Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue PLoS Computational Biology Année : 2023

Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths

Résumé

In human and other metazoans, the determinants of replication origin location and strength are still elusive. Origins are licensed in G1 phase and fired in S phase of the cell cycle, respectively. It is debated which of these two temporally separate steps determines origin efficiency. Experiments can independently profile mean replication timing (MRT) and replication fork directionality (RFD) genome-wide. Such profiles contain information on multiple origins’ properties and on fork speed. Due to possible origin inactivation by passive replication, however, observed and intrinsic origin efficiencies can markedly differ. Thus, there is a need for methods to infer intrinsic from observed origin efficiency, which is context-dependent. Here, we show that MRT and RFD data are highly consistent with each other but contain information at different spatial frequencies. Using neural networks, we infer an origin licensing landscape that, when inserted in an appropriate simulation framework, jointly predicts MRT and RFD data with unprecedented precision. We furthermore uncover an analytical formula that predicts intrinsic from observed origin efficiency combined with MRT data. Comparison of inferred intrinsic origin efficiencies with experimental profiles of licensed origins (ORC, MCM) and actual initiation events (Bubble-seq, SNS-seq, OK-seq) show that intrinsic origin efficiency is not solely determined by licensing efficiency. Thus, human replication origin efficiency is set at both the origin licensing and firing steps.
Fichier principal
Vignette du fichier
Arbona_Human.pdf (15.96 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03817417 , version 1 (17-10-2022)

Identifiants

Citer

Jean-Michel Arbona, Hadi Kabalane, Arach Goldar, Olivier Hyrien, Benjamin Audit. Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths. PLoS Computational Biology, 2023, 19 (5), pp.e1011138. ⟨10.1371/journal.pcbi.1011138⟩. ⟨hal-03817417⟩
134 Consultations
19 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More