SNP2APA

SNP2APA maps single nucleotide polymorphisms (SNPs) that affect alternative polyadenylation (APA) events in human cancers to enable analysis of APA-associated genetic variation and clinical associations.

Key Features:

• Identification of apaQTLs: Systematic identification of APA quantitative trait loci (apaQTLs) across 32 cancer types, comprising 467,942 cis-apaQTLs and 30,721 trans-apaQTLs.
• Dataset composition: Integration of genotype and APA data from 9,082 samples sourced from The Cancer Genome Atlas (TCGA) and The Cancer 3'UTR Altas (TC3A).
• Clinical and GWAS integration: Integration with patient survival information and genome-wide association study (GWAS) loci, identifying 2,154 apaQTLs associated with patient survival time and 151,342 apaQTLs located within GWAS loci.
• Predictive PAS motif analysis: Use of polyadenylation signal (PAS) motif prediction algorithms to forecast the effects of SNPs on polyadenylation signals.

Scientific Applications:

• Investigating SNP effects in cancer: Studying how SNP-induced APA changes contribute to cancer development and progression.
• Therapeutic target exploration: Exploring regulatory networks influenced by APA to prioritize potential therapeutic targets.
• Personalized medicine and prognosis: Correlating genetic variants with patient outcomes to inform personalized medicine and prognostic studies.

Methodology:

SNP2APA was developed from genotype and APA data of 9,082 samples from TCGA and TC3A, with systematic identification of SNPs impacting APA events across 32 cancer types and integration of identified apaQTLs with survival and GWAS data.