Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain.

Allen Institute for Brain Science, Seattle, WA 98103, USA.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (Impact Factor: 1.62). 08/2007; 4(3):382-93. DOI: 10.1109/tcbb.2007.1035
Source: DBLP

ABSTRACT Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (

1 Bookmark
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Differential gene expression patterns in cells of the mammalian brain result in the morphological,connectional, and functional diversity of cells. A wide variety of studies have shown that certaingenes are expressed only in specific cell-types. Analysis of cell-type-specific gene expressionpatterns can provide insights into the relationship between genes, connectivity, brain regions, andcell-types. However, automated methods for identifying cell-type-specific genes are lacking to date.
    BMC Bioinformatics 06/2014; 15(1):209. · 3.02 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Anatomical landmarks play an important role in many biomedical image analysis applications (e.g., registration and segmentation). Landmark detection can be computationally very expensive, especially in 3D images, because every single voxel in a region of interest may need to be evaluated. In this paper, we introduce two 3D local image descriptors which can be computed simultaneously for every voxel in a volume. Both our proposed descriptors are extensions of the DAISY descriptor, a popular descriptor that is based on the histograms of oriented gradients and was named after its daisy-flower-like configuration. Our experiments on mouse brain gene expression images indicate that our descriptors are discriminative and are able to reduce the detection errors of landmark points more than 30% when compared with SIFT-3D, an extension in 3D of SIFT (scale-invariant feature transform). We also demonstrate that our descriptors are more computationally efficient than SIFT-3D and n-SIFT (an extension SIFT in n-dimensions) for densely sampled points. Therefore, our descriptors can be used in applications that require computation of the descriptors at densely sampled points (e.g., landmark point detection or feature-based registration).
    Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society 01/2014; · 1.04 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources.
    Frontiers in Neuroinformatics 01/2014; 8:51.

Full-text (2 Sources)

Available from
May 22, 2014