There is a critical need to identify the molecular signatures that can define cell states and predict disease progression in C9orf72-associated amyotrophic lateral sclerosis and frontotemporal dementia (c9ALS/FTD) at a systems level. This P01 application proposes to investigate c9ALS/FTD pathomechanisms that can inform new therapeutic targets and identify biomarkers for diagnosing and prognosticating disease. Functional genomics approaches such as those taken in Projects 1-3 will generate hundreds of thousands of data points requiring sophisticated analytical methods for their biological interpretation. The adoption of network-based approaches has been a natural step as most biological systems can be accurately modeled and represented using these methods. The Bioinformatics and Biostatistics Core (Core D) will therefore support investigators in developing strategies for multi-omics data analysis and data integration of transcriptomic and proteomic data generated in Projects 1-3, and in applying additional state of the art data analytics to individual projects. Core D will also continue to assist Projects 1 and 3 by performing power analysis to address sample sizes as described in the proposed projects. In addition, Core D will promote uniform standards for data reporting through a common data dictionary and a web server to enable data exchange among the investigators. Specifically, Core D will work with investigators across Projects 1-3 and Cores B and C to develop a standardized set of descriptors and parameters appropriate for data harmonization and standard formats for raw and processed data.
The Specific Aims are the following:
Specific Aim 1. Provide a framework for standardized data-sharing and analyses of transcriptomics and proteomics studies. To provide infrastructure and harmonization among all projects, Core D will: 1) design and maintain a web-portal on a secure web server; 2) work with investigators to develop a standardized set of descriptors and parameters appropriate for data harmonization; and 3) perform power analysis to justify sample size and assist with statistical modeling of clinical information with confounding variables.
Specific Aim 2. Construct multi-scale gene/protein networks and integrate with single-cell transcriptomics to identify core networks altered in c9ALS/FTD. To support bioinformatic analyses for all Projects, Core D will carry-out data integration.
Specific Aim 3. Prioritize networks and bioinformatically validate the transcriptomic and proteomic discoveries by integrating with external published and unpublished datasets. To prioritize key drivers of c9ALS and bioinformatically validate the findings, Core D will: 1) develop and implement bioinformatic approaches to validate major findings of the Projects with multiple datasets encompassing human neurodegenerative disease cases and corresponding mouse models; and 2) define core regulators of c9ALS by integrating multi-dimensional data generated in Projects 1-3.
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