Gang Wu Lab

Each set of data is important in helping us understand the landscape of normal development as well as identify genetic variability and molecular drivers of disease. Bioinformatics and machine learning techniques are instrumental in categorizing and translating the vast amount of collected data into meaningful biological information that can inform treatment strategies for our patient population. 

My team sits at the crossroads of technology, genomics, programming and statistical analysis. We focus on translating the information captured by various technological platforms and integrating it to more accurately predict biological processes and events occurring within cells and within individuals.   

We are a multifaceted team consisting of biologists, computer scientists and mathematicians. Our collaborative body of expertise produces unique perspectives that can be applied to research questions. This has resulted in data with less artifacts, higher confidence and bigger impacts in the clinical realm. 

Our ability to build pipelines with carefully considered filters and analyses produces meaningful data that can help us pinpoint important genetic variants contributing to disease. 

Lack of a control cohort is a common challenge in rare disease research. We recently created a synthetic control cohort to understand the sources of artifacts from sequencing, mapping and variant calling, allowing us to use the summary statistics from public databases as the control for rare variant burden tests. By ensuring that we have a large control group in which to compare, our novel rare variant analysis framework (CoCoRV) can more accurately identify genes carrying rare, deleterious genetic variants enriched in rare diseases and that typically do not have large sample sizes.   

Our group is applying our expertise to study motor neuron diseases such as amyotrophic lateral sclerosis (ALS). ALS is a rare neurodegenerative disease with no cure. Recently, our group helped identify a novel genetic modifier that is associated with the reversal of ALS. This finding provides a promising lead for potential therapeutic targets for ALS. 

When a genome has a propensity to acquire mutations, genomic instability ensues, and this is a hallmark of aging as well as cancer. Instability can arise from point mutations, indels or chromosomal rearrangements, all of which can have significant impact on the overall health of the individual. We are interested in leveraging technology and using an evolutionary lens to model genomic instability, especially the complex genomic rearrangements in extrachromosomal circular DNA, and understand how these DNA alterations manifest over time.