Software and Database

MCCP (BioRxiv, 2022): A user-friendly web portal to explore the landscape of metabolite-mediated cell-cell communications.

SCIG (Nucleic Acids Research, 2025): SCIG: A AI tool for Uncovering the Cell Identity Genes (CIGs) and their master regulatory genes using single cell transcriptome data.

NanoNm (Molecular Cell, 2024): A machine learning model to detect the RNA modification 2’-O-methylation (Nm) in Nanopore direct RNA sequencing data.

MEBOCOST (Nucleic Acids Research, Accepted): A computational technology to enable detection of cell-cell communications mediated by metabolites and their sensor proteins (receptor, transporter, etc) based on single-cell RNA-Seq data.

StripeDiff (Science Advances, 2022): A statistical method and software to identify differential stripes between two Hi-C samples.

MACMIC (Genomics, Proteomics and Bioinformatics, 2021): A statistical method to define epigenomic features that have maximal co-localization but minimal correlation across the genome.

TADsplimer (Genome Biology, 2020): A bioinformatic toolkit to identify TAD splits and mergers between Hi-C samples.

CEFCIG at GitHub or GoogleSite(Nature Communications, 2020): A Computational Epigenetic Framework for Cell Identity Gene discovery.

• GridGO: a grid-based genetic algorithm for automatic optimization of epigenetic signature analysis on the basis of biological significance.
• CIGdiscover: a logistic regression model for cell identity genes discovery
• CIGnet: a novel network method to uncover master transcription factors for cell identity genes in an unbiased, knowledge-guided, and quantitatively testable manner.
• CIGDB: A Cell Identity Gene Database.

CAGRE (Science, 2019): A bioinformatic tool for Commulative Analysis of Genomic Region Enrichment.

FAID (Nature, 2018): A bioinformatic tool for Feature Analysis of Inserted DNA in a genome.

DANPOS2 (Nature Genetics, 2015): a set of 8 bioinformatics tools, including:

• Dpos: comparative analysis of MNase-Seq data between samples.
• Dpeak: comparative analysis ChIP-Seq data between samples.
• Dregion: analyzing broad epigenetic domains, e.g., super enhancers.
• Stat: statistical analysis of positions, peaks, or regions defined by DANPOS.
• Selector: mapping positions, peaks, or regions to any given genomic features such as genes.
• Profile: analyzing epigenetic signal at any given genomic regions, e.g., genes, enhancers.
• Wiq: a tool for genome wide quantile normalization of epigenetic data.
• Wig2Wiq: a tool for converting wiggle format file to wiq format.

DANPOS (Genome Research, 2013): A tool for Dynamic Analysis of Nucleosome and Protein Occupancy by Sequencing

SpikeSCN (Genes & Development, 2013): Genome structure and copy number variation analysis based on genome sequencing with spike-in control.

BarVar (Cell reports, 2013): A bioinformatics pipeline to screen for drug-resistant genetic variations from barcode sequencing data.

NucHMM (GPB, 2010): A Hidden Markov Model for nucleosome positioning prediction based on DNA sequence feature.

GenomePSD (Nucleic acids research, 2008): A Power Spectrum Density analysis tool for genome sequence.