The long-term objective of this project is to enable comprehension and analysis of high-throughput genomic data using advanced statistical and bioinformatics approaches. The vehicle for attaining this objective is the Bioconductor project, based on the R statistical programming language and encompassing an established repository of more than 400 software packages. Bioconductor software packages are used in many areas of health-related academic, government, pharmaceutical, and clinical pursuits. Typical uses include microarray or RNA-seq differential representation in case / control or more elaborate experimental designs; characterization of copy number, single nucleotide polymorphism, or other variants assayed with microarray or sequence data;assaying regulatory or epigenetic changes with array or sequence-based (e.g., ChlP-seq) technologies;high-throughput analysis of flow cytometric data;and other high-throughput (e.g., imaging) assays.
Specific aims of this proposal include: 1, Enabling Bioconductor software distribution and use by a wide community of users and developers;2, Developing computational analytic infrastructure needed to track rapidly emerging and computationally demanding data types;and 3, Contributing new statistical methods for genome scale biology, including clinical application of microarray and RNA-seq data, analysis of the genetics of gene expression, and approaches to comparative assessment of analytic methodologies. The overall approach involves production and maintenance of internet-based resources for software distribution and support, ensuring conformance of Bioconductor packages to high standards of software assurance, and significant documentation and training efforts to enable Bioconductor use and development by a broad community;focused efforts on software implementations for the representation and processing of large data sets and new data types, integrated solutions for efficient software deployment and re-use in third party projects, and data structures and approaches that foster reproducible research;and statistical methods development and implementation as new Bioconductor packages. Software from the Bioconductor pro eci will contribute to analysis and comprehension of genome-scale data on a daily basis in national and international settings. Bioconductor N serve as a conduit for introduction of sophisticated methods to appropriately analyze existing and new high throughput data types. Bioconductor provides an important platform on which doctoral and post-doctoral individuals are trained to engage high-throughput data using statistically appropriate methodologies.

Public Health Relevance

Modern health sciences researchers are gaining access to increasingly large and complicated data, for instance entire genome sequences. Effective interpretation of this data is as essential to understanding fundamental biological principles as it is to informing personalized medicine. The S/oconc/uctor project facilitates development and use of statistical software to enable researchers to arrive at this understanding.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Biotechnology Resource Cooperative Agreements (U41)
Project #
5U41HG004059-07
Application #
8337802
Study Section
Special Emphasis Panel (ZHG1-HGR-M (O2))
Program Officer
Bonazzi, Vivien
Project Start
2006-09-28
Project End
2016-02-29
Budget Start
2012-03-01
Budget End
2013-02-28
Support Year
7
Fiscal Year
2012
Total Cost
$1,235,329
Indirect Cost
$669,674
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Obenchain, Valerie; Lawrence, Michael; Carey, Vincent et al. (2014) VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30:2076-8
Shioda, Toshi; Rosenthal, Noel F; Coser, Kathryn R et al. (2013) Expressomal approach for comprehensive analysis and visualization of ligand sensitivities of xenoestrogen responsive genes. Proc Natl Acad Sci U S A 110:16508-13
Lawrence, Michael; Huber, Wolfgang; Pages, Herve et al. (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9:e1003118