Bioinformatics and computational biology are two related disciplines that have developed from the need to analyze and interpret large, complex datasets which have emerged in the last decade as genomics, proteomics, systems biology, and other high-throughput approaches have become more feasible. Bioinformatics and computational biology utilize techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. The Predoctoral Training Program in Bioinformatics and Computational Biology (BCB) was established at UNC-Chapel Hill in the Fall 2002 to address these needs. In 2007 the training program transitioned to the Ph.D. Curriculum in Bioinformatics and Computational Biology. The goal of the Ph.D. Curriculum is to train the next generation of scientists who can develop and apply quantitative/analytical tools to driving biological problems. The Ph.D. curriculum provides the necessary latitude to prepare students with the right balance of quantitative skills (e.g., mathematics, statistics, and computer science) and experimental approaches (e.g., genetics, cell biology, molecular biology) for making important contributions to modern biological research. There are currently 13 full professors, 8 associate professors, and 16 assistant professors among the 37 total BCB faculty. The Ph.D. curriculum consists of four key components: formal coursework, research rotations, Ph.D. research and a colloquium. The coursework is includes three tiers of training: foundational courses, core modules, and advanced courses. Eight specialized core modules have been developed that cover major areas of bioinformatics and computational biology, such as information theory, machine learning, sequence comparison, phylogeny, data management, ontology, data mining, biostatistics, biomolecular structure/function prediction, and modeling of complex systems. Funds are requested to support six predoctoral students per year. The requested funds will dovetail with the UNC investment in research infrastructure, faculty recruitment, and education in both genomics and bioinfonnatics and computational biology, leveraging intramural as well as extramural industrial support to expand this vital interdisciplinary training program.

Public Health Relevance

Interpreting the vast amount of data produced by high throughput biomedical technologies requires novel computational and mathematical approaches. The Curriculum in Bioinformatics and Computational Biology at the University of North Carolina at Chapel Hill provides the graduate training needed to develop and apply computational methods for solving driving complex biomedical problems.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Somers, Scott D
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of North Carolina Chapel Hill
Schools of Medicine
Chapel Hill
United States
Zip Code
Liu, Ching-Ti; Buchkovich, Martin L; Winkler, Thomas W et al. (2014) Multi-ethnic fine-mapping of 14 central adiposity loci. Hum Mol Genet 23:4738-44
Liu, Eric Yi; Morgan, Andrew P; Chesler, Elissa J et al. (2014) High-resolution sex-specific linkage maps of the mouse reveal polarized distribution of crossovers in male germline. Genetics 197:91-106
Yourstone, Scott M; Lundberg, Derek S; Dangl, Jeffery L et al. (2014) MT-Toolbox: improved amplicon sequencing using molecule tags. BMC Bioinformatics 15:284
Rogala, Allison R; Morgan, Andrew P; Christensen, Alexis M et al. (2014) The Collaborative Cross as a resource for modeling human disease: CC011/Unc, a new mouse model for spontaneous colitis. Mamm Genome 25:95-108
Simon, Jeremy M; Hacker, Kathryn E; Singh, Darshan et al. (2014) Variation in chromatin accessibility in human kidney cancer links H3K36 methyltransferase loss with widespread RNA processing defects. Genome Res 24:241-50
Weiser, Matthew; Mukherjee, Sayan; Furey, Terrence S (2014) Novel distal eQTL analysis demonstrates effect of population genetic architecture on detecting and interpreting associations. Genetics 198:879-93
Didion, John P; Buus, Ryan J; Naghashfar, Zohreh et al. (2014) SNP array profiling of mouse cell lines identifies their strains of origin and reveals cross-contamination and widespread aneuploidy. BMC Genomics 15:847
Hilton, Isaac B; Simon, Jeremy M; Lieb, Jason D et al. (2013) The open chromatin landscape of Kaposi's sarcoma-associated herpesvirus. J Virol 87:11831-42
Baran-Gale, Jeanette; Fannin, Emily E; Kurtz, C Lisa et al. (2013) Beta cell 5'-shifted isomiRs are candidate regulatory hubs in type 2 diabetes. PLoS One 8:e73240
Lundberg, Derek S; Yourstone, Scott; Mieczkowski, Piotr et al. (2013) Practical innovations for high-throughput amplicon sequencing. Nat Methods 10:999-1002

Showing the most recent 10 out of 13 publications