An enduring challenge in biomedical research is deciphering the function of genes, and in particular how they work together to influence human health and disease. This project centers on the development and application of computational databases, tools and techniques for the study of large quantities of functional genomics data with a focus on the nervous system, building on our experience in meta-analysis of gene expression profiling data.
Our first aim focuses on refining and applying methods for computational analysis of gene function in the nervous system, based on gene networks derived from expression profiling and other public data.
Our second aim i s to study the relationships between phenotypes and gene expression patterns, and applying the approaches to expression changes associated with diseases of the nervous system. Third, we propose to develop new visualization methods for gene networks, and to incorporate data on transcriptional gene regulation including transcription factor binding sites and genetic variation in gene expression. These resources will be designed to interoperate with other neuroinformatics databases, and disseminated through our """"""""Gemma"""""""" web-based database system.

Public Health Relevance

Disorders of the brain such as schizophrenia, autism spectrum disorder, Alzheimer's disease and stroke take a huge toll on society. Improving our understanding of how genes and gene networks contribute to normal and pathological processes in the brain will contribute to the development of improved diagnostics and treatments. This project will advance such understanding in multiple ways, by developing and applying computational analyses of huge quantities of genomics data on the brain.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM076990-08
Application #
8330161
Study Section
Special Emphasis Panel (ZRG1-NT-B (08))
Program Officer
Lyster, Peter
Project Start
2005-08-01
Project End
2013-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
8
Fiscal Year
2012
Total Cost
$240,537
Indirect Cost
$15,827
Name
University of British Columbia
Department
Type
DUNS #
251949962
City
Vancouver
State
BC
Country
Canada
Zip Code
V6 1-Z3
Fortelny, Nikolaus; Butler, Georgina S; Overall, Christopher M et al. (2017) Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein-Protein Interactions. Mol Cell Proteomics 16:1038-1051
Ballouz, Sara; Weber, Melanie; Pavlidis, Paul et al. (2017) EGAD: ultra-fast functional analysis of gene networks. Bioinformatics 33:612-614
Rogic, Sanja; Wong, Albertina; Pavlidis, Paul (2016) Meta-Analysis of Gene Expression Patterns in Animal Models of Prenatal Alcohol Exposure Suggests Role for Protein Synthesis Inhibition and Chromatin Remodeling. Alcohol Clin Exp Res 40:717-27
Jiang, Yuxiang; Oron, Tal Ronnen; Clark, Wyatt T et al. (2016) An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biol 17:184
Ballouz, Sara; Pavlidis, Paul; Gillis, Jesse (2016) Using predictive specificity to determine when gene set analysis is biologically meaningful. Nucleic Acids Res :
Tan, Powell Patrick Cheng; Rogic, Sanja; Zoubarev, Anton et al. (2016) Interactive Exploration, Analysis, and Visualization of Complex Phenome-Genome Datasets with ASPIREdb. Hum Mutat 37:719-26
Fortelny, Nikolaus; Pavlidis, Paul; Overall, Christopher M (2015) The path of no return--Truncated protein N-termini and current ignorance of their genesis. Proteomics 15:2547-52
French, Leon; Liu, Po; Marais, Olivia et al. (2015) Text mining for neuroanatomy using WhiteText with an updated corpus and a new web application. Front Neuroinform 9:13
Ch'ng, Carolyn; Kwok, Willie; Rogic, Sanja et al. (2015) Meta-Analysis of Gene Expression in Autism Spectrum Disorder. Autism Res 8:593-608
Fortelny, Nikolaus; Cox, Jennifer H; Kappelhoff, Reinhild et al. (2014) Network analyses reveal pervasive functional regulation between proteases in the human protease web. PLoS Biol 12:e1001869

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