Our project helps address challenges neurobiologists face when trying to interpret the results of genomics and genetics studies, to identify candidate genes for specific normal or disease processes, and to leverage the huge quantities of available gene expression data. In this phase of the project, we are moving further beyond gene expression into other areas for which integration with genomics data is increasingly important and challenging for neuroscientists, including electrophysiology, diseases, cell types, and epigenetics.
Our first aim i s to develop and apply computational methods and tools for enhanced gene function analysis and prediction in the nervous system. We are proposing to expand our database of genomics data, Gemma, to include re- analyzed data from DNA methylation studies from public databases. We will also enhance Gemma to allow more condition-centric analysis for coexpression and differential expression studies, and comparative analysis of conditions from a gene network and regulation point of view. We will develop and apply novel methods for evaluating genomics data quality to increase the value of the data in Gemma, and to provide contextual information to protect against biases caused by gene multifunctionality (genericity). Second, we are developing and applying NeuroElectro, a knowledgebase system linking quantitative electrophysiological properties and cell types. The system will be expanded to include more cell types and more parameters, especially synaptic properties and treatment and disease conditions, as well as more experimental parameters such as ion concentrations to permit normalization. We will integrate information on gene expression in specific neuron types and brain regions, and perform analyses to identify genes that contribute to electrophysiological properties and their regulators, including potential relationships to diseases. Finally, we are proposing to bring together the data resources and tools we have developed along with third-party resources to add new user- friendly components to Gemma for accessing and analyzing the data to address common questions such as enrichment of gene expression within specific brain regions. This project provides a step in the path to enabling the types of interdisciplinary data analyses that are increasingly essential to progress in neuroscience, putting them in the hands of the researchers where they will have the most direct impact. All of our data and software are designed to be user-friendly, open-source, and freely available to the community, and will be disseminated through our own web site, programmatic interfaces as well as via integration with third-party systems.
Disorders of the brain such as schizophrenia, autism spectrum disorder and Alzheimer's disease 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 new bioinformatics tools and methods for neuroscientists, leveraging computational analyses of huge quantities of genomics and other data relating to brain function.
|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|
|Tripathy, Shreejoy J; Toker, Lilah; Bomkamp, Claire et al. (2018) Assessing Transcriptome Quality in Patch-Seq Datasets. Front Mol Neurosci 11:363|
|Bhuiyan, Shamsuddin A; Ly, Sophia; Phan, Minh et al. (2018) Systematic evaluation of isoform function in literature reports of alternative splicing. BMC Genomics 19:637|
|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|
Showing the most recent 10 out of 20 publications