The ultimate goal of the Program is to develop methodologies to study cancer. One important aspect of it is to understand the biology, or function, underlying the large amount of data that will be analyzed. The four projects in this Program will deal with data and annotations at different levels. For example, genetic variation data will be analyzed in Project 1, gene and protein data is used in Project 2, 3 and 4, while microbiome and metabolites data is used in Project 3. Functional annotations to these datasets will be crucial for each project to properly analyze the data and interpret the results. The key focus of Core B is to support these projects by providing resources to annotate the data. The Core will work very closely with Core D (Data analysis and software development), which provides support in centralized data storage. The project will leverage the existing in-house annotation tools, for example, ANNOVAR for genetic variant annotation and PANTHER for gene/protein annotation, and identify external resources, prioritize and build an integrative and comprehensive infrastructure for annotation. The system will allow us to annotate and re-annotate data that are brought into the Program by Core D. It is crucial to maintain all annotations up to date, so it should make frequently updates from the source. It should also have a user-friendly interface to allow members in the Program to annotate the data while they are generating them, browse and identify the data of interest and retrieve shared results by other researchers.

Public Health Relevance

The Functional Annotation Core (Core B) will identify internal and external annotation resources, including public annotation data and annotation software and tools, and provide annotations to the colorectal cancer data collected by Core D to produce the master datasets. In addition, the Core will provide support for Program investigators to add additional annotations by building web interfaces, training and organizing workshops.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
1P01CA196569-01A1
Application #
9072854
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
Name
University of Southern California
Department
Type
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90032
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 :
Ritz, Beate R; Chatterjee, Nilanjan; Garcia-Closas, Montserrat et al. (2017) Lessons Learned From Past Gene-Environment Interaction Successes. Am J Epidemiol 186:778-786
Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770
Thomas, Duncan C (2017) Estimating the Effect of Targeted Screening Strategies: An Application to Colonoscopy and Colorectal Cancer. Epidemiology 28:470-478
Rao, D C; Sung, Yun J; Winkler, Thomas W et al. (2017) Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale. Circ Cardiovasc Genet 10:
The Gene Ontology Consortium (2017) Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res 45:D331-D338
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

Showing the most recent 10 out of 28 publications