We propose to continue the Bioinformatics and Integrative Genomics (BIG) pre-doctoral training program at Harvard Medical School. Since its inception in 2000, this program has provided rigorous multidisciplinary education in genome sciences for those students with proven excellence in quantitative fields. The gap between the ability to generate large amounts genomic data and the ability to efficiently analyze and interpret them continues to grow. Our program aims to train multi-disciplinary investigators who are capable of both integrating the genome-scale technologies into their experimental investigations and synthesizing the resulting datasets into coherent knowledge frameworks and mathematical models. The BIG training program draws on three significant strengths of the local academic environment: the breadth of talented investigators at Harvard Medical School and its affiliated hospitals and research institutes; the rich variety of quantitatively-oriented courses in bioinformatics and genomics offered at Harvard and MIT; and national leadership of BIG faculty in genome-scale disease research, including harnessing of genomic approaches to clinical applications. The BIG pre-doctoral curriculum includes the following: 1) courses in fundamentals of genetics and molecular biology, taken alongside graduate students in the biological sciences; 2) statistics and machine learning, with focus on their applications to population studies, clinical medicine and basic biology; 3) survey of the larger scope of biomedical sciences, including medical informatics and translational medicine. This training program will continue to produce leaders, both in academia and industry, who are using genomic sciences to advance biomedical research and its application to medicine.

Public Health Relevance

Integrating genomics into the practice of biomedical research and medicine is a primary goal of the National Human Genome Research Institute's strategic plan. The Bioinformatics and Integrative Genomics (BIG) Program at Harvard Medical School meets this goal by providing students trained in the quantitative sciences the intellectual tools to integrate genomics with biomedical sciences, access to ?big data? from genome sciences and healthcare systems, and faculty mentors who are experts in genomics and bioinformatics. This program will continue to produce leaders who are using genomic sciences to advance biomedical research and clinical practice.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Institutional National Research Service Award (T32)
Project #
5T32HG002295-18
Application #
9990832
Study Section
National Human Genome Research Institute Initial Review Group (GNOM)
Program Officer
Gatlin, Tina L
Project Start
2001-09-30
Project End
2023-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
18
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Pediatrics
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
Conway, Jake R; Kofman, Eric; Mo, Shirley S et al. (2018) Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med 10:93
Fang, Chao; Zhong, Huanzi; Lin, Yuxiang et al. (2018) Assessment of the cPAS-based BGISEQ-500 platform for metagenomic sequencing. Gigascience 7:1-8
Wala, Jeremiah A; Bandopadhayay, Pratiti; Greenwald, Noah F et al. (2018) SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res 28:581-591
Lodato, Michael A; Rodin, Rachel E; Bohrson, Craig L et al. (2018) Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359:555-559
Brown, Adam S; Patel, Chirag J (2018) A review of validation strategies for computational drug repositioning. Brief Bioinform 19:174-177
Brown, Adam S; Rasooly, Danielle; Patel, Chirag J (2018) Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning. CPT Pharmacometrics Syst Pharmacol 7:124-129
Sagers, Jessica E; Brown, Adam S; Vasilijic, Sasa et al. (2018) Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma. Sci Rep 8:5437
Luber, Jacob M; Tierney, Braden T; Cofer, Evan M et al. (2018) Aether: leveraging linear programming for optimal cloud computing in genomics. Bioinformatics 34:1565-1567
Villani, Alexandra-ChloƩ; Satija, Rahul; Reynolds, Gary et al. (2017) Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356:
Kosmicki, Jack A; Samocha, Kaitlin E; Howrigan, Daniel P et al. (2017) Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat Genet 49:504-510

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