Predoctoral Biostatistics Training In Genesis/Genomics
Rafael Angel Irizarry, Associate Professor
Oncology, Sidney Kimmel Comprehensive Cancer Centejohns Hopkins University
Grant 5T32GM074906-04 from National Institute Of General Medical Sciences IRG: ZGM1
Abstract: Goals The JHU Department of Biostatistics proposes a joint MHS-PhD built upon the existing Bioinformatics MHS and Biostatistics PhD programs. The program´s goal is to produce graduates that will be full scientific partners on interdisciplinary biomedical research teams. This will be achieved by integrating rigorous training in biostatistics and bioinformatics design and analysis methods with training and direct participation in translational and cross-disciplinary research in molecular and population genetics. Strengths The JHU biomedical research and education environment is internationally recognized in all areas required to meet our goal. Our program will be built on the successful PhD program in Biostatistics and MHS program in Bioinformatics, and will draw additional strength from the PhD programs in Applied Mathematics & Statistics, Human Genetics, and Genetic Epidemiology. Institutional support includes an outstanding biocomputing infrastructure and a dry laboratory space that provides a common intellectual and physical environment for researchers in all computational aspects of molecular and population genetics to work together with our trainees. Our methodological core faculty has an established record of research in a broad spectrum of design and data analysis problems in genetics and genomics, often coupled with a high-impact substantive research agenda, and a demonstrated record of collaboration and contributions to translational research. Plan The integrated MHS/PhD program has a straightforward overall structure, consisting of four parallel sequences of courses in Genetics, Computing, Statistical Methods, and Theory, complemented by a laboratory rotation, an internship-based MHS capstone project and the PhD theses. The proposed coursework allows for considerably increased flexibility compared to standard biostatistics curricula and includes substantial additional interdisciplinary training. Training grant support will be provided for up to six trainees per year. Each will be supported for the initial 3 years; research assistantships will fund the remaining period. The program will be housed in the Department of Biostatistics and be supported by faculty in the Departments of Biostatistics, Applied Mathematics & Statistics, Epidemiology, Molecular Microbiology & Immunology, and Oncology
Project start date: 2006-08-01
Project end date: 2011-06-30
Sponsored Links Excellgen http://Excellgen.com
Grants awarded to Rafael Angel Irizarry
Preprocessing And Analysis Tools For Contemporary Microarray Applications
Rafael Angel Irizarry, Associate Professor
Biostatisticsjohns Hopkins University
Grant 5R01GM083084-03 from National Institute Of General Medical Sciences IRG: GCAT
Abstract: Microarrays are an example of powerful high throughput genomics tools that are revolutionizing the measurement of biological systems. In this and other technologies, a number of critical steps are required to convert the raw measures into the results relied upon by biologists and clinicians. These data manipulation have enormous influence on the quality of the ultimate measurements and studies that rely upon them. Our group has previously demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Various companies have now incorporated our methods into their data analysis software (e.g. GeneSpring, GeneTraffic). Microarrays are now being used to measure diverse high genomic endpoints including genotype, chromosomal abnormalities including deletions/insertions, protein binding sites, methylation, and alternative splicing. In each case, the genomic units of measurement are short oligonucleotides referred to as probes. Without appropriate understanding of the bias and variance of these measurements, biological inferences based upon probe analysis will be compromised. In these new technologies, we expect our proposed research to produce statistical methods that facilitate improvements similar to those attained with expression arrays. The need for more research of this kind has grown dramatically in recent years, with the rapid expansion of novel uses of the microarray technology. Our long-term goal is to improve the quality of results obtained using microarray experiments via the use of improved statistical methodology. Toward this goal, the current proposal has the following specific aims to develop basic analysis tools for the most popular emerging applications, to develop preprocessing methodology to serve the most urgent needs of the user community, and to develop general statistical methodology for population wide hot-spot detection
Project start date: 2007-09-24
Project end date: 2012-08-31
5R01GM083084-02 (2008): $416148
1R01GM083084-01 (2007): $450058
Software For The Statistical Analysis Of Microarray Probe Level Data
Rafael Angel Irizarry, Associate Professor
Biostatisticsjohns Hopkins University
