SNPDelScore

SNPDelScore evaluates deleterious effects of noncoding single nucleotide polymorphisms (SNPs) across the human genome to prioritize candidate phenotype-causal regulatory variants.

Key Features:

• Integration of Multiple Methods: Utilizes a panel of predictive methodologies including artificial intelligence and deep learning approaches to quantify noncoding mutation deleteriousness.
• Precomputed Deleteriousness Scores: Precomputes deleteriousness scores for all common SNPs across 44 human cell lines.
• Consensus Identification: Compares scores from multiple methods to identify consensus SNPs with the highest deleteriousness signals as candidate phenotype-causal variants.
• Functional and Genomic Contextualization: Incorporates GWAS Catalog annotations, libraries of transcription factor-binding sites, and genic characteristics of mutations to contextualize scores.

Scientific Applications:

• Disease Research: Prioritizes noncoding variants that may contribute to disease etiology by identifying high-deleteriousness regulatory SNPs.
• Genomic Studies: Supports investigation of gene regulatory elements and the impact of noncoding variants on genomic regulation and function.
• Cross-disciplinary Collaboration: Provides comprehensive variant-level data that can be integrated into population genetics and functional genomics studies.

Methodology:

Integration of multiple predictive models (including artificial intelligence and deep learning approaches), precomputation of deleteriousness scores for common SNPs across 44 human cell lines, and comparison of scores across methods with inclusion of GWAS Catalog annotations, transcription factor‑binding site libraries, and genic characteristics.