Most diseases can be attributed at least in part to genetic mutations or epigenetic modifications. High-throughput sequencing approaches such as genome sequencing (DNA-seq), transcriptome sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) produce massive data resources for the researcher to interrogate disease-causative changes. While extremely valuable, data generated from clinical samples are usually heterogeneous and noisy.
The research of Liguo Wang, Ph.D., concentrates on the major theme of developing computational tools and methods to transform these immense data into biological knowledge and uncover the underlying mechanisms.
- High-throughput sequencing data analysis. More specifically, Dr. Wang's research focuses on using DNA-seq or exome-seq data to identify genetic alterations, including single nucleotide variation (SNV), copy number variation (CNV) and structure variation (SV); using ChIP-seq or ChIP-exo to demarcate protein binding locations or profile histone methylation maps throughout the genome; and using RNA-seq to interrogate expression change, alternative splicing change, novel transcripts, aberrant transcripts such as gene fusion, and RNA-DNA differences (coupled with DNA-seq or exome-seq).
- Data mining and integration. The amount of biological data is growing exponentially and is spread over numerous heterogeneous data repositories. It is necessary to develop efficient bioinformatics tools to federate different types of data and extract relevant information effectively and conveniently.
- Prostate cancer development and progression. Deciphering the behavior of the androgen receptor (AR) under disease states is fundamentally important to understand prostate cancer. Taking advantages of high-throughput sequencing, it is possible to genome-widely investigate how the AR interacts with cis-regulatory DNA elements, trans-regulatory protein factors as well as the epigenetic environments (such as DNA and histone methylation status) during prostate cancer development and progression.
Significance to patient care
Computational approaches are able to screen prognosis biomarkers from massive datasets efficiently. Data processing and extraction of accurate information is indispensable in individualized medical treatment.