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Yulin Dai, PhD

Assistant Professor

Dr. Yulin Dai joined UTHealth School of Biomedical Informatics (SBMI) in January of 2020. He has interdisciplinary training and nearly 10 years of bioinformatics and statistical genetics research experience.

He has long-term interests in understanding the etiology underlying human genetic diseases and answering biology questions by utilizing computational approaches. During his doctoral program, he focused on the application of the evolution theory to shed light on the biological insights in population genetics/comparative genomics. In his postdoc training in Vanderbilt University Medical Center, He worked with pediatricians as a bioinformatician to analyze the NGS data generated from rare disease families in the nationwide Undiagnosed Disease Network (UDN). Later, he joined the Bioinformatics and Systems Medicine Laboratory (BSML) in UTHealth as a postdoc, he focused on developing methods to infer the causative variants/genes in complex diseases. He developed statistical methods for systematically detecting the disease-relevant tissue- and cell-type for complex diseases. He integrated multi-omics data and performed network-based approaches to prioritize putative disease variants/genes in multiple complex traits, including congenital disease, autoimmune disease, and psychiatric disorders. Ultimately, he wants to understand the genetic implications especially disease-associated genetic alterations on their relevant cell-types.

Besides, he also constructed bioinformatics pipelines to analyze different omics of next-generation sequencing (NGS) (epi) genomics data in many collaboration-based projects.

Contact

Yulin.Dai@uth.tmc.edu
Phone: 713-500-3462
Fax: 713-500-3929

Staff Support

Leticia Flores
Phone: 713-500-3912

Education

  • PhD, Bioinformatics, 2015, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China
  • BS, System Biology, 2010, University of Science and Technology of China, Hefei, P.R. China

Areas of Expertise

  • Tissue/Cell type specificity in complex disease: methods and applications
  • Multi-Omics data integration: methods and applications
  • Next-generation sequencing data analysis and tool development: NGS (epi) genomics data (bulk/single-cell level)