We develop computational biology methodologies for genomic data analysis and integration, with the aim to understand systems-level gene regulatory mechanisms. Since virtually all the cells in a living organism share the same genome, epigenetic information provides an essential role in regulating cell-type specific gene regulation. Our long-term goal is to systematically investigate the structure and dynamics the gene regulatory network and its role in maintenance of cell-type specificity. To this end, we have developed integrated approaches to identify the chromatin state organization, to determine the targeting mechanism for epigenetic factors, and to reconstruct the gene regulatory network from integration of multiple data-types. We have applied these approaches to stem cells and cancer biology. We actively participate in the ENCODE consortium. We collaborate closely with a number of basic scientists and clinical physicians at Harvard and affiliated hospitals. Our research is mainly funded by NHGRI and NHLBI.
New! Postdoc Positions Available
Single Cell Genomics. The candidate(s) will develop computational methods for analyzing single-cell transcriptomic and mass cytometry data, with the goals to characterize cellular states and identify rare cell-types, to model the dynamic changes associated with cell-state changes, to investigate the regulatory mechanism at gene expression variation, and to apply this knowledge to stem cell and cancer biology. The candidate will have the opportunity to closely interact with basic biologists and clinical investigators at the Dana-Farber Cancer Institute and Boston Children's Hospital.
Epigenetics and Gene Regulation. The candidate will develop systems biology approaches and software packages to classify chromatin states, to integrate gene expression, DNA sequence, and epigenomic data, to construct and dissect gene regulatory networks, with the goals to gain mechanistic insights into cell-state transitions during stem cell differentiation and disease progression, and to systematically characterize the biological function of the disease-associated genetic variants. The candidate will have the opportunity to closely interact with basic biologists and clinical investigators at the Dana-Farber Cancer Institute and Boston Children's Hospital.
The successful applicant(s) should hold a doctoral degree or equivalent qualification in nonlinear dynamics, physics, statistics, bioinformatics, or a similar field. Candidate holding a degree in biological science should have demonstrated experience in computational or statistical work. Strong programming (in Matlab, R, C/C++, or Python) and communication skills are required. Previous experience in analysis, interpretation, and integration of genomic, transcriptomic and epigenomic data is also required. Previous knowledge in single-cell biology is highly desired but not required. Lead author in at least one publication in major peer-reviewed scientific journals.
Interested applicants please send CV and at least two recommendation letters.
Our CoordinateRoom 1060, Longwood Center, Boston (intersection between Longwood Ave and Brookline Ave). map
Mailing AddressDept of Biostatistics and Computational Biology, LC1060, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215
Email: gcyuan at jimmy dot harvard dot edu