| Chapter title |
Functional Analysis of Metabolomics Data.
|
|---|---|
| Chapter number | 20 |
| Book title |
Data Mining Techniques for the Life Sciences
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3572-7_20 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
| Authors |
Mónica Chagoyen, Javier López-Ibáñez, Florencio Pazos |
| Editors |
Oliviero Carugo, Frank Eisenhaber |
| Abstract |
Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools. |
Mendeley readers
Geographical breakdown
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|---|---|---|
| Spain | 1 | 6% |
| Brazil | 1 | 6% |
| Unknown | 15 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 29% |
| Student > Master | 4 | 24% |
| Student > Ph. D. Student | 4 | 24% |
| Lecturer | 1 | 6% |
| Student > Doctoral Student | 1 | 6% |
| Other | 2 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
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| Computer Science | 2 | 12% |
| Mathematics | 1 | 6% |
| Medicine and Dentistry | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 2 | 12% |