Lin An

The paper integrates epigenomic and transcriptomic data from mouse hematopoiesis to identify regulatory elements and their dynamics across cell types.

S3norm improves normalization of epigenomic data by simultaneously adjusting sequencing depth and signal-to-noise ratio, enhancing the detection of biological variation.

OnTAD identifies hierarchical TAD structures from Hi-C data with improved accuracy and speed, revealing new insights into TAD organization and gene regulation.

The paper integrates GATA transcription factors and epigenomes in mouse hematopoietic cells using IDEAS to map regulatory landscapes and identify candidate cis-regulatory elements.

Snapshot generates hierarchical visualizations of epigenetic features along cell differentiation hierarchies using epigenomic data from the VISION project.

This paper presents an integrative framework to detect structural variants in cancer genomes using multiple methods and finds that non-coding structural variation significantly impacts genome function in cancer cells.

HiCPlus enhances the resolution of Hi-C interaction heatmaps using deep learning, reconstructing high-resolution data from low-resolution inputs.