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.
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.
|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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