Title |
MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity
|
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Published in |
BMC Bioinformatics, May 2012
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DOI | 10.1186/1471-2105-13-99 |
Pubmed ID | |
Authors |
Dinesh K Barupal, Pradeep K Haldiya, Gert Wohlgemuth, Tobias Kind, Shanker L Kothari, Kent E Pinkerton, Oliver Fiehn |
Abstract |
Exposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 25% |
Spain | 2 | 17% |
Korea, Republic of | 1 | 8% |
Netherlands | 1 | 8% |
Australia | 1 | 8% |
Colombia | 1 | 8% |
Unknown | 3 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 50% |
Scientists | 6 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 2% |
Netherlands | 2 | <1% |
Brazil | 2 | <1% |
Switzerland | 1 | <1% |
Panama | 1 | <1% |
Germany | 1 | <1% |
Portugal | 1 | <1% |
South Africa | 1 | <1% |
United Kingdom | 1 | <1% |
Other | 6 | 2% |
Unknown | 249 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 70 | 26% |
Researcher | 60 | 22% |
Student > Master | 28 | 10% |
Student > Doctoral Student | 13 | 5% |
Student > Bachelor | 13 | 5% |
Other | 50 | 19% |
Unknown | 36 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 89 | 33% |
Biochemistry, Genetics and Molecular Biology | 34 | 13% |
Chemistry | 25 | 9% |
Computer Science | 20 | 7% |
Medicine and Dentistry | 17 | 6% |
Other | 38 | 14% |
Unknown | 47 | 17% |