MetaRanker

MetaRanker 2.0 prioritizes genetic variation in the human genome by integrating heterogeneous datasets to rank protein-coding genes for association with phenotypes and diseases.

Key Features:

• Variant scope: Targets both common and rare frequency genetic variations across the human genome.
• Data integration: Integrates genetic association studies, protein-protein interaction networks, large-scale text-mining outputs, copy number variation information, and gene expression experiment results.
• Genome-wide prioritization: Prioritizes the entire protein-coding segment of the human genome to produce genome-wide gene rankings.
• Candidate shortlisting: Shortlists candidate genes most likely to be relevant for targeted follow-up studies based on aggregated evidence.
• Configurable inputs: Allows specification of particular datasets to tailor the prioritization to specific research needs.
• Computational ranking: Synthesizes and analyzes integrated datasets to rank genes by their likelihood of association with specific phenotypes or diseases.

Scientific Applications:

• Genome-wide gene prioritization: Produces ranked lists of protein-coding genes for use in downstream genetic and functional studies.
• Candidate gene selection: Shortlists genes for targeted experimental validation or follow-up association studies.
• Variant significance assessment: Aids identification of common and rare genetic variations with potential biological significance or clinical relevance.

Methodology:

Synthesizes and analyzes integrated datasets to rank genes based on their likelihood of association with specific phenotypes or diseases, with user-specified dataset selection to tailor analyses.