Yufeng Shen

Yufeng Shen


Associate Professor, Department of Systems Biology
Associate Director, Columbia Genome Center



Department of Systems Biology
Center for Computational Biology and Bioinformatics
JP Sulzberger Columbia Genome Center
Department of Biomedical Informatics
Program for Mathematical Genomics


(212) 851-4662

Yufeng Shen is an associate professor in the Columbia University Department of Systems Biology and Department of Biomedical Informatics. After completing his PhD in computational biology in 2007 at the Human Genome Sequencing Center at Baylor College of Medicine, he led the analysis of the first personal genome produced by next-generation sequencing (that of Dr. James D. Watson). In 2008 he joined Columbia University as a postdoctoral fellow, working in computational genomics and genetics of drug adverse reactions, and then joined the faculty in July 2011. Dr. Shen is interested in developing and applying computational methods to study human genetics and diseases. The research in his group is at the interface of biology, statistics, and computer science. Specifically, his group is working in four areas, including genome sequencing and assembly, mapping of disease genes, the role of the major histocompatibility complex (MHC) in autoimmunity, and pharmacogenomics.

More News


Newly Tenured Systems Biology Faculty
Congratulations to Drs. Yufeng Shen, Nicholas Tatonetti, and Chaolin Zhang of the Department of Systems Biology, who have been awarded tenure and promoted to associate professor. Their new appointments are effective July 1, 2019.
Novel Method Identifies New Risk Genes for Developmental Disorders
The genetics of developmental disorders, such as congenital heart disease and autism, are highly complex. There are roughly 500 to 1,000 risk genes that can lead to each of these diseases, and to date, only about a few dozen have been identified. Focusing on haploinsufficiency, a key biological mechanism of genetic risk in developmental disorders, Dr. Yufeng Shen has developed a computational method that enables researchers to find new risk genes in these diseases.
DAMAGES: A Method for Predicting Rare Disease Risk Genes
Looking for distinctive gene expression patterns in different brain cell types offers a more efficient way to search for genetic alterations implicated in autism and reveals a molecular signature of the disorder.
Scientists Quantify Genetic Connections between Cancer and Developmental Disorders
A study led by Yufeng Shen suggests that cancer data could be used to pinpoint rare, damaging genetic alterations that increase the risk of developmental syndromes like autism.
Graduate Course Focuses on Foundations of Deep Sequencing
The new team-taught course provides an understanding of both the experimental principles of next-generation sequencing technologies and key statistical methods for analyzing the data they produce.