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The mission of the Wang Lab is to develop novel genomics and bioinformatics methods to improve the diagnosis, treatment, and prognosis of rare diseases, to ultimately facilitate the implementation genomic medicine on scale. Research in the lab can be divided into several areas. First, the Wang Lab is developing analytical pipelines for whole genome and whole exome sequencing data. Some examples of computational tools used in the lab include ANNOVAR, Phenolyzer, and InterVar and CancerVar.
The Wang Lab is 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 the lab team 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, and DeepMod and DeepRepeat.
Additionally, the lab 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.
- Developing pipelines to analyze whole genome and whole exome sequencing data and identify causal genes of rare diseases
- Developing bioinformatics methods to detect structural variants from long-read sequencing techniques, such as linked-read sequencing, optical mapping and PacBio/Nanopore sequencing
- Developing genomic methods to detect DNA modifications and map DNA replication traffic
- Mining electronic health records to identify phenotypic features for rare diseases and building models for disease diagnosis that combine genotype and phenotype data