The goal of CCB Genomics research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets.
Cells must limit RNA–RNA interactions to avoid irreversible RNA entanglement. Cells may prevent deleterious RNA-RNA interactions by genome organization to…
bioRxivCells modified outside of the body and then reintroduced provide an advantage over most small-molecule therapeutics in that cells can…
ScienceTo facilitate single-cell multi-omics analysis and improve reproducibility, we present single-cell pipeline for end-to-end data integration (SPEEDI), a fully automated…
Cell Systems
Genome-wide Scale functional interaction networks for 144 human tissues and cell types
Deep learning-based algorithmic framework for predicting chromatin effects
This server performs in silico nano-dissection, an approach we developed to identify genes with novel cell-lineage specific expression.
Integrative Multi-species Prediction
Search-Based Exploration of Expression Compendium [Human]
K-Nearest Neighbors Imputation
Functional Networks of Tissues in Mouse
A data-driven perspective to your gene expression profile for human tissues and diseases.
Data-driven predictions of gene expression, function, regulation, and interactions in human.
Sleipnir Library for Computational Functional Genomics
