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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM067553-07
Application #
8102724
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Hagan, Ann A
Project Start
2005-07-01
Project End
2015-06-30
Budget Start
2011-07-01
Budget End
2012-06-30
Support Year
7
Fiscal Year
2011
Total Cost
$141,977
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biochemistry
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Keele, Gregory R; Prokop, Jeremy W; He, Hong et al. (2018) Genetic Fine-Mapping and Identification of Candidate Genes and Variants for Adiposity Traits in Outbred Rats. Obesity (Silver Spring) 26:213-222
Wolff, Samuel C; Kedziora, Katarzyna M; Dumitru, Raluca et al. (2018) Inheritance of OCT4 predetermines fate choice in human embryonic stem cells. Mol Syst Biol 14:e8140
Corty, Robert W; Kumar, Vivek; Tarantino, Lisa M et al. (2018) Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice. G3 (Bethesda) 8:3783-3790
Levy, Asaf; Salas Gonzalez, Isai; Mittelviefhaus, Maximilian et al. (2018) Genomic features of bacterial adaptation to plants. Nat Genet 50:138-150
Metz, Kathleen S; Deoudes, Erika M; Berginski, Matthew E et al. (2018) Coral: Clear and Customizable Visualization of Human Kinome Data. Cell Syst 7:347-350.e1
Popova, Mariya; Isayev, Olexandr; Tropsha, Alexander (2018) Deep reinforcement learning for de novo drug design. Sci Adv 4:eaap7885
Goldfarb, Dennis; Lafferty, Michael J; Herring, Laura E et al. (2018) Approximating Isotope Distributions of Biomolecule Fragments. ACS Omega 3:11383-11391
Saito, Ryoichi; Smith, Christof C; Utsumi, Takanobu et al. (2018) Molecular Subtype-Specific Immunocompetent Models of High-Grade Urothelial Carcinoma Reveal Differential Neoantigen Expression and Response to Immunotherapy. Cancer Res 78:3954-3968
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

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