Grant 5R01RR021967-03 from National Center For Research Resources IRG: ZRG1
Abstract: Microarray technology is a powerful tool for measuring genome-wide expression levels. These arrays have become a standard tool in medical science and basic biology research. In these technologies, a number of critical steps are required to convert the raw data, referred to as probe-level data, into the expression-level measures relied upon by biologists and clinicians. These data manipulations, referred to as pre-processing, have enormous influence on the quality of the ultimate measurements and on the studies that rely upon them. Affymetrix GeneChip expression array technology is the most widely used commercial platform. Our group has previously demonstrated that the use of the alternative pre-processing methodology can substantially improve accuracy and precision of gene expression measurements, relative to the ad-hoc procedures introduced by the manufacturers of this technology. Although a large number of tools exist for the analysis of expression measurements, software for the analysis of probe-level data is quite limited. The further improvement of pre-processing procedures is an important evolving research field and requires the availability of appropriate software. Through our Bioconductor affy R package we provide a flexible environment that is the premier open source tool for the analysis of Affymetrix probe-level data. The software is freely available to all and has become widely used by the research community. In fact, our thousands of users include various members of the research and development team at Affymetrix. Since its first release in May 2002, we have added various extensions, stand-alone software that implements the most used algorithms, and a web-tool for assessment of competing pre-processing algorithms. Furthermore, various commercial products have ported some of our tools making them available to an even larger base of users. Our proposed goal is to continue the support of our software and further develop our tools to increase their usefulness to the research community
Project start date: 2007-09-02
Project end date: 2010-06-30
5R01RR021967-02 (2008): $277192
1R01RR021967-01A2 (2007): $303446
PREDOCTORAL BIOSTATISTICS TRAINING IN GENESIS/GENOMICS
Rafael Angel Irizarry, Professor
Johns Hopkins University, Broadway Research Building Suite 117, Baltimore, Md 21205
Grant 3T32GM074906-04S1 from National Institute Of General Medical Sciences
Abstract: Goals The JHU Department of Biostatistics proposes a joint MHS-PhD built upon the existing Bioinformatics MHS and Biostatistics PhD programs. The program´s goal is to produce graduates that will be full scientific partners on interdisciplinary biomedical research teams. This will be achieved by integrating rigorous training in biostatistics and bioinformatics design and analysis methods with training and direct participation in translational and cross-disciplinary research in molecular and population genetics. Strengths The JHU biomedical research and education environment is internationally recognized in all areas required to meet our goal. Our program will be built on the successful PhD program in Biostatistics and MHS program in Bioinformatics, and will draw additional strength from the PhD programs in Applied Mathematics & Statistics, Human Genetics, and Genetic Epidemiology. Institutional support includes an outstanding biocomputing infrastructure and a dry laboratory space that provides a common intellectual and physical environment for researchers in all computational aspects of molecular and population genetics to work together with our trainees. Our methodological core faculty has an established record of research in a broad spectrum of design and data analysis problems in genetics and genomics, often coupled with a high-impact substantive research agenda, and a demonstrated record of collaboration and contributions to translational research. Plan The integrated MHS/PhD program has a straightforward overall structure, consisting of four parallel sequences of courses in Genetics, Computing, Statistical Methods, and Theory, complemented by a laboratory rotation, an internship-based MHS capstone project and the PhD theses. The proposed coursework allows for considerably increased flexibility compared to standard biostatistics curricula and includes substantial additional interdisciplinary training. Training grant support will be provided for up to six trainees per year. Each will be supported for the initial 3 years; research assistantships will fund the remaining period. The program will be housed in the Department of Biostatistics and be supported by faculty in the Departments of Biostatistics, Applied Mathematics & Statistics, Epidemiology, Molecular Microbiology & Immunology, and Oncology
Keywords: Biometrics; Biometry; Biometry and Biostatistics; Biostatistics; Genomics; Training; pre-doc; pre-doctoral; predoc; predoctoral; statistics/biometry
Project start date: 2009-08-03
Project end date: 2011-08-02
Budget start date: 3-AUG-2009
Budget end date: 2-AUG-2011
PFA/PA: PAR-04-132
3T32GM074906-04S1 (2009): $261084