Detection and characterization of replication origins defined by DNA polymerase epsilon

Background Although the process of DNA replication is highly conserved the location of origins of replication (ORI) may vary from one tissue to the next or one round of replication to the next in eukaryotes, suggesting flexibility in the choice of locations to initiate replication. Lists of human ORI therefore vary widely in number and location and there are no methods available to compare them. Results Here we report the genome-wide localization of ORI in POLE-mutated human tumors using whole genome sequencing data. Mutations accumulated after many rounds of replication of unsynchronized dividing cell populations in tumors allow to identify constitutive origins, which we show are shared with high-fidelity between individuals and tumor types. Using a Smith–Waterman-like dynamic programming approach, we compared replication origin positions obtained from multiple different methods. The comparison allowed us to define a consensus set of replication origins, identified consistently by multiple ORI detection methods. Conclusions Many DNA features co-localized with the consensus set of ORI, including chromatin loop anchors, G-quadruplexes, S/MARs and CpGs. Among all features, the H2A.Z histone exhibited the most significant association. Our results show that mutation-based detection of replication origins is a viable approach to determining their location and associated sequence features. ### Competing Interest Statement The authors have declared no competing interest. * G4 : G-quadruplexes ICGC : International Cancer Genome Consortium MNVA : Multiple numeric vector alignment mORI : mutationally-defined ORI NVA : Numeric vector alignment ORI : origins of replication PMA : POLE-exo associated Mutation Asymmetry score RFD : replication fork directionality S/MARs : scaffold/nuclear matrix attached regions TADs : topologically associating domains TCGA : The Cancer Genome Atlas TSS : transcriptions start sites WGS : whole genome sequencing data