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Related Experiment Video

Updated: Nov 21, 2025

Analyzing and Building Nucleic Acid Structures with 3DNA

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Published on: April 26, 2013

A deep learning framework combined with word embedding to identify DNA replication origins.

Feng Wu¹, Runtao Yang², Chengjin Zhang¹

¹School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264200, China.

Scientific Reports

|January 13, 2021

Summary

This study introduces a novel deep learning method using Word2vec and convolutional neural networks for accurate identification of DNA replication origins (ORIs). The approach significantly improves ORI identification in eukaryotic genomes, offering a valuable tool for genome analysis.

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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

DNA replication is crucial for genetic inheritance, with Origins of Replication (ORIs) regulating this process.
Accurate identification of ORIs is vital for understanding DNA replication and gene expression regulation.
Existing ORI identification methods for eukaryotes, often based on traditional machine learning, have limitations in handling variable-length sequences and achieving high accuracy.

Purpose of the Study:

To develop an improved method for identifying eukaryotic Origins of Replication (ORIs).
To overcome the limitations of traditional machine learning approaches in ORI identification.
To provide a more accurate and efficient tool for genome analysis.

Main Methods:

Sequence segmentation methods were developed to process gene sequences.
The Word2vec technique was employed to convert gene sequences into numerical word vectors, capturing inter-sequence correlations.
A deep learning framework, combining a convolutional neural network with an embedding layer, was constructed for ORI identification.

Main Results:

The Word2vec technique effectively transformed sequence relationships into numerical features.
The developed predictor achieved high performance across four species, with overall accuracies ranging from 0.765 to 0.975.
The method demonstrated stable performance with high Matthew's correlation coefficients and AUC values, indicating robust ORI and non-ORI classification.

Conclusions:

The proposed deep learning predictor significantly improves the accuracy of eukaryotic Origin of Replication identification compared to state-of-the-art methods.
The method's ability to handle variable-length sequences and its high predictive accuracy make it a valuable tool for high-throughput genome analysis.
This approach offers a reliable and confident method for classifying ORIs and non-ORIs, advancing our understanding of DNA replication mechanisms.