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Kai Wang, PhD
Kai Wang

Dr. Wang's research focuses on the development of bioinformatics methods to improve the understanding of the genetic basis of human diseases, and the integration of electronic health records and genomic information to facilitate genomic medicine on scale.



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Dr. Wang's research aims to develop novel genomics and bioinformatics methods to improve the diagnosis, treatment, and prognosis of rare diseases, to ultimately facilitate the implementation of genomic medicine on scale. His research can be divided into several areas. First, Dr. Wang and his lab are developing analytical pipelines for whole genome and whole exome sequencing data. Some examples of computational tools used in the lab include ANNOVAR, Phenolyzer, InterVar, and CancerVar.

Dr. Wang and his team are also developing genomic assays and methods to analyze long-read data, such as those generated from linked-read sequencing, optical mapping, PacBio, and Nanopore sequencing. These methods help identify causal genetic variants on cases that failed to be diagnosed by traditional whole genome/exome sequencing approaches, and help map aberrant DNA modifications such as methylations in tissues from patients in comparison to controls. Some examples of computational tools developed by the lab include RepeatHMM, NextSV, LongSV, LinkedSV, LongGF, NanoMod, DeepMod, and DeepRepeat.

Additionally, Dr. Wang is developing data mining approaches from clinical phenotypic information in Electronic Health Records (EHR) to correlate genotype and phenotype together, and better understand the phenotypic heterogeneity of inherited diseases. Some examples of computational tools the lab employs include EHR-Phenolyzer, Doc2HPO, Phen2Gene, and PhenCards, which use natural language processing on clinical notes to predict possible genetic syndromes and candidate genes.

Titles and Academic Titles


Professor of Pathology and Laboratory Medicine

Publication Highlights