{"doi":"10.1093/nar/gkad576","title":"Predicting functional consequences of SNPs on mRNA translation via machine learning","abstract":"The functional impact of single nucleotide polymorphisms (SNPs) on translation has yet to be considered when prioritizing disease-causing SNPs from genome-wide association studies (GWAS). Here we apply machine learning models to genome-wide ribosome profiling data to predict SNP function by forecasting ribosome collisions during mRNA translation. SNPs causing remarkable ribosome occupancy changes are named RibOc-SNPs (Ribosome-Occupancy-SNPs). We found that disease-related SNPs tend to cause notable changes in ribosome occupancy, suggesting translational regulation as an essential pathogenesis step. Nucleotide conversions, such as 'G → T', 'T → G' and 'C → A', are enriched in RibOc-SNPs, with the most significant impact on ribosome occupancy, while 'A → G' (or 'A→ I' RNA editing) and 'G → A' are less deterministic. Among amino acid conversions, 'Glu → stop (codon)' shows the most significant enrichment in RibOc-SNPs. Interestingly, there is selection pressure on stop codons with a lower collision likelihood. RibOc-SNPs are enriched at the 5'-coding sequence regions, implying hot spots of translation initiation regulation. Strikingly, ∼22.1% of the RibOc-SNPs lead to opposite changes in ribosome occupancy on alternative transcript isoforms, suggesting that SNPs can amplify the differences between splicing isoforms by oppositely regulating their translation efficiency.","journal":"Nucleic Acids Research","year":2023,"id":351385,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9601,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":484470,"name":"Liang Chen","orcid":"0000-0001-6164-4553","position":1,"is_corresponding":false},{"id":781453,"name":"Zheyu Li","orcid":"0009-0008-4952-8523","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T01:12:41.740237Z","pmid":"37427781","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}