{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Arnedo-Pac C"],"funding":["Asociacion Espanola contra el Cancer","Cancer Research UK","European Research Council","Spanish Ministry of Science","‘la Caixa’ Foundation","NCI NIH HHS","'la Caixa' Foundation ('la Caixa')","CERCA"],"pagination":["6-27"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10883281"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(1)"],"pubmed_abstract":["The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot prope"],"journal":["Molecular systems biology"],"pubmed_title":["Hotspot propensity across mutational processes."],"pmcid":["PMC10883281"],"funding_grant_id":["682398","RTI2018-094095-B-I00","ID 100010434; LCF/BQ/ES18/11670011","CGCATF-2021/100007","OT2 CA278668","GC16173697BIGA"],"pubmed_authors":["Arnedo-Pac C","Lopez-Bigas N","Muinos F","Gonzalez-Perez A"],"additional_accession":[]},"is_claimable":false,"name":"Hotspot propensity across mutational processes.","description":"The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot prope","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jan","modification":"2026-07-15T03:00:57.297Z","creation":"2025-04-07T03:12:20.618Z"},"accession":"S-EPMC10883281","cross_references":{"pubmed":["38177930"],"doi":["10.1038/s44320-023-00001-w"]}}