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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
Published on: March 22, 2018
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An evolutionary model identifies the main evolutionary biases for the evolution of genome-replication profiles
Rossana Droghetti1, Nicolas Agier2, Gilles Fischer2
1Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, Milan, Italy.
Elife
|May 20, 2021
Summary
The evolution of genome replication timing in yeast is shaped by the birth and death of replication origins. Evolutionary pressures penalize stalled forks and favor the loss of inefficient origins.
Area of Science:
- * Evolutionary biology
- * Molecular genetics
- * Genomics
Background:
- * The temporal program of genome replication in yeast species of the *Lachancea* clade suggests replication origin dynamics drive evolutionary changes.
- * Understanding the evolutionary forces shaping replication timing is crucial for deciphering genome stability and function.
Purpose of the Study:
- * To develop and apply an evolutionary model to identify biases in replication origin dynamics.
- * To investigate the evolutionary pressures influencing the birth and death of replication origins.
- * To establish a predictive framework for studying replication timing evolution.
Main Methods:
- * Developed a birth-death process model for replication origins.
- * Utilized comparative genomic data from *Lachancea* yeast species.
- * Compared evolutionary models against empirical data to identify key pressures.
Main Results:
- * Replication origin birth and loss events are significantly influenced by evolutionary pressures.
- * Origins associated with higher double-stall probability of replication forks are disfavored.
- * Less efficient replication origins are more susceptible to evolutionary loss.
Conclusions:
- * The study provides an empirically grounded framework for evolutionary studies of replication timing.
- * Replication timing profiles are shaped by specific evolutionary biases acting on origin dynamics.
- * This model advances our understanding of genome replication evolution.
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