GESPA

GESPA predicts the pathogenicity of non-synonymous single nucleotide polymorphisms (nsSNPs) and associates them with disease phenotypes by evaluating amino acid conservation across orthologs and paralogs and integrating medical literature evidence.

Key Features:

• Pathogenicity prediction: Evaluates amino acid conservation across orthologs and paralogs to infer the functional impact of nsSNPs.
• Disease phenotype association: Predicts specific disease phenotypes linked to nsSNPs using combined computational and literature evidence.
• Medical literature integration: Incorporates data from medical literature to contextualize and supplement computational predictions.
• Validation on variant databases: Developed and validated using variant datasets such as humsavar, ClinVar, and humvar.
• SQL-based cloud data management: Employs fast SQL-based cloud storage and retrieval systems for data handling and scalability.

Scientific Applications:

• Variant interpretation for clinical genetics: Assists prioritization of nsSNPs for clinical variant interpretation and genetic studies.
• Identification of genetic markers: Aids identification of potential genetic markers for disease susceptibility and progression.
• Genomics and personalized medicine research: Supports research in genomics, personalized medicine, and molecular biology by linking nsSNPs to phenotypes.

Methodology:

Performs computational analyses of evolutionary conservation by assessing the conservation status of amino acids in orthologous and paralogous sequences and integrates medical literature data to predict disease associations.