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Related Experiment Video

Updated: Oct 25, 2025

Cell-Specific Paired Interrogation of the Mouse Ovarian Epigenome and Transcriptome
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Published on: February 24, 2023

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A time-resolved transcriptome landscape of the developing mouse ovary.

Xiaodan Zhang1, Guoping Liu2, Ning Zhang1

  • 1Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China.

Biochemical and Biophysical Research Communications
|August 3, 2021
PubMed
Summary
This summary is machine-generated.

This study provides a comprehensive transcriptional profile of mouse ovarian development, identifying gene networks crucial for understanding this complex process and offering insights into gene functions.

Keywords:
Gene interaction networkMachine learningOvarian developmentTranscriptome

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Area of Science:

  • Reproductive Biology
  • Developmental Biology
  • Genomics

Background:

  • Ovarian structure and function rely on normal development.
  • Systematic molecular analyses of ovarian development are scarce.
  • Previous research focused on specific gene functions, lacking a holistic view.

Purpose of the Study:

  • To create a comprehensive transcriptional profile of mouse ovarian development across multiple stages.
  • To identify gene modules and networks involved in ovarian development dynamics.
  • To provide functional predictions for genes with unknown roles in the ovary.

Main Methods:

  • Collected transcriptional profiles from mouse ovaries at 11 distinct time points.
  • Utilized coexpression analysis to identify gene modules with coordinated expression patterns.
  • Employed machine learning to construct 159 functional gene interaction networks.

Main Results:

  • Generated a detailed molecular atlas of mouse ovarian development.
  • Identified numerous gene modules and 159 gene interaction networks.
  • Confirmed that most networks are linked to essential ovarian developmental processes.
  • Provided functional predictions for previously uncharacterized genes.

Conclusions:

  • The study presents a valuable resource for understanding ovarian development.
  • Identified gene networks offer insights into the molecular mechanisms governing ovarian organogenesis.
  • The findings serve as a foundation for future functional research in reproductive biology.