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.
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.
| Kutchko, Katrina M; Madden, Emily A; Morrison, Clayton et al. (2018) Structural divergence creates new functional features in alphavirus genomes. Nucleic Acids Res 46:3657-3670 |
| Reed, Jason W; Wu, Miin-Feng; Reeves, Paul H et al. (2018) Three Auxin Response Factors Promote Hypocotyl Elongation. Plant Physiol 178:864-875 |
| Corty, Robert W; Valdar, William (2018) vqtl: An R Package for Mean-Variance QTL Mapping. G3 (Bethesda) 8:3757-3766 |
| Kirk, Jessime M; Kim, Susan O; Inoue, Kaoru et al. (2018) Functional classification of long non-coding RNAs by k-mer content. Nat Genet 50:1474-1482 |
| de la Torre-Ubieta, Luis; Stein, Jason L; Won, Hyejung et al. (2018) The Dynamic Landscape of Open Chromatin during Human Cortical Neurogenesis. Cell 172:289-304.e18 |
| Turner, Sarah D; Maurizio, Paul L; Valdar, William et al. (2018) Dissecting the Genetic Architecture of Shoot Growth in Carrot (Daucus carota L.) Using a Diallel Mating Design. G3 (Bethesda) 8:411-426 |
| Keith, Benjamin P; Barrow, Jasmine B; Toyonaga, Takahiko et al. (2018) Colonic epithelial miR-31 associates with the development of Crohn's phenotypes. JCI Insight 3: |
| Maguire, Jack B; Boyken, Scott E; Baker, David et al. (2018) Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design. J Chem Theory Comput 14:2751-2760 |
| Corty, Robert W; Valdar, William (2018) QTL Mapping on a Background of Variance Heterogeneity. G3 (Bethesda) 8:3767-3782 |
| Oreper, Daniel; Schoenrock, Sarah A; McMullan, Rachel et al. (2018) Reciprocal F1 Hybrids of Two Inbred Mouse Strains Reveal Parent-of-Origin and Perinatal Diet Effects on Behavior and Expression. G3 (Bethesda) 8:3447-3468 |
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