PREPROCESSING AND ANALYSIS TOOLS FOR CONTEMPORARY MICROARRAY APPLICATIONS
Rafael Angel Irizarry
Johns Hopkins University, W400 Wyman Park Building, Baltimore, Md 21218
Grant 5R01GM083084-04 from National Institute Of General Medical Sciences
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
Keywords: Aberrant Chromosome; Abnormalities, Chromosomal; Algorithms; Alternate Splicing; Alternative Splicing; Analysis, Data; Binding Sites; Biological; Biology; Characteristics; Chromosomal Aberrations; Chromosomal Alterations; Chromosome Aberrations; Chromosome Alterations; Chromosome Anomalies; Chromosome abnormality; Combining Site; Communities; Computer Programs; Computer software; Cytogenetic Aberrations; Cytogenetic Abnormalities; Data; Data Analyses; Detection; Exhibits; Exons; Gene Expression; Genome; Genomics; Genotype; Glass; Goals; Hot Spot; Hot Spots (Area of Increased Mortality); Investigators; Journals; Label; Location; Magazine; Manufacturer; Manufacturer Name; Measurement; Measures; Method LOINC Axis 6; Methodology; Methods; Methylation; Microarray Analysis; Microarray-Based Analysis; Nucleic Acids; Oligo; Oligonucleotides; Population; Procedures; Proliferating; Promoter; Promoters (Genetics); Promotor; Promotor (Genetics); Property; Property, LOINC Axis 2; Protein Binding; Protein Methylation; Quality Control; RNA Splicing, Alternative; Reactive Site; Reading; Relative; Relative (related person); Reporting; Research; Research Personnel; Researchers; Sampling; Site; Slide; Software; Solid; Solutions; Speed; Speed (motion); Statistical Methods; Surface; Technology; Work; Yang; base; biological systems; computer program/software; experiment; experimental research; experimental study; high throughput technology; improved; insertion-deletion; insertion-deletion mutation; insertion/deletion; insertion/deletion mutation; interest; microarray technology; new technology; novel; population based; research study; tool
Project start date: 2007-09-24
Project end date: 2012-08-31
Budget start date: 1-SEP-2010
Budget end date: 31-AUG-2011
PFA/PA: PA-07-070
5R01GM083084-04 (2010): $433199
Sponsored Links Excellgen http://Excellgen.com
PREPROCESSING AND ANALYSIS TOOLS FOR CONTEMPORARY MICROARRAY APPLICATIONS
Rafael Angel Irizarry, Professor
Johns Hopkins University, Broadway Research Building Suite 117, Baltimore, Md 21205
Grant 5R01GM083084-03 from National Institute Of General Medical Sciences
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
Keywords: Aberrant Chromosome; Abnormalities, Chromosomal; Algorithms; Alternate Splicing; Alternative Splicing; Analysis, Data; Binding Sites; Biological; Biology; Characteristics; Chromosomal Aberrations; Chromosomal Alterations; Chromosome Aberrations; Chromosome Alterations; Chromosome Anomalies; Chromosome abnormality; Combining Site; Communities; Computer Programs; Computer software; Cytogenetic Aberrations; Cytogenetic Abnormalities; Data; Data Analyses; Detection; Exhibits; Exons; Gene Expression; Genome; Genomics; Genotype; Glass; Goals; Hot Spot; Hot Spots (Area of Increased Mortality); Investigators; Journals; Label; Location; Magazine; Manufacturer; Manufacturer Name; Measurement; Measures; Method LOINC Axis 6; Methodology; Methods; Methylation; Microarray Analysis; Microarray-Based Analysis; Nucleic Acids; Oligo; Oligonucleotides; Population; Procedures; Proliferating; Promoter; Promoters (Genetics); Promotor; Promotor (Genetics); Property; Property, LOINC Axis 2; Protein Binding; Protein Methylation; Quality Control; RNA Splicing, Alternative; Reactive Site; Reading; Relative; Relative (related person); Reporting; Research; Research Personnel; Researchers; Sampling; Site; Slide; Software; Solid; Solutions; Speed; Speed (motion); Statistical Methods; Surface; Technology; Work; Yang; base; biological systems; computer program/software; experiment; experimental research; experimental study; high throughput technology; improved; insertion-deletion; insertion-deletion mutation; insertion/deletion; insertion/deletion mutation; interest; microarray technology; new technology; novel; population based; research study; tool
Project start date: 2007-09-24
Project end date: 2012-08-31
Budget start date: 1-SEP-2009
Budget end date: 31-AUG-2010
PFA/PA: PA-07-070
5R01GM083084-03 (2009): $426762
5R01GM083084-02 (2008): $416148
Grants awarded to Rafael Angel Irizarry
ANALYSIS TOOLS AND SOFTWARE FOR SECOND GENERATION SEQUENCING DATA
Rafael Angel Irizarry, Professor
Johns Hopkins University, W400 Wyman Park Building, Baltimore, Md 21218
Grant 1R01HG005220-01 from National Human Genome Research Institute
Abstract: Second-generation sequencing (sec-gen) technology is poised to radically change how genomic data is obtained and used. Capable of sequencing millions of short strands of DNA in parallel, this technology can be used to assemble complex genomes for a small fraction of the price and time of previous technologies. In fact, a recently formed international consortium, the 1000 Genomes Project, plans to sequence the genomes of approximately 1,200 people. The possibility of comparative analysis at the sequence level of a large number of samples across multiple populations may be achievable within the next five years. These datasets also present unprecedented challenges in statistical analysis and data management. For example, a central goal of the 1000 Genomes Project is to quantify across-sample variation at the single nucleotide level. At this resolution, small error rates in sequencing prove significant, especially for rare variants. Furthermore, sec-gen sequencing is a relatively new technology for which potential biases and sources of obscuring variation are not yet fully understood. Therefore, modeling and quantifying the uncertainty inherent in the generation of sequencing reads is of utmost importance. Properly relating this uncertainty to the true underlying variation in the genome, especially, variation between and among populations will be essential for projects that use sec-gen sequencing data to meet their scientific goals. Although genome sequencing is the application that most attention has received, sec-gen technology is also being used to produce quantitative measurements related to applications previously associated with microarrays. Of these, chromatin immunoprecipitation followed by sequencing (ChIP- Seq) has been the most successful. Existing tools have been developed for analyzing one sample at a time. Methodology for drawing inference from multiple samples has not yet been developed. The demand for such methods will increase rapidly as the technology becomes more economical and multiple samples become standard. Other applications for which statistical methodology is needed are RNA and microRNA transcription analysis. In all these sequencing applications, a number of critical steps are required to convert raw intensity measures into the sequence reads that will be used in down-stream analysis. Ad-hoc approaches, that assign weights to each base call, are unsuitable. Our goal is to create a sound and unified statistical and computational methodology for representing and managing uncertainty throughout the sec-gen sequencing data analysis pipeline built on a robust, modular and extensible software platform. Second-generation sequencing technology is poised to radically change how genomic data is obtained and used. These datasets also present unprecedented challenges in statistical analysis and modeling and quantifying uncertainty inherent in the generation of sequencing reads is of utmost importance. We will develop data analysis tools for widely used applications using statistical methods that account for this uncertainty
Keywords: Accounting; Algorithms; Analysis, Data; Attention; Bioconductor; Biological; CHIP assay; ChIP (chromatin immunoprecipitation); Characteristics; Communities; Complex; Computer Programs; Computer Software Tools; Computer software; Computing Methodologies; DNA; Data; Data Analyses; Data Set; Dataset; Deoxyribonucleic Acid; Detection; Drugs, Nonproprietary; Fluorescence; Gene Expression; Gene Products, RNA; Gene Transcription; General Transcription Factor Gene; Generations; Generic Drugs; Genetic Transcription; Genome; Genomics; Genotype; Goals; Hand; Infrastructure; International; Label; Manufacturer; Manufacturer Name; Maps; Marketing; Measurement; Measures; Method LOINC Axis 6; Methodology; Methods; Methods and Techniques; Methods, Other; Methylation; Metric; Modeling; Models, Statistical; Nucleotides; Play; Population; Position; Positioning Attribute; Price; Probabilistic Models; Probability; Procedures; Process; Protein Methylation; Proto-Oncogene, Transcription Factor; RNA; RNA Expression; RNA, Non-Polyadenylated; Reading; Regulatory Element; RegulatoryElement; Reporting; Research; Research Infrastructure; Resolution; Ribonucleic Acid; Role; Sampling; Shapes; Software; Software Tools; Sound; Sound - physical agent; Source; Spinal Column; Spine; Statistical Methods; Statistical Models; Stream; Structure; Systematic Bias; Techniques; Technology; Time; Tools, Software; Transcription; Transcription factor genes; Transcription, Genetic; Uncertainty; Variant; Variation; Vertebral column; Weight; Work; backbone; base; chromatin immunoprecipitation; comparative; computational methodology; computational methods; computational tools; computer methods; computer program/software; computerized tools; data management; design; designing; develop software; developing computer software; doubt; experience; experiment; experimental research; experimental study; flexibility; generic; genome sequencing; high throughput technology; improved; instrument; meetings; new technology; open source; pricing; public health relevance; research study; social role; software development; sound; tool; user-friendly
Relevance: Second-generation sequencing technology is poised to radically change how genomic data is obtained and used. These datasets also present unprecedented challenges in statistical analysis and modeling and quantifying uncertainty inherent in the generation of sequencing reads is of utmost importance. We will develop data analysis tools for widely used applications using statistical methods that account for this uncertainty
Project start date: 2010-08-11
Project end date: 2013-05-31
Budget start date: 11-AUG-2010
Budget end date: 31-MAY-2011
PFA/PA: PA-07-070
1R01HG005220-01 (2010): $410000
Preprocessing And Analysis Tools For Contemporary Microarray Applications
Rafael Angel Irizarry, Associate Professor
Biostatisticsjohns Hopkins University
w400 Wyman Park Building
baltimore, Md 212182680
Grant 1R01GM083084-01 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
1R01GM083084-01 (2007): $450058
SOFTWARE FOR THE STATISTICAL ANALYSIS OF MICROARRAY PROBE LEVEL DATA
Rafael Angel Irizarry
Department/ Educational Institution Type:
Grant 5R01RR021967-03 from National Center For Research Resources
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
Keywords: Algorithms; base; Basic Research; Basic Science; Benchmarking; Best Practice Analysis; Bioconductor; Biology; Communities; computer program/software; Computer Programs; Computer software; Data; Data Analysis, Statistical; Data Interpretation, Statistical; Data Set; Dataset; develop software; developing computer software; Development; Development and Research; Documentation; Education; Educational aspects; Emergent Technologies; Emerging Technologies; Environment; experiment; experimental research; experimental study; flexibility; Gene Expression; genome-wide; Goals; graphic user interface; Graphical interface; graphical user interface; improved; Individual; Internet; interoperability; Language; Mails; Maintenance; Maintenances; Manufacturer; Manufacturer Name; Maps; Measurement; Measures; Medical; member; Memory; Method LOINC Axis 6; Methodology; Methods; Microarray Analysis; microarray technology; Microarray-Based Analysis; Modeling; Models, Statistical; new technology; open source; Probabilistic Models; probe-level data; Procedures; Process; Programming Languages; programs; Programs (PT); Programs [Publication Type]; psychomotor reaction time; Quality Control; R & D; R&D; Reaction Time; Relative; Relative (related person); Reporting; Research; research and development; research study; Response RT; Response Time; Software; software development; Speed; Speed (motion); Statistical Data Analyses; Statistical Data Interpretation; Statistical Models; Structure; Technology; Text; tool; TXT; Update; user-friendly; web; Work; world wide web; Writing; WWW
Project start date: 2007-09-02
Project end date: 2011-06-30
Budget start date: 1-JUL-2009
Budget end date: 30-JUN-2011
PFA/PA: PAR-05-057
5R01RR021967-03 (2009): $267792
5R01RR021967-02 (2008): $277192
1R01RR021967-01A2 (2007): $303446
PREDOCTORAL BIOSTATISTICS TRAINING IN GENETICS/GENOMICS
Rafael Angel Irizarry, Professor
Johns Hopkins University, W400 Wyman Park Building, Baltimore, Md 21218
Grant 5T32GM074906-05 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; Genetic; Genomics; Training; pre-doc; pre-doctoral; predoc; predoctoral; statistics/biometry
Project start date: 2006-08-01
Project end date: 2011-06-30
Budget start date: 1-JUL-2010
Budget end date: 30-JUN-2011
PFA/PA: PAR-04-132
5T32GM074906-05 (2010): $111814
5T32GM074906-04 (2009): $130542
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