Title |
Meta-All: a system for managing metabolic pathway information
|
---|---|
Published in |
BMC Bioinformatics, October 2006
|
DOI | 10.1186/1471-2105-7-465 |
Pubmed ID | |
Authors |
Stephan Weise, Ivo Grosse, Christian Klukas, Dirk Koschützki, Uwe Scholz, Falk Schreiber, Björn H Junker |
Abstract |
Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed. |
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Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Colombia | 1 | 3% |
Netherlands | 1 | 3% |
France | 1 | 3% |
Canada | 1 | 3% |
Russia | 1 | 3% |
United States | 1 | 3% |
Unknown | 24 | 80% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 30% |
Student > Ph. D. Student | 7 | 23% |
Student > Bachelor | 3 | 10% |
Professor > Associate Professor | 3 | 10% |
Student > Master | 2 | 7% |
Other | 3 | 10% |
Unknown | 3 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 18 | 60% |
Computer Science | 4 | 13% |
Biochemistry, Genetics and Molecular Biology | 2 | 7% |
Chemistry | 2 | 7% |
Medicine and Dentistry | 1 | 3% |
Other | 1 | 3% |
Unknown | 2 | 7% |