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The Bioinformatics Core serves as a support network for all VCU Alcohol Research Center (ARC) projects and will interact closely with each component to provide computational, statistical, and research design expertise. The core will play a central role in the data collection, curation, management, storage, and analysis. As such, we will first collect and curate project related data including those generated by the VCUARC and those that are available from other outside sources. Examples of these internal and external data include human linkage and association studies, mouse quantitative trait loci (QTL), microarray data, fly and worm candidate gene networks, and literature searches. The collected data will be integrated via a data management system and updated routinely by computer programs. Many data mining and bioinformatics analyses will be performed for the identification of susceptibility genes, including cross-species homology searches and gene network analysis. The core will also develop, implement, and refine methods for prioritizing genes for additional study. Finally, in anticipation of our future plans for a full Alcohol Research Center application (P50 or P60), we will design and partially implement a user-friendly web-based platform. This platform will provide (1) simultaneous access to the data generated in the projects and to certain public genomic databases and (2) tools for statistical analysis and data presentation. The system will be developed as a prototype for 'Ethanolomics' - a comprehensive cross-species system for genes involved in alcohol response. The overall goal of facilitating data sharing and the application of emerging methods will be to accelerate the understanding of alcohol related traits.
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Constructing A Transcriptomic Atlas of Retrotransposon in Alzheimer's Disease
Deep learning methods to predict the function of genetic variants in orofacial clefts
Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data
Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data
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