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
A two or three year postdoctoral position in computational biology with the focus on single-cell transcriptome analysis is available at the Guo-Cheng Yuan Lab, in the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute/Harvard School of Public Health.
The candidate will develop nonlinear dynamics and statistical methods for analyzing single-cell transcriptomic data, with the aim of characterizing the complex cell developmental hierarchy that has been implied but difficult to detect using traditional methods in cancer and other systems. The fellow will have opportunities to interact with colleges in biostatistics in Harvard School of Public Health and closely collaborate with experimental groups in Harvard Medical School and affiliated research hospitals.
The successful applicant 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 data-types is highly desired. Lead author in at least one publication in major peer-reviewed scientific journals.
Interested applicants please send CV and at least two recommendation letters to Dr. Guo-Cheng Yuan (email@example.com).