The Cancer Center's Computational Biology Resource (CBR) leverages the power of interdisciplinary thinking by partnering statistical theorists, experimentalists, and software engineers in a cooperative atmosphere accessible to Cancer Center investigators. This multi disciplinary computational approach to basic biomedical research problems employs advanced computational techniques to uncover and analyze the hidden order in complex data sets, further clarifying complex biological questions. CBR staff are available to Center Staff to facilitate experimental design, data analysis, custom programming, and applications support. Three full-time statistical analysts are available on a first-come, first-served basis to provide Assisted Analysis Support and facilitate access to sequence alignment and data-mining algorithms as well as QTL experimental design and data analysis, microarray image processing, basic quantitative analysis, and subsequent statistical analysis (e.g. cluster analysis). The resource provides expertise in both the use and mining of public databases (GeneBank, Entrez, and MGD) and application of existing analytical tools including BLAST, GCG, Mapmaker, and MapManager. Database design and software engineering are available to Center staff for custom scientific application development on a competitive application basis and in addition support the assisted analysis effort as needed. Ongoing applications training and user support for both imported scientific applications and those developed in-house are also provided. The CBR is tasked with software maintenance, which is impacted by common operating system and platform changes. Computational Biology is equipped with essential telecommunications, both Macintosh and PC computers essential for programming and analytical functions, and networked servers that are available to Staff. The CRB is led by Gary Churchill, Ph.D., a statistical geneticist, who works closely with Cancer Center staff to make all current analytical methodologies available through this highly integrated resource.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
2P30CA034196-19
Application #
6542864
Study Section
Subcommittee E - Prevention &Control (NCI)
Project Start
1996-08-25
Project End
2006-07-31
Budget Start
Budget End
Support Year
19
Fiscal Year
2001
Total Cost
Indirect Cost
Name
Jackson Laboratory
Department
Type
DUNS #
042140483
City
Bar Harbor
State
ME
Country
United States
Zip Code
04609
Becker, Timothy; Lee, Wan-Ping; Leone, Joseph et al. (2018) FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods. Genome Biol 19:38
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