Open4Gene: Hurdle Model-based Method for Peak-to-Gene Linkage Analysis

Open4Gene is an Hurdle model-based method for peak-to-gene linkage analysis from single-cell multiome (ATAC+RNA) datasets. Open4Gene applies two-component mixture Hurdle model to account for excess zeros in single-nucleus RNA data and model linkage between peak open chromatin (ATAC) and gene expression (RNA).

The Open4Gene source code (R package) and tutorial are available at https://github.com/hbliu/Open4Gene.

Our manuscript describing Open4Gene was published as: Liu et al., Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants. Science (2025). PMID: 39913582

SMART: Specific Methylation Analysis and Report Tool

SMART is a quantitative method for the identification of tissue or cell type-specific DNA methylation marks from bisulfite sequencing datasets. This tool focuses on the methylation-based genome segmentation, de novo identification of differentially methylated regions (DMRs), and the identification of DMRs from predefined regions of interest.

The SMART source code (Python package) and tutorial are available at https://github.com/hbliu/SMART2.

Our manuscript describing Open4Gene was published as: Liu et al., Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes. Nucleic Acids Research (2016). PMID: 26635396

QDMR: Quantitative Differentially Methylated Region

QDMR is an entropy-based method for the identification of differentially methylated regions by entropy. This tool focuses on quantifying methylation differences by entropy and identifying differentially methylated regions (DMRs) within predefined regions of interest.

The QDMR source code (Java package) and tutorial are available at https://github.com/hbliu/QDMR.

Our manuscript describing QDMR was published as: Zhang*, Liu* et al., QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Research (2011). PMID: 21306990

eGFRcrea GWAS and Genetic Scorecard Atlases

The eGFRcrea GWAS and Genetic Scorecard Atlases provide a comprehensive mapping and prioritization of GWAS variants associated with kidney function, including functional variants, target genes, and cell type-specific regulatory circuits.

These atlases include the eGFRcrea GWAS (sample size N = 2.2 million), eGFRcrea GWAS (N = 1.5 million), Kidney eQTL, Kidney meQTL, Kidney Cell Open Chromatin Atlas, and Kidney Disease Genetic Scorecard, which Hongbo constructed during postdoctoral training in the Susztak Lab at the University of Pennsylvania.

Our manuscripts describing eGFRcrea GWAS and Kidney Disease Genetic Scorecard were published as:
Liu et al., Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants. Science (2025). PMID: 39913582
Liu et al., Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease. Nature Genetics (2022). PMID: 35710981

Human Methylation Mark Atlas across Tissues and Cell Types

The Human Methylation Mark Atlas provides the DNA methylation marks identified across 50 human tissues and cell types by applying SMART to large-scale bisulfite sequencing datasets.

The Human Methylation Mark Atlas is available at http://fame.edbc.org/methymark.

Our manuscript describing this atlas was published as: Liu et al., Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes. Nucleic Acids Research (2016). PMID: 26635396

DevMouse: Mouse Developmental Methylome Database

DevMouse is the mouse developmental methylome database, which provides DNA methylomes in temporal order and quantitative analysis of methylation dynamics during mouse development.

The DevMouse database is available at http://db.edbc.org/DevMouse.

Our manuscript describing DevMouse was published as: Liu et al., DevMouse, the mouse developmental methylome database and analysis tools. Database (2014). PMID: 24408217