Title |
MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases
|
---|---|
Published in |
BMC Bioinformatics, January 2012
|
DOI | 10.1186/1471-2105-13-6 |
Pubmed ID | |
Authors |
Akhil Kumar, Patrick F Suthers, Costas D Maranas |
Abstract |
Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity). |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Members of the public | 1 | 33% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 4% |
United Kingdom | 4 | 1% |
Germany | 3 | 1% |
Canada | 3 | 1% |
Brazil | 2 | <1% |
Latvia | 2 | <1% |
Russia | 2 | <1% |
Austria | 1 | <1% |
South Africa | 1 | <1% |
Other | 7 | 2% |
Unknown | 249 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 83 | 29% |
Researcher | 63 | 22% |
Student > Master | 36 | 13% |
Student > Doctoral Student | 17 | 6% |
Student > Bachelor | 14 | 5% |
Other | 41 | 14% |
Unknown | 30 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 108 | 38% |
Biochemistry, Genetics and Molecular Biology | 38 | 13% |
Computer Science | 31 | 11% |
Engineering | 29 | 10% |
Chemical Engineering | 10 | 4% |
Other | 27 | 10% |
Unknown | 41 | 14% |