Recent advances in high-throughput genomics and proteomics technologies call for a new generation of scientists equipped to extract knowledge from large biological datasets and develop and apply novel data analytical, mathematical modeling and computational simulation techniques. The Predoctoral Training Program in Bioinformatics and Computational Biology (BCB) was established at UNC-Chapel Hill in the Fall 2002 to address these needs. The Program is administered by the Carolina Center for Genome Sciences (CCGS), which was formed in August 2001 with a mission of conducting basic and applied research in all areas related to genome sciences. Twelve BCB faculty members have been recruited by UNC-CH in the last two years, with specialties in evolutionary genetics, information science, proteome analysis, protein folding, statistical genetics, and mathematical biology. In total, 39 computational and experimental genomics faculty distributed among 16 departments and schools are affiliated as BCB mentors. The program consists of four key components: a colloquium, research rotations, formal coursework, and PhD research. The coursework is designed to include three tiers of formal training: prerequisite, core, and advanced courses. Eight specialized core modules have been developed for the BCB program that cover major related areas 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 for two years each. Matching support from several local industrial organizations has been committed to allow additional student support and/or industrial internships. The students will pursue PhD degrees in participating UNC-CH Departments with a common emphasis on Bioinformatics and Computational Biology; future plans include transitioning into independent Phi3 granting curriculum. The requested funds will dovetail with the UNC investment in research infrastructure, faculty recruitment, and education in both genomics and bioinformatics, leveraging intramural as well as extramural industrial support to expand this vital interdisciplinary training program.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM067553-02
Application #
7059911
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Li, Jerry
Project Start
2005-07-01
Project End
2010-06-30
Budget Start
2006-07-01
Budget End
2007-06-30
Support Year
2
Fiscal Year
2006
Total Cost
$112,118
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Type
Schools of Pharmacy
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Borland, David; Yi, Hong; Grant, Gavin D et al. (2018) The Cell Cycle Browser: An Interactive Tool for Visualizing, Simulating, and Perturbing Cell-Cycle Progression. Cell Syst 7:180-184.e4
Lansford, Jefferson L; Dharmasiri, Udara; Chai, Shengjie et al. (2018) Computational modeling and confirmation of leukemia-associated minor histocompatibility antigens. Blood Adv 2:2052-2062
Bonacci, Thomas; Suzuki, Aussie; Grant, Gavin D et al. (2018) Cezanne/OTUD7B is a cell cycle-regulated deubiquitinase that antagonizes the degradation of APC/C substrates. EMBO J 37:
Maurizio, Paul L; Ferris, Martin T; Keele, Gregory R et al. (2018) Bayesian Diallel Analysis Reveals Mx1-Dependent and Mx1-Independent Effects on Response to Influenza A Virus in Mice. G3 (Bethesda) 8:427-445
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

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