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A digital atlas to characterize the mouse brain transcriptome.

James P Carson1, Tao Ju, Hui-Chen Lu

  • 1Program in Structural and Computational Biology and Molecular Biophysics, National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, Texas, United States of America. james.carson@bcm.edu

Plos Computational Biology
|September 27, 2005
PubMed
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This study introduces a computational method for annotating gene expression data within a digital brain atlas. This approach aids in querying and comparing complex biological datasets, advancing neuroscience research.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Genomics

Background:

  • Generating massive gene expression data at cellular resolution for the brain presents challenges in data integration and analysis.
  • A common frame of reference is crucial for effectively utilizing large-scale brain gene expression datasets.

Purpose of the Study:

  • To develop a computational method for annotating gene expression patterns within a digital brain atlas.
  • To facilitate custom user queries and comparisons of gene expression data.
  • To demonstrate the utility of the method in identifying disease-related genes and analyzing genetic mutations.

Main Methods:

  • Developed a semi-automated computational method for annotating gene expression patterns.
  • Integrated gene expression data with a digital atlas of the postnatal mouse brain.

Related Experiment Videos

  • Applied the method to 200 genes, including analysis of dopamine transporter expression and the Rorb gene in the barrelless mutant.

    • Main Results:

    • Successfully annotated gene expression patterns in the postnatal mouse brain.
    • Identified candidate genes potentially related to Parkinson disease using substantia nigra dopamine transporter expression as a query.
    • Quantitatively compared expression patterns to discover down-regulation of transcription factor Rorb in the barrelless mutant.

    Conclusions:

    • The developed computational annotation method provides a valuable tool for analyzing and querying large-scale gene expression data.
    • This approach is applicable to diverse complex tissues and data types, advancing biological research and discovery.
    • Facilitates comparative analyses for identifying disease markers and understanding genetic variations.