}\) for bin \(l\) in cell type \(k\).

Oct 21, 2024 | mdpi.com | Guangyong Chen |Yiqun Zhang |Rui Jiang

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Jul 19, 2024 | science.org | Ruiyi Zhang |Aashish Gupta |Zhichao Lu |Min Zhu |Chenxing Wang |Rui Jiang | +10 more

AbstractAged patients often suffer poorer neurological recovery than younger patients after traumatic brain injury (TBI), but the mechanisms underlying this difference remain unclear. Here, we demonstrate abnormal myelopoiesis characterized by increased neutrophil and classical monocyte output but impaired nonclassical patrolling monocyte population in aged patients with TBI as well as in an aged murine TBI model.