Title |
ExpressionData - A public resource of high quality curated datasets representing gene expression across anatomy, development and experimental conditions
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Published in |
BioData Mining, August 2014
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DOI | 10.1186/1756-0381-7-18 |
Pubmed ID | |
Authors |
Philip Zimmermann, Stefan Bleuler, Oliver Laule, Florian Martin, Nikolai V Ivanov, Prisca Campanoni, Karen Oishi, Nicolas Lugon-Moulin, Markus Wyss, Tomas Hruz, Wilhelm Gruissem |
Abstract |
Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, several reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions. Here, we describe a new type of standardized datasets representative for the spatial and temporal dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments. Expression data is aggregated by anatomical part or stage of development to yield a representative transcriptome for each category. For example, we created a genome-wide expression dataset representing the FDA tissue panel across 35 tissue types. The proposed datasets were created for human and several model organisms and are publicly available at http://www.expressiondata.org. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 47% |
Germany | 2 | 11% |
Australia | 2 | 11% |
Netherlands | 1 | 5% |
Canada | 1 | 5% |
Unknown | 4 | 21% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 12 | 63% |
Members of the public | 7 | 37% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Germany | 1 | 3% |
Unknown | 32 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 12 | 35% |
Student > Ph. D. Student | 5 | 15% |
Student > Bachelor | 3 | 9% |
Student > Doctoral Student | 2 | 6% |
Professor | 2 | 6% |
Other | 8 | 24% |
Unknown | 2 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 18 | 53% |
Biochemistry, Genetics and Molecular Biology | 5 | 15% |
Computer Science | 5 | 15% |
Chemistry | 1 | 3% |
Engineering | 1 | 3% |
Other | 0 | 0% |
Unknown | 4 | 12% |