Software we developed

EagleC: A deep-learning framework for detecting structural variants from bulk and single-cell chromatin contact maps. (Wang et al., Science Advances, 2022). Github.

NeoLoopFinder: A computational framework to identify copy number variations (CNVs) and predict enhancer-hijacking events genome-wide in the cancer genome. (Wang et al., Nature Methods, 2021). Github.

Peakachu: A supervised machine learning framework to predict chromatin loops from a genome-wide contact map (such as Hi-C and HiChIP). (Salameh and Wang et al., Nature Communications, 2020). Github.

HiCPlus: The first machine learning strategy to computationally enhance the chromatin contact maps.(Zhang et al., Nature Communications, 2018). Github.

HiCRep: One of the first and most widely used software to assess the reproducibility of Hi-C data. (Yang et al., Genome Research, 2017). Github.

The 3D Genome Browser: An interactive platform for visualizing and exploring published chromatin interaction datasets across species, genome assemblies, and resolutions. The resource has served more than 120,000 users worldwide and received more than 1.2 million visits. (Wang et al., Genome Biology 2018, Yu and Fu et al., NAR 2026).Link.