Abstract

The Data Processing and Software Development Core (Core D) will compile databases, assist with data analysis, and develop new user-friendly software programs in support of the overall goals of the program project. The Core will create ?master? colorectal cancer databases that will provide a focal point for inspiring and applying new methods developed by project investigators. The Core will also work with project investigators to translate their research code into user- friendly software programs that can be implemented by outside investigators to apply new methods to their study data. The Core will develop the infrastructure related to data processing and software development necessary to carry out these goals. In general, the activities of this Core will serve a central role in integrating the methods development across projects and in disseminating program findings to the broader scientific community.

Public Health Relevance

The Data Processing and Software Development Core (Core D) will compile master databases that link individual-level colorectal cancer data with genomic annotation information and will assist project investigators with analysis of these data. The Core will also develop new user-friendly software programs that make the novel statistical methods developed by the Program available to investigators in the general scientific community.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
1P01CA196569-01A1
Application #
9072856
Study Section
Special Emphasis Panel (ZCA1)
Project Start
2016-07-01
Project End
2021-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost

  Institution

Name
University of Southern California
Department
Type
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90032

  Publications

Ryser, Marc D; Min, Byung-Hoon; Siegmund, Kimberly D et al. (2018) Spatial mutation patterns as markers of early colorectal tumor cell mobility. Proc Natl Acad Sci U S A 115:5774-5779
Liu, Jie; Liang, Gangning; Siegmund, Kimberly D et al. (2018) Data integration by multi-tuning parameter elastic net regression. BMC Bioinformatics 19:369
Moss, Lilit C; Gauderman, William J; Lewinger, Juan Pablo et al. (2018) Using Bayes model averaging to leverage both gene main effects and G?×? E interactions to identify genomic regions in genome-wide association studies. Genet Epidemiol :
Mi, Huaiyu; Huang, Xiaosong; Muruganujan, Anushya et al. (2017) PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res 45:D183-D189
Gref, Anna; Merid, Simon K; Gruzieva, Olena et al. (2017) Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up. Am J Respir Crit Care Med 195:1373-1383
Thomas, Duncan C (2017) What Does ""Precision Medicine"" Have to Say About Prevention? Epidemiology 28:479-483
Pereira, Miguel; Thompson, John R; Weichenberger, Christian X et al. (2017) Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects. Genet Epidemiol 41:320-331
Chopra, Sameer; Liu, Jie; Alemozaffar, Mehrdad et al. (2017) Improving needle biopsy accuracy in small renal mass using tumor-specific DNA methylation markers. Oncotarget 8:5439-5448
Croteau-Chonka, Damien C; Qiu, Weiliang; Martinez, Fernando D et al. (2017) Gene Expression Profiling in Blood Provides Reproducible Molecular Insights into Asthma Control. Am J Respir Crit Care Med 195:179-188
McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher et al. (2017) Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186:753-761

Showing the most recent 10 out of 28 publications