A5037743191- Genomics and Chromatin Dynamics
- Epigenetics and DNA Methylation
- Single-cell and spatial transcriptomics
- Bioinformatics and Genomic Networks
- Cancer Genomics and Diagnostics
- RNA modifications and cancer
- Gene expression and cancer classification
- Immune cells in cancer
- Pancreatic function and diabetes
- Ubiquitin and proteasome pathways
- Biomedical Text Mining and Ontologies
- Genetic Associations and Epidemiology
- Ferroptosis and cancer prognosis
- Protein Degradation and Inhibitors
- Computer Graphics and Visualization Techniques
- CRISPR and Genetic Engineering
- Cancer Immunotherapy and Biomarkers
- Diabetes and associated disorders
- Metabolism, Diabetes, and Cancer
- Genomics and Phylogenetic Studies
- Plant Molecular Biology Research
- RNA Research and Splicing
Pennsylvania State University
2016-2024
Dana-Farber Cancer Institute
2022-2024
Harvard University
2022-2023
C4 Therapeutics (United States)
2023
Gorgias Press (United States)
2020
CCCTC-binding factor (CTCF) is a conserved zinc finger transcription implicated in wide range of functions, including genome organization, activation, and elongation. To explore the basis for CTCF functional diversity, we coupled an auxin-induced degron system with precision nuclear run-on. Unexpectedly, oriented motifs gene bodies are associated transcriptional stalling manner independent bound CTCF. Moreover, at different binding sites (CBSs) displays highly variable resistance to...
The spatial organization of chromatin in the nucleus has been implicated regulating gene expression. Maps high-frequency interactions between different segments have revealed topologically associating domains (TADs), within which most regulatory are thought to occur. TADs not homogeneous structural units but appear be organized into a hierarchy. We present OnTAD, an optimized nested TAD caller from Hi-C data, identify hierarchical TADs. OnTAD reveals new biological insights role levels,...
Thousands of epigenomic data sets have been generated in the past decade, but it is difficult for researchers to effectively use all relevant their projects. Systematic integrative analysis can help meet this need, and VISION project was established
Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios from different experiments can hinder our ability identify real variation raw data. Proper normalization required prior analysis gain meaningful insights. Most existing methods for standardize signals by rescaling either background regions peak...
Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states the non-cancerous cells tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome limitations resolution consistency that result from manual labelling using known markers. Using millions TME single cells, MetaTiME learns meta-components encode independent components...
Knowledge of locations and activities
Abstract Background Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity epigenetic data pose significant challenges for biologists to identify the regulatory events controlling Results To reduce complexity, we developed package, called Snapshot, clustering visualizing candidate cis-regulatory elements (cCREs) based on their signals This package first introduces binarized indexing strategy cCREs. It then...
Knowledge of locations and activities cis-regulatory elements (CREs) is needed to decipher basic mechanisms gene regulation understand the impact genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining states identifying cCREs blood cell types generate regulatory maps that are comparable integrative modeling eight...
Abstract Summary Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic datasets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in dimensions (features) examined make it challenging effectively extract biologically meaningful inferences from these datasets. We developed a package that reduces while normalizing data by novel normalization method, followed integrative...
Abstract Combinatorial patterns of epigenetic features reflect transcriptional states and functions genomic regions. While many have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple across cell types, species, experimental conditions by leveraging information...
Abstract Thousands of epigenomic datasets have been generated in the past decade, but it is difficult for researchers to effectively utilize all data relevant their projects. Systematic integrative analysis can help meet this need, and VISION project was established V al I dated S ystematic ntegrati ON hematopoiesis. Here, we systematically integrated extensive recording epigenetic features transcriptomes from many sources, including individual laboratories consortia, produce a comprehensive...
Abstract The spatial organization of chromatin in the nucleus has been implicated many aspects regulated gene expression. Maps high frequency interactions between different segments have revealed Topologically Associating Domains (TADs), within which most regulatory are thought to occur. Recent studies shown that TADs not homogeneous structural units, but rather they appear be organized into a hierarchy. However, precise identification hierarchical TAD structures remains challenge. We...
Abstract Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology learns patterns condition-specificity present data. provides generic framework facilitating host analyses, such as clustering features sharing...
Abstract Topologically associating domains (TADs) and stripes are important architectural structures on Hi-C data that for gene regulation. We present Joint Optimized nested TADs Stripes (JOn-TADS), a unified caller in data. JOnTADS effectively identifies hierarchical population micro-C datasets, single-cell It provides robust identifications aligned with known biology captures interaction frequency variations contact maps across diverse contexts. When multiple samples available, JOn-TADS...
ABSTRACT Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios from different experiments can hinder our ability identify real variation raw data. Proper normalization required prior analysis gain meaningful insights. Most existing methods for standardize signals by rescaling either background...
Abstract Summary Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic data sets have been generated under biological contexts to provide insights into regulatory processes. However, the technical noise in dimensions (features) examined make it challenging effectively extract biologically meaningful inferences from these sets. We developed a package that reduces while normalizing by novel normalization method, followed integrative dimensional...
Abstract Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity epigenetic data pose significant challenges for biologists to identify the regulatory events controlling Here, we present new method, called Snapshot, that uses generate hierarchical visualization DNA regions with features segregating along any given differentiation hierarchy interest. Different hierarchies types may be used highlight history...
Abstract Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states the non-cancerous cells tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common tumor microenvironment (TME). We developed a data driven framework, MetaTiME, to overcome limitations resolution consistency that result from manual labelling using known markers. Using millions TME single cells, MetaTiME learns meta-components encode independent...
ABSTRACT Combinatorial patterns of epigenetic features reflect transcriptional states and functions genomic regions. While many have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple across cell types, species, experimental conditions by leveraging information...
Summary Members of the GATA family transcription factors play key roles in differentiation specific cell lineages by regulating expression target genes. Three distinct hematopoietic differentiation. In order to better understand how these function regulate genes throughout genome, we are studying epigenomic and transcriptional landscapes cells a model-driven, integrative fashion. We have formed collaborative multi-lab VISION project conduct V al I dated S ystematic ntegrati ON data mouse...
Motivation: Systems biology integrates expression, methylation, transcription factor binding and histone modification profiles with other physiological characteristics of a specific organ. Repositories that provide the required data, like ENCODE, generally work on high level do not take heterogeneity cell types within an organ into consideration. The hematopoietic system allows characterization study each type involved in generation blood cells from bone marrow stem thus provides good...
Abstract Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology learns patterns condition-specificity present data. provides generic framework facilitating host analyses, such as clustering features sharing...