annotate abstract.txt @ 110:f53042ffdf02
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bshanks@bshanks.dyndns.org |
| date |
Thu Apr 23 03:12:36 2009 -0700 (16 years ago) |
| parents |
a38cc9a46200 |
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| bshanks@108 | 1 \documentclass[11pt]{nih-blank}
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| bshanks@108 | 2
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| bshanks@108 | 3 \usepackage[small,compact]{titlesec}
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| bshanks@108 | 4
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| bshanks@108 | 5 \begin{document}
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| bshanks@108 | 6
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| bshanks@108 | 7 == Abstract ==
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| bshanks@109 | 9 This application addresses broad Challenge Area (06) __Enabling Technologies__ and specific Challenge Topic, 06-HG-101: __New computational and statistical methods for the analysis of large data sets from next-generation sequencing technologies__. Massive new datasets obtained with techniques such as in situ hybridization (ISH), immunohistochemistry, in situ transgenic reporter, allow the expression levels of many genes at many locations to be compared. Our goal is to develop automated methods to relate spatial variation in gene expression to anatomy. We want to find marker genes for specific anatomical regions, and also to draw new anatomical maps based on gene expression patterns. We will validate these methods by applying them to 46 anatomical areas within the cerebral cortex, by using the Allen Mouse Brain Atlas coronal dataset (ABA). This gene expression dataset was generated using ISH, and contains over 4,000 genes. For each gene, a digitized 3-D raster of the expression pattern is available: for each gene, the level of expression at each of 51,533 voxels is recorded. Specifically, we will:\\
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| bshanks@108 | 10
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| bshanks@108 | 11 (1) develop an algorithm to screen spatial gene expression data for combinations of marker genes which selectively target anatomical regions\\
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| bshanks@108 | 12
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| bshanks@108 | 13 (2) develop an algorithm to suggest new ways of carving up a structure into anatomically distinct regions, based on spatial patterns in gene expression\\
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| bshanks@108 | 15 (3) create a 2-D "flat map" dataset of the mouse cerebral cortex that contains a flattened version of the Allen Mouse Brain Atlas ISH data, as well as the boundaries of cortical anatomical areas.\\
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| bshanks@108 | 17 In addition to validating the usefulness of the algorithms, the application of these methods to cortex will produce immediate benefits, because there are currently no known genetic markers for most cortical areas. The results of the project will support the development of new ways to selectively target cortical areas, and it will support the development of a method for identifying the cortical areal boundaries present in small tissue samples.
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| bshanks@108 | 18
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| bshanks@108 | 19 All algorithms that we develop will be implemented in a GPL open-source software toolkit. The toolkit, as well as the machine-readable datasets developed in aim (3), will be published and freely available for others to use.
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| bshanks@108 | 23 \end{document}
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