Dawei Li, Ph.D.

Assistant Professor

Training & Education

Dr. Li was first trained in psychiatric/behavioral genetics (molecular and computational), then in statistical genetics at Rockefeller University, and genomics and bioinformatics at Yale University.

Research Interests

Research in our lab includes disease genetic risk discovery and related bioinformatics development. We recently developed novel bioinformatics methods to genotype endogenous retrovirus and transposable element variants (published in Bioinformatics) and measure their expression (unpublished), and methods to examine the human virome/viral integrations (published in Genome Research). More recently, we curated multi-omics data from substance use disorder and myalgic encephalomyelitis/chronic fatigue syndrome (genome, transcriptome, methylome, brain images, and phenome) (unpublished). We plan to use our new methods to study neuroinflammation and immune responses in the diseases. We will also integrate multi-omics data to identify other new genetic and environmental factors. We are also interested in long-reads and single cell sequencing for higher resolution. Please visit our lab webpage for more information about our multi-omics program development. 


MMG233: Genetics and Genomics

Featured Publications

Chen X, Kost J, Sulovari A, Wong N, Liang WS, Cao J, Li D*. A virome-wide clonal integration analysis platform for discovering cancer viral etiology. Genome Research. 2019 May;29(5):819-830. PMID: 30872350. (A high-throughput sequencing-based bioinformatics approach to detection of viral integration events in the human genome on the virome-wide level, named VIcaller, was developed and demonstrated in a genome data.)

Chen X, Li D*. ERVcaller: Identifying polymorphic endogenous retrovirus and other transposable element insertions using whole-genome sequencing data. Bioinformatics. 2019 Oct 15;35(20):3913-3922. PMID: 30895294. (A high-throughput sequencing-based bioinformatics approach to genome-wide genotyping of ERVs and other TEs, named ERVcaller, was developed. This tool allows for genome-wide association studies of TEs with any complex disease.)

Sulovari A, Li D*. VIpower: Simulation-based tool for estimating power of viral integration detection via high-throughput sequencing. Genomics. 2019 Jan 30. [Epub ahead of print]. PMID: 30710609. (This study can help design new sequencing experiments to increase the power and accuracy to detect genome-wide viral integrations as well as ERVs and other TEs.)

Chen X, Kost J, Li D*. Comprehensive comparative analysis of methods and software for identifying viral integrations. Briefings in Bioinformatics. 2018 Aug 8. [Epub ahead of print]. PMID: 30102374. (This study, for the first time, proposed the “virome-wide” concept for viral integration detection.)

Sulovari A, Liu Z, Zhu Z*, Li D*. Genome-wide meta-analysis of copy number variations with alcohol dependence. Pharmacogenomics Journal. 2018 May 22;18(3):398-405. PMID: 28696413. (First genome-wide meta-analysis in any addictive substance)

All Li publications

Dawei Li, PhD

Contact Information

Office: 8 Hills

Phone: 802-656-9838