Weixiang Fang

I am a postdoctoral fellow in the Department of Biomeidical Engineering and Center for Epigenetics at Johns Hopkins University. Currently, I am a member of the Reza Kalhor lab. Previously, I did my PhD in the Honagkai Ji Lab.

My research includes:

Methodologies for reconstructing cell fate histories using lineage barcodes.
Methodologies for inferring single cell phylogenies.
Model for bridging single cell state and fate readouts in development.
Single-cell genomics, deep learning models for integrating different data modalities.
Epigenomics and evolution, transferring animal model findings to human.

My long-term interest is in:

Building statistical and computational methodologies for drawing insights from genomics data. I believe that models we use should allow us to gain mechanistic and causal understanding of biology.
Understanding dynamical process of differentiation and morphogenesis in development, as well as sources and mechanisms of developmental disorders.

selected publications

Quantitative fate mapping: A general framework for analyzing progenitor state dynamics via retrospective lineage barcoding

Weixiang Fang, Claire M Bell, Abel Sapirstein, and 5 more authors

Cell 2022
Quantifying Functional Conservation of Human and Mouse Regulatory Elements via FUNCODE

Weixiang Fang, Chaoran Chen, Boyang Zhang, and 4 more authors

bioRxiv 2024

Abs

Evolutionary conservation is crucial for understanding genome functions and lays the foundation for using animal models in studying human diseases. However, conventional conservation scores based on DNA sequence evolution do not capture the dynamic biochemical activities of DNA elements, termed functional conservation. Quantifying functional conservation has been limited by the availability of functional genomic data matched across species. To address this, we developed FUNCODE, a framework for characterizing functional conservation through in silico sample matching. Applying FUNCODE to 2,595 uniformly processed datasets from the Encyclopedia of DNA Elements (ENCODE), we generated genome-wide FUNCODE scores for human and mouse regulatory elements, identifying 3.3 million functionally conserved human-mouse element pairs. We demonstrate FUNCODE’s diverse applications, including annotating 78,501 novel regulatory elements, transferring 37,968 high-resolution human ENCODE Hi-C loops in immune lineages to mice, identifying conserved functional signals for disease modeling, and enhancing cross-species integration of single-cell omics data.Competing Interest StatementThe authors have declared no competing interest.