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Cleavage and Blastulation01:33

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After a large-single-celled zygote is produced via fertilization, the process of cleavage occurs while zygotes travel through the uterine tube. Cleavage is a mitotic cell division that does not result in growth. With each round of successive cell division, daughter cells get increasingly smaller.
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DiCleavePlus: A Transformer-Based Model to Detect Human Dicer Cleavage Sites Within Cleavage Patterns.

Lixuan Mu1, Tatsuya Akutsu1

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.

Genes to Cells : Devoted to Molecular & Cellular Mechanisms
|December 15, 2025
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Summary

DiCleavePlus accurately predicts Dicer cleavage sites on precursor microRNAs (pre-miRNAs). This novel framework improves upon existing methods by utilizing both sequence and structural information for enhanced gene regulation analysis.

Keywords:
attention‐based neural networkdeep learningdicer cleavage site predictionmiRNA

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genetics

Background:

  • MicroRNAs (miRNAs) are key regulators of gene expression at the posttranscriptional level.
  • Mature miRNA biogenesis depends on precise cleavage of precursor miRNAs (pre-miRNAs) by the Dicer enzyme.
  • Current computational methods for predicting Dicer cleavage sites have limitations, including suboptimal performance or reliance on restricted sequence patterns.

Purpose of the Study:

  • To develop a more accurate computational framework for predicting human Dicer cleavage sites on pre-miRNAs.
  • To overcome the limitations of existing Cleavage Pattern-based and non-pattern-based prediction models.
  • To leverage both sequence and structural information of pre-miRNAs for improved prediction accuracy.

Main Methods:

  • Introduction of DiCleavePlus, a novel Cleavage Pattern-based prediction framework.
  • Inputting an extended Cleavage Pattern and the full-length pre-miRNA sequence into the model.
  • Utilizing a Transformer-based encoder to extract relevant features from both the pattern and the pre-miRNA sequence.

Main Results:

  • DiCleavePlus demonstrates accurate and robust performance in predicting human Dicer cleavage sites.
  • The framework effectively integrates sequence and structural features for enhanced prediction.
  • Benchmarking experiments confirm the superior performance of DiCleavePlus compared to existing approaches.

Conclusions:

  • DiCleavePlus represents a significant advancement in predicting Dicer cleavage sites on pre-miRNAs.
  • The model's ability to exploit extended sequence patterns and structural information offers improved accuracy.
  • This tool has the potential to enhance research in miRNA biogenesis and gene regulation analysis.