| Chapter title |
Plant Metabolomics: From Experimental Design to Knowledge Extraction
|
|---|---|
| Chapter number | 19 |
| Book title |
Legume Genomics
|
| Published in |
Methods in molecular biology, January 2013
|
| DOI | 10.1007/978-1-62703-613-9_19 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-612-2, 978-1-62703-613-9
|
| Authors |
Amit Rai, Shivshankar Umashankar, Sanjay Swarup, Rai, Amit, Umashankar, Shivshankar, Swarup, Sanjay |
| Abstract |
Metabolomics is one of the most recent additions to the functional genomics approaches. It involves the use of analytical chemistry techniques to provide high-density data of metabolic profiles. Data is then analyzed using advanced statistics and databases to extract biological information, thus providing the metabolic phenotype of an organism. Large variety of metabolites produced by plants through the complex metabolic networks and their dynamic changes in response to various perturbations can be studied using metabolomics. Here, we describe the basic features of plant metabolic diversity and analytical methods to describe this diversity, which includes experimental workflows starting from experimental design, sample preparation, hardware and software choices, combined with knowledge extraction methods. Finally, we describe a scenario for using these workflows to identify differential metabolites and their pathways from complex biological samples. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Romania | 1 | 3% |
| Unknown | 28 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 27% |
| Student > Master | 5 | 17% |
| Researcher | 5 | 17% |
| Professor > Associate Professor | 4 | 13% |
| Unspecified | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 6 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 33% |
| Chemistry | 4 | 13% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Environmental Science | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 8 | 27% |