| Budget Amount *help |
¥14,520,000 (Direct Cost: ¥12,600,000、Indirect Cost: ¥1,920,000)
Fiscal Year 2007: ¥8,320,000 (Direct Cost: ¥6,400,000、Indirect Cost: ¥1,920,000)
Fiscal Year 2006: ¥6,200,000 (Direct Cost: ¥6,200,000)
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| Research Abstract |
We have obtained whole-genome (40 million points; 1Gb) expression profiles of 22 tissues, neural stem cells, and cell-lines of the human. We also constructed a visualization tool for the transcriptome database acompanied with annotated genome-wide data, and analyzed characteristics of the transcriptome in the human genome. Because a higher quality set of RNA was required for the tiling array experiments compared to standard microarray ones, we originally developed an RNA purification methodology and a quality control system, with which we conformed that the RNA from all the samples were with high quality. Also, we developed/modified protocols for full-length RNA detection and cloning, and confirmed the expressions by qRT-PCR. For the expression profiling, we developed a new and widely applicable normalization method by optimizing parameters so that they would achieve the least false negative rate and the highest sensitivity on the basis of additional experimental data from gene-express
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ion microarrays. Using these, we newly found that most regions in the human genome were expressed in each tissue and that more than, 90% of transcriptome were from non-coding regions. We also identified dozens of thousands of transcipt products that expressed specifically in each tissue. By analysis with human genome annotation data, we found interesting relationship between the expression patterns, sequence conservation, and variation of polymorphisms in the human genome. These provide new methodologies for focusing regions for functional analyses. This database of genome-wide expression data in the human tissues is unique in the world. Researchers will be able to find disease-related loci including non-coding regions by comparing the expression data in human tissues with those from disease cells, e.g. cancer cells. Particularly, this database is useful for searching tissue specific disease-related molecules and also for prioritizing candidate molecules for therapies by looking at expression in normal tissues to avoid side effects. Less
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