Several of our PhD graduates have gone on to careers in academia. The most recent is Jianrong Wang, BINF 2012.
Jianrong’s doctoral thesis was entitled, “Computational algorithm development for epigenomic analysis.” His research at Georgia Tech under the supervision of Prof. King Jordan resulted in publications of 7 first-author papers with novel algorithms, which focus on:
- Statistical modeling for efficient high-throughput next-generation sequencing data analysis, including ambiguous-read mapping and broad-peak calling;
- Machine learning algorithm developments to predict regulatory elements on gene expression, such as insulators and boundary elements;
- Computational inferences of epigenetics patterns, such as combinatorial histone modification signatures ('histone code') and large-scale chromatin domains;
- Integrative analysis of DNA damage, transposable elements and cellular senescence.
After graduation from Georgia Tech, Jianrong moved to MIT and Broad Institute to continue his bioinformatics research as a postdoctoral researcher. His project focused on building new probabilistic models and efficient learning algorithms to infer three-dimensional chromatin interactions between distal enhancer elements and gene promoters in diverse human cell-types or tissues. The predicted cell-type specific enhancer-gene networks provide a rich resource to gain insights of long-range enhancer regulation dynamics and the associated mechanisms. The research also provides a platform to systematically annotate the functional perturbations caused by non-coding genetic variants that are significantly associated with different human diseases, including cancer.
Jianrong will join Michigan State University as an assistant professor and will start his lab in January, 2017. His research will continue in the direction of developing robust and innovative machine learning algorithms to tackle the big-data challenges in functional genomics, regulatory networks and human disease. A variety of integrative probabilistic models will be built to characterize the multi-layer gene regulation systems in different cellular contexts by leveraging heterogeneous genome-wide 'omics' datasets and small-scale experiments from collaborators. The long-term goal of his lab is to identify the underlying disrupted pathways and mechanisms of human diseases at the systems-level, which is expected to lead to new biological understandings and improved biomedical applications.