This proposal attempts to address the growing need for integrated gene expression informatics resources in neuroscience. The last five years have seen a rapid increase in the production of gene expression in all areas of biology. Analyzing this wealth of data is becoming more and more complex. While there are public data repositories where raw data can be published, there are few if any efforts to provide advanced analytic capabilities that address the specific needs of neuroscience. We propose to create such a facility that will allow neuroscientists to perform sophisticated computational analyses of large quantities of expression data coming from multiple laboratories. Data submitters will be able to control access to their data and the results coming from their data. The project will involve developing a database for meta-analysis of neuroscience-related gene expression data, tools for submission of data, and tools and algorithms for accessing the database and analyzing the data. Users will be able to perform meta-analyses of differential expression, coexpression (correlated expression), differential coexpression, expression detection, and of expression patterns in functionally-related groups of genes. In addition the system will incorporate other genomics data such as sequences, gene pathways and protein-protein interactions. By linking to and overlaying such data, users will be able to better predict the importance of particular analysis results. Finally, we will develop methods to incorporate domain-specific data into gene expression microarray analyses, such as neuroanatomy, neural connectivity and brain in situ hybridizations. This project will enable future collaborative efforts to target specific diseases or processes for examination.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM076990-06
Application #
7742635
Study Section
Special Emphasis Panel (ZRG1-MDCN-K (55))
Program Officer
Lyster, Peter
Project Start
2005-08-01
Project End
2011-09-15
Budget Start
2009-12-01
Budget End
2011-09-15
Support Year
6
Fiscal Year
2010
Total Cost
$207,639
Indirect Cost
Name
University of British Columbia
Department
Type
DUNS #
251949962
City
Vancouver
State
BC
Country
Canada
Zip Code
V6 1-Z3
Toker, Lilah; Mancarci, Burak Ogan; Tripathy, Shreejoy et al. (2018) Transcriptomic Evidence for Alterations in Astrocytes and Parvalbumin Interneurons in Subjects With Bipolar Disorder and Schizophrenia. Biol Psychiatry 84:787-796
Tebaykin, Dmitry; Tripathy, Shreejoy J; Binnion, Nathalie et al. (2018) Modeling sources of interlaboratory variability in electrophysiological properties of mammalian neurons. J Neurophysiol 119:1329-1339
Bainer, Russell O; Trendowski, Matthew R; Cheng, Cheng et al. (2017) A p53-regulated apoptotic gene signature predicts treatment response and outcome in pediatric acute lymphoblastic leukemia. Cancer Manag Res 9:397-410
Ballouz, Sara; Weber, Melanie; Pavlidis, Paul et al. (2017) EGAD: ultra-fast functional analysis of gene networks. Bioinformatics 33:612-614
Mancarci, B Ogan; Toker, Lilah; Tripathy, Shreejoy J et al. (2017) Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data. eNeuro 4:
Tripathy, Shreejoy J; Toker, Lilah; Li, Brenna et al. (2017) Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput Biol 13:e1005814
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
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 :

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