Title |
Visualizing post genomics data-sets on customized pathway maps by ProMeTra – aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example
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Published in |
BMC Systems Biology, August 2009
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DOI | 10.1186/1752-0509-3-82 |
Pubmed ID | |
Authors |
Heiko Neuweger, Marcus Persicke, Stefan P Albaum, Thomas Bekel, Michael Dondrup, Andrea T Hüser, Jörn Winnebald, Jessica Schneider, Jörn Kalinowski, Alexander Goesmann |
Abstract |
The rapid progress of post-genomic analyses, such as transcriptomics, proteomics, and metabolomics has resulted in the generation of large amounts of quantitative data covering and connecting the complete cascade from genotype to phenotype for individual organisms. Various benefits can be achieved when these "Omics" data are integrated, such as the identification of unknown gene functions or the elucidation of regulatory networks of whole organisms. In order to be able to obtain deeper insights in the generated datasets, it is of utmost importance to present the data to the researcher in an intuitive, integrated, and knowledge-based environment. Therefore, various visualization paradigms have been established during the last years. The visualization of "Omics" data using metabolic pathway maps is intuitive and has been applied in various software tools. It has become obvious that the application of web-based and user driven software tools has great potential and benefits from the use of open and standardized formats for the description of pathways. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 6 | 5% |
United Kingdom | 5 | 4% |
Germany | 1 | <1% |
France | 1 | <1% |
Canada | 1 | <1% |
Belgium | 1 | <1% |
Norway | 1 | <1% |
China | 1 | <1% |
Denmark | 1 | <1% |
Other | 2 | 2% |
Unknown | 92 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 34 | 30% |
Student > Ph. D. Student | 23 | 21% |
Student > Master | 14 | 13% |
Professor > Associate Professor | 8 | 7% |
Student > Bachelor | 7 | 6% |
Other | 22 | 20% |
Unknown | 4 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 68 | 61% |
Computer Science | 13 | 12% |
Biochemistry, Genetics and Molecular Biology | 12 | 11% |
Engineering | 3 | 3% |
Arts and Humanities | 2 | 2% |
Other | 8 | 7% |
Unknown | 6 | 5% |