1.1 Binary file abstract.pdf has changed

2.1 --- a/abstract.txt Wed Apr 22 22:24:24 2009 -0700 2.2 +++ b/abstract.txt Thu Apr 23 03:12:01 2009 -0700 2.3 @@ -6,7 +6,7 @@ 2.4 2.5 == Abstract == 2.6 2.7 -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:\\ 2.8 +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:\\ 2.9 2.10 (1) develop an algorithm to screen spatial gene expression data for combinations of marker genes which selectively target anatomical regions\\ 2.11

3.1 Binary file grant.pdf has changed

4.1 --- a/grant.txt Wed Apr 22 22:24:24 2009 -0700 4.2 +++ b/grant.txt Thu Apr 23 03:12:01 2009 -0700 4.3 @@ -48,8 +48,7 @@ 4.4 \newpage 4.5 4.6 == Analysis of high dimensional data for genomic anatomy in the brain == 4.7 - 4.8 -This proposal addresses challenge topic 06-HG-101. Massive new datasets obtained with techniques such as in situ hybridization (ISH), immunohistochemistry, in situ transgenic reporter, microarray voxelation, and others, 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. 4.9 +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, microarray voxelation, and others, 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. 4.10 4.11 \vspace{0.3cm}\hrule 4.12 == The Challenge and Potential impact ==