Jing Zhang

Single-cell genomics and regulatory networks for 388 human brains

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq

The paper identifies and validates enhancer regions in primary human neural progenitor cells using capture STARR-seq, linking them to genes involved in psychiatric disease pathways.

ScISOr-ATAC reveals convergent and divergent splicing and chromatin specificities between matched cell types across cortical regions, evolution, and in Alzheimer's disease.

The paper introduces ScISOr-ATAC to measure splicing and chromatin accessibility in frozen brain tissues, finding convergent and divergent patterns in different cell types and conditions.

DeepVelo: Data-driven Single-cell Transcriptomic Deep Velocity Field Learning with Neural Ordinary Differential Equations

DeepVelo models single-cell transcriptome dynamics using neural ODEs, improving velocity field accuracy and identifying developmental driver genes.

STARRPeaker: Uniform processing and accurate identification of STARR-seq active regions

The paper introduces STARRPeaker, a processing pipeline for STARR-seq data that improves enhancer detection by addressing sequencing biases and RNA stability effects.

An integrative ENCODE resource for cancer genomics

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

DiNeR identifies significant changes in transcription factor co-regulation networks by analyzing ChIP-seq data from disease and normal samples, revealing key disease-associated regulators.

Epigenome-based Splicing Prediction using a Recurrent Neural Network

The paper develops a recurrent neural network to predict splicing outcomes using epigenetic signals, showing that spatiotemporal chromatin context is key to splicing regulation.

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.