Gene Set Clustering based on Functional annotation

From functional enrichment results to biological networks

A collection of source codes for network-based multi-omics analysis using integrated genome-wide association studies (GWAS) and transcriptomic data to identify genetic contribution into lithium response in patients with bipolar disorder (BD).

util scripts for various omics data analysis

Cross-platform (Windows + Linux) no-code desktop app for reproducible bulk RNA-seq & microarray analysis: STAR/HISAT2/Salmon alignment, DESeq2/limma, GO/KEGG/g:Profiler enrichment, and STRING PPI networks — with first-class support for crops and fungi that lack a Bioconductor OrgDb.

Context-aware restriction of the Gene Ontology (GO) for enrichment analysis.

De novo transcriptome of Staurois parvus + DGE + FE

Explainable AI framework for breast cancer transcriptomics, biomarker discovery, molecular subtype classification, and patient-centric heterogeneity analysis.

A Python script for testing over- and under-representation of Gene Ontology (GO) terms in a target gene set relative to a background set, using the hypergeometric test with Benjamini-Hochberg FDR correction.

RNA-seq analysis of FOXP1-regulated genes in a mouse hippocampus model with integration of schizophrenia GWAS data

Comprehensive RNA-seq differential expression analysis with automated Snakemake workflow, tool-specific Conda environments, and publication-style visualizations.

Interactive ATAC-seq peak analysis app in R Shiny—annotation, motif enrichment, machine learning, and more. Singularity/HPC ready.