The successful training program in Bioinformatics and Integrative Genomics (BIG) has been transferred from the MIT-Harvard Division of Health Sciences and Technology to the Harvard Medical School Division of Medical Sciences (DMS). This entails incremental changes in the curriculum, because of full cross-registration and a change in focus of the clinical experience away from general physiology and towards medical genetic clinics, and clinical genetics laboratory experience. BIG continues to focus on the growing gap between the ability to generate copious amounts genomic data and the ability to systematically and efficiently make sense of all this data, particularly for clinical relevance. Ths program admits students with proven excellence in quantitative science and seeks to train competent, 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 for formulating and resolving fundamental questions in biology and medicine. The BIG training program draws on three significant strengths of the local academic environment: the breadth of talented investigators at Harvard, the Medical School and affiliated hospitals, and MIT, the rich variety of quantitatively-oriented courses in advanced genomics-bioinformatics offered at Harvard and MIT, and national leadership of BIG faculty in harnessing clinical processes and electronic health records for genome-scale disease research. As a new track within DMS, the BIG pre-doctoral program provides a three legged curriculum: 1) rigorous course work in machine learning, probability theory, decision science, and biostatistics 2) in-depth courses that include the range of genome-scale measurements applied to population studies, clinical medicine and basic biology 3) survey of the larger scope of biomedical sciences as well as a rich, unique clinical experience focused on genetics clinics and genetic laboratory testing.
Integrating genomics into the practice of medicine and biomedical research is a primary goal of the National Human Genome Research Institute's strategic plan. The Harvard University Bioinformatics and Integrative Genomics (BIG) Program meets this goal by providing students trained in the quantitative sciences the intellectual tools t integrate genomics with medical science, access to big data from healthcare systems to experiment with methods of leveraging healthcare data in genomic science, faculty who are leading efforts to integrate genome-scale results into clinical practice, and faculty who are expert in genomics and bioinformatics.
|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-Chloé; 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|
Showing the most recent 10 out of 79 publications