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Related Experiment Video

Updated: Jan 23, 2026

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
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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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sefOri: selecting the best-engineered sequence features to predict DNA replication origins.

Chenwei Lou1, Jian Zhao1, Ruoyao Shi2

  • 1BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.

Bioinformatics (Oxford, England)
|June 21, 2019
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Summary
This summary is machine-generated.

This study introduces a refined feature selection method to accurately identify DNA replication origins. This approach significantly improves prediction accuracy in yeast genomes, aiding in understanding cell division regulation.

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Area of Science:

  • Genomics and Molecular Biology
  • Computational Biology
  • Cell Biology

Background:

  • Cell division relies on precise spatial and temporal regulation of DNA replication.
  • DNA replication initiates at specific DNA replication origins.
  • Existing models predict origins based on sequence-level physicochemical properties.

Purpose of the Study:

  • To develop and validate a feature selection procedure for refining DNA replication origin classification models.
  • To enhance the accuracy of predicting DNA replication origins in yeast genomes.

Main Methods:

  • Implementation of a novel feature selection procedure.
  • Application of the refined model to yeast genomes, including Saccharomyces cerevisiae.
  • Evaluation of prediction accuracy improvements.

Main Results:

  • Achieved up to a 26% improvement in prediction accuracy for DNA replication origins in yeast.
  • Demonstrated enhanced prediction accuracies across four investigated yeast genomes.
  • Developed and provided access to the sefOri software (version 1.0) and an online server.

Conclusions:

  • The proposed feature selection method significantly improves the accuracy of DNA replication origin prediction.
  • This refined approach offers a valuable tool for studying genome replication regulation.
  • The availability of the sefOri software and online server facilitates broader research applications.