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Related Experiment Video

Updated: Jul 19, 2026

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform
09:04

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

Published on: January 30, 2017

A gene coexpression network for bovine skeletal muscle inferred from microarray data.

Antonio Reverter1, Nicholas J Hudson, Yonghong Wang

  • 1Bioinformatics Group, Commonwealth Scientific and Industrial Research Organisation Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067, Australia. Tony.Reverter-Gomez@csiro.au

Physiological Genomics
|September 21, 2006
PubMed
Summary

This study analyzes beef cattle gene expression to build a muscle gene network, revealing correlations in muscle structural proteins and identifying associations between genes and transcription factors.

Related Experiment Videos

Last Updated: Jul 19, 2026

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform
09:04

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

Published on: January 30, 2017

Area of Science:

  • Animal Genomics
  • Molecular Biology
  • Systems Biology

Background:

  • Understanding gene expression in beef cattle is crucial for improving muscle and fat development.
  • Previous studies have analyzed gene expression but lacked a comprehensive network approach for muscle and fat tissues.

Purpose of the Study:

  • To apply large-scale multivariate mixed-model equations for joint analysis of gene expression in beef cattle muscle and fat tissues.
  • To construct and analyze a gene network to identify biological modules and associations between genes.

Main Methods:

  • Utilized multivariate mixed-model equations for joint analysis of nine gene expression experiments (147 hybridizations, 47 conditions).
  • Employed a correlation-based method to construct a gene network for 822 genes.
  • Performed detailed network analysis to identify protein groupings based on physical association.

Main Results:

  • Identified distinct modules related to muscle structural proteins, enzymes, extracellular matrix, fat metabolism, and protein synthesis.
  • Found significant correlations in the expression of z-disk protein components (MYOZ1, TCAP, PDLIM3).
  • Observed correlations in fast-twitch muscle proteins and enzymes, but not in slow-twitch specific proteins; identified gene-transcription factor associations.

Conclusions:

  • The gene network analysis successfully recapitulates known muscle biology.
  • Revealed underlying associations within and between skeletal muscle structural components.
  • Provides a foundation for further research into beef cattle muscle development and function.