Develop NGS-based analysis algorithms and conduct integrated genomics analysis to uncover the molecular mechanisms of human diseases

The recent progress in studying human diseases on a genome-wide scale by NGS technologies, including whole-genome/whole-exome sequencing, RNA-seq, ribosome profiling, ChIP-seq and many others, has yielded new insights into genetic pathways and networks that reveal the molecular mechanisms of human diseases. However, the current computational algorithms still cannot effectively extract information to elucidate the molecular mechanisms of human diseases from high-throughput DNA, RNA and epigenetic sequencing data. My lab has been developing various algorithms and leading the integrated bioinformatics analyses to uncover new disease genes and pathways, and elucidate molecular mechanisms for cardiovascular diseases.


Huang L, et al. (2019) SR-B1 drives endothelial cell LDL transcytosis via DOCK4 to promote atherosclerosis. Nature. 569(7757):565-569.

Chu H, et al. (2021) Protein Phosphatase 2A Activation via ApoER2 in Trophoblasts Drives Preeclampsia in a Mouse Model of the Antiphospholipid Syndrome. Circulation Research 129 (7), 735-750.

Cui M, et al. (2020) Dynamic transcriptional responses to injury of regenerative and non-regenerative cardiomyocytes revealed by single-nucleus RNA sequencing. Developmental Cell. 53(1):102-116.

Cui M, et al. (2021) Nrf1 promotes heart regeneration and repair by regulating proteostasis and redox balance. Nature Communications 12 (1), 1-15.

Develop statistics and deep learning algorithms to build novel predictive models on drug response, clinical outcomes and other biological features for various human diseases


Xu L, et al. (2013) Dynamic epistasis for different alleles of the same gene. Proceedings of the National Academy of Sciences USA 109:10420-10425.

Zhang M, et al. (2018) DIGREM: an integrated web-based platform for detecting effective multi-drug combinations. Bioinformatics. 35(10):1792-1794.

Xu L, et al. (2018) Integrative Bayesian analysis identifies rhabdomyosarcoma disease genes. Cell Reports 24, 238–251.

Zheng et al. (2019) Bayesian modeling identifies PLAG1 as a key regulator of proliferation and survival in rhabdomyosarcoma cells. Molecular Cancer Research 18 (3), 364-374.

Develop statistical algorithms and conduct integrated genomics analysis to identify new biomarkers and therapeutic targets for human cancer


Chen KS, et al. (2018) Mutations in microRNA processing genes in Wilms tumors derepress the IGF2 regulator PLAG1. Genes & Development 32(15-16):996-1007.

Ci B, et al. (2020) Development of a Data Model and Data Commons for Germ Cell Tumors. JCO clinical cancer informatics. 4, 555-566.

Laetsch TW, et al. (2018) Undifferentiated sarcomas in children harbor clinically-relevant oncogenic fusions and gene copy-number alterations: A report from the Children’s Oncology Group. Clinical Cancer Research 24(16):3888-3897.

Kendall GC, et al. (2018) PAX3-FOXO1 transgenic zebrafish models identify HES3 as a mediator of tumorigenesis. Elife 5;7. e.33800.

Li S, et al. (2019) Twist2 amplification in rhabdomyosarcoma represses myogenesis and promotes oncogenesis by redirecting MyoD DNA binding. Genes & Development. 33(11-12):626-640.

Develop statistical algorithms and conduct integrated genomics analysis to identify new biomarkers and therapeutic targets in a variety of non-cancer human diseases


Ramirez-Martinez A, et al. (2021) Essentiality of the nuclear envelope protein Net39 for nuclear integrity, chromatin organization, and muscle growth. Nature Communications 12 (1), 1-12.

Cannavino J, et al. (2021) Regulation of cold-induced thermogenesis by the RNA binding protein FAM195A. Proceedings of the National Academy of Sciences USA 118 (23) e2104650118.

Bhargava V et al. (2020) GCNA Preserves Genome Integrity and Fertility Across Species. Developmental Cell. 52 (1), 38-52. e10.