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Chromosome Replication

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Before a cell can divide, it must accurately replicate all of its chromosomes, including the DNA and its associated histone and non-histone proteins. This process begins at numerous origins of replication during the S phase of the cell cycle in each of a cell’s chromosomes simultaneously. Certain nucleotides can act as origins of replication, but these sequences are not well defined - especially in complex, multi-cellular, eukaryotic species. The length of DNA that spans an origin...

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In eukaryotic cells, DNA replication is highly conserved and tightly regulated. Multiple linear chromosomes must be duplicated with high fidelity before cell division, so there are many proteins that fulfill specialized roles in the replication process. Replication occurs in three phases: initiation, elongation, and termination, and ends with two complete sets of chromosomes in the nucleus.
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Related Experiment Video

Updated: Dec 2, 2025

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by

Leyi Wei¹, Wenjia He², Adeel Malik³

¹computer science from Xiamen University, China.

Briefings in Bioinformatics

|November 5, 2020

Summary

This summary is machine-generated.

Stack-ORI is a new machine learning tool that accurately identifies origins of replication (ORIs) across different species and cell types. It outperforms existing methods by integrating multiple prediction models for better DNA replication insights.

Keywords:

eXtreme Gradient Boosting feature extraction model interpretability origin of replication site stacking strategy

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Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

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Imaging Replicative Domains in Ultrastructurally Preserved Chromatin by Electron Tomography

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Published on: May 20, 2022

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Origins of replication (ORIs) are crucial for DNA replication.
Identifying ORI distribution is key to understanding replication regulation.
Current methods for ORI detection require improvement.

Purpose of the Study:

To develop a novel machine learning approach for accurate ORI identification.
To create cell-specific prediction models for diverse eukaryotic species.
To enhance the understanding of ORI regulation mechanisms.

Main Methods:

Developed Stack-ORI, a machine learning model using 10 cell-specific predictors.
Employed 12 feature encoding schemes for nucleic acid composition and properties.
Utilized eXtreme Gradient Boosting (XGBoost) classifier for baseline and final models.
Integrated baseline model predictions as feature vectors for the final model.

Main Results:

Stack-ORI demonstrated significantly improved performance over baseline models on training and independent datasets.
The approach outperformed existing ORI predictors across all tested cell-specific models.
SHapley Additive exPlanation (SHAP) provided interpretations for feature importance in ORI prediction.

Conclusions:

Stack-ORI offers a robust and accurate method for identifying cell-specific origins of replication.
The novel approach enhances genomic DNA replication studies.
Interpretability of the model aids in understanding key features for ORI prediction.