AlzCode

AlzCode integrates multi-omics datasets and biological networks to analyze genes and gene sets associated with Alzheimer’s disease and evaluate their relationships with neuropathological and clinical traits.

Key Features:

• Multi-Omics Integration: Combines gene expression data from bulk and single-cell RNA-seq with protein expression datasets for Alzheimer’s disease analysis.
• Network-Based Gene Analysis: Incorporates Alzheimer’s disease-specific biological networks including gene co-expression networks and functional gene networks.
• Clinical and Neuropathological Trait Integration: Integrates datasets containing CERAD scores, Braak staging scores, Clinical Dementia Rating, cognitive assessments, and clinical severity indicators.
• Regulatory and Interaction Data Integration: Includes protein–protein interactions, regulatory networks, sequence similarity information, and miRNA–target interactions.
• Gene and Gene Set Analysis: Performs analytical evaluation of individual genes and gene sets across integrated datasets.

Scientific Applications:

• Alzheimer’s Disease Gene Discovery: Identifies and evaluates candidate genes associated with Alzheimer’s disease.
• Pathway and Network Analysis: Investigates biological pathways and molecular interaction networks involved in Alzheimer’s disease pathogenesis.
• Genotype–Phenotype Association Studies: Examines relationships between gene expression patterns and clinical or neuropathological traits.

Methodology:

AlzCode integrates multi-omics datasets, biological interaction networks, and clinical phenotype data and applies statistical analyses to evaluate associations between genes, molecular networks, and Alzheimer’s disease traits.