MetaCore: Enabling Systems Biology Research through Pathway Analysis

  • Registration Closed
  • Aug 02, 2017
  • 09:30 AM to 04:00 PM
  • NIH Library Training Room

Session Description

MetaCore is a curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The data types include microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out, and other custom interactions which can all be analyzed in tandem. MetaCore is based on a high-quality, manually-curated database of transcription factors, receptors, ligands, kinases, drugs, and endogenous metabolites as well as other molecular classes; species-specific directional interactions between protein-protein, protein-DNA and protein-RNA, drug targeting, and bioactive molecules and their effects; signaling and metabolic pathways represented on maps and networks; and rich ontologies for diseases and processes with hierarchical or graphic output.

MetaCore’s analytical package includes intuitive tools for searching and data visualization, enabling the identification of the most relevant biological pathways, networks, and processes. The Genomic Analysis Tools (GAT) help users upload gene variant files, run genotype analyses, filter and annotate variants, and perform downstream analyses of their filtered variants.

Attendees will learn to:

  • Mine for gene, protein, disease, and compound information with supporting journal references
  • Upload gene lists and expression data and filter them on ontologies or significance
  • Perform functional enrichment of their own data, or quality controlled publically available data
  • Explore Canonical Pathway Maps to find connections between a gene/protein/drug and a known signaling pathways
  • Build signaling networks to investigate the molecular neighborhood around a molecule
  • Use the Genomic Analysis Tools (GAT) to upload, compare, and analyze gene variants

Benefits:

  • Simplify the generation and prioritization of experimental hypotheses following ‘omics’ or NGS experiments
  • Assess and validate potential therapeutic targets and disease biomarkers
  • Access to easy-to-use workflows and reports

 

